Construction control method and system for frost heave and thaw settlement characteristics of frozen soil layer

Abstract

The present invention provides a construction control method and system for the frost heave and thaw settlement characteristics of a frozen soil layer, and relates to a technical field, including obtaining a plurality of engineering geological characteristic parameters at a construction site; establishing a multi-field coupled three-dimensional numerical analysis model that simulates heat conduction, water migration, and stress deformation of a frozen soil layer, and using comparative data to calibrate and verify the established multi-field coupled three-dimensional numerical analysis model; using the calibrated and verified multi-field coupled three-dimensional numerical analysis model, and determining a target threshold range for frost heave deformation control and a target threshold range for thaw settlement deformation control of a frozen soil layer through a multi-objective optimization algorithm; a data processing and analysis system performs real-time analysis and fusion on monitoring data uploaded by a wireless sensor network, and outputs corresponding construction control decision instructions.

Description

Technical Field

[0001] The invention relates to civil engineering technology, and in particular to a construction control method and system for the frost heave, thaw and sink characteristics of a frozen soil layer. Background Art

[0002] Frozen soil will produce frost heave during the freezing process, which is manifested as volume expansion and increased strength, and will produce thaw settlement during the melting process, which is manifested as volume contraction and reduced strength. When the frost heave deformation exceeds a certain limit, it will cause problems such as surface uplift, pipeline arching, and building cracking; when the thaw settlement deformation exceeds a certain limit, it will cause surface settlement, cavern deformation, lining cracking and other problems. At present, there are the following technical defects and unresolved problems in artificial freezing construction:

[0003] There is a lack of real-time and continuous monitoring methods for frost heave, thaw and settlement. Traditional methods mostly use manual inspections and regular testing to obtain frost heave, thaw and settlement information. The monitoring frequency is low and the timeliness is poor. It is difficult to fully grasp the dynamic change process of frozen soil deformation, which easily leads to monitoring blind spots and control lags.

[0004] There is a lack of fusion analysis methods for multi-source heterogeneous monitoring data. The construction process involves multiple physical quantities such as temperature, displacement, and stress. The monitoring data collected by different types of sensors are heterogeneous in terms of temporal and spatial scales and data formats. How to effectively fuse multi-source monitoring data to form consistent frost heave and thaw settlement evaluation indicators is an urgent problem to be solved.

[0005] There is a lack of quantitative prediction models for frost heave and thaw settlement deformation. The factors that affect frost heave and thaw settlement deformation are complex and changeable. Existing studies are mostly based on empirical formulas or finite element numerical simulations. There is a lack of machine learning prediction models based on measured data, making it difficult to accurately predict and analyze the trend of frost heave and thaw settlement deformation. Summary of the invention

[0006] The embodiments of the present invention provide a construction control method and system for the frost heave and thaw settlement characteristics of a frozen soil layer, which can solve the problems in the prior art.

[0007] A first aspect of an embodiment of the present invention provides a construction control method for the frost heave and thaw settlement characteristics of a frozen soil layer, comprising:

[0008] Obtain multiple engineering geological characteristic parameters of the construction site; based on the obtained multiple engineering geological characteristic parameters, establish a multi-field coupled three-dimensional numerical analysis model that simulates the heat conduction, water migration, and stress deformation of the frozen soil layer; obtain multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer by conducting indoor frost heave and thaw settlement characteristic tests and on-site monitoring tests; use the comparative data to calibrate and verify the established multi-field coupled three-dimensional numerical analysis model;

[0009] Using the verified multi-field coupling three-dimensional numerical analysis model, the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process is simulated and analyzed, and the time history curves and spatial distribution cloud maps of the frozen soil layer at different construction stages and at different depths are extracted; on the basis of the simulation analysis, the requirements of the construction schedule, environmental conditions, and structural safety are comprehensively considered, and the target threshold ranges of the frozen soil layer frost heave deformation control and the target threshold ranges of the thaw deformation control are determined through a multi-objective optimization algorithm; a wireless sensor network is deployed in the frozen soil layer to monitor the temporal and spatial distribution data of the temperature field, moisture field, deformation field, and stress field of the frozen soil layer in real time, and the real-time monitoring data is uploaded to the data processing and analysis system;

[0010] The data processing and analysis system performs real-time analysis and fusion of the monitoring data uploaded by the wireless sensor network, extracts data parameters, and compares them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the corresponding construction control decision instructions are output.

[0011] In an optional embodiment, based on the obtained multiple engineering geological characteristic parameters, a multi-field coupled three-dimensional numerical analysis model simulating heat conduction, water migration, and stress deformation of the frozen soil layer is established, and multiple groups of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer are obtained by carrying out indoor frost heave and thaw settlement characteristic tests and field monitoring tests. The steps of using the comparative data to verify the established multi-field coupled three-dimensional numerical analysis model include:

[0012] Obtain the engineering geological parameters of the frozen soil layer, obtain complete soil layer information through core drilling, use geophysical testing technology to obtain continuous spatial distribution information of the frozen soil layer, carry out in-situ testing of the frozen soil layer, and directly obtain parameters reflecting its engineering characteristics, including thermal conductivity, thermal diffusivity, water content, density and permeability; among them, geophysical testing technologies include high-density resistivity method and transient electromagnetic method. The high-density resistivity method applies current through the probe and measures the potential difference to obtain the apparent resistivity profile, and the transient electromagnetic method excites a pulse magnetic field through the coil and measures the secondary field electromotive force to obtain the apparent conductivity profile. The combination of the two methods can accurately invert the spatial distribution of the frozen soil layer; in-situ testing includes transient thermal probe method to determine thermal conductivity and thermal diffusivity, neutron and gamma ray method to determine water content and density, and water seepage test to determine permeability;

[0013] Based on the acquired engineering geological parameters, a multi-field coupled three-dimensional numerical analysis model reflecting the complex coupling mechanism of frozen soil layers is established. Based on the continuous medium mechanics and porous medium theory, the thermal-hydraulic-mechanical coupling control equation is established. The constitutive model is selected, and the volume fraction average model is used to establish the functional relationship between the specific heat capacity and thermal conductivity and the temperature and volume ice content. The power function attenuation model is used to characterize the relationship between the permeability coefficient and the pore ice saturation. The linear expansion coefficient is estimated by the volume fraction weighted average formula. The hyperbolic model is used to describe the change of elastic modulus with temperature and ice content. The thermal-hydraulic-mechanical coupling control equation is solved by the finite element method. The fully implicit solution format is adopted, and the coupled solution of multiple field physical quantities in each time step is realized by the iterative algorithm.

[0014] Indoor frost heave and thaw settlement characteristic tests and field monitoring tests were carried out to obtain multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer. The indoor frost heave and thaw settlement characteristic test was carried out using a high and low temperature environmental chamber and a frost heave deformation tester to analyze the frost heave deformation curves of frozen soil samples under different temperature gradients and water content conditions. The test temperature gradient was achieved through the refrigeration and heating system of the environmental chamber, the temperature was measured using a platinum resistance temperature sensor, the sample water content was determined by the drying method, the axial deformation was measured using a displacement sensor, and the radial deformation was measured using a circumferential strain gauge. The field monitoring test was carried out by deploying an automated monitoring system consisting of temperature, water content, displacement, and soil pressure sensors to collect real-time thermal-hydraulic-mechanical response data of the frozen soil layer under different construction conditions.

[0015] The constitutive parameters of the established multi-field coupled three-dimensional numerical analysis model are calibrated using the indoor frost heave and thaw settlement characteristics test data, so that the multi-field coupled three-dimensional numerical analysis model can accurately reflect the thermal-hydraulic-mechanical characteristics of the frozen soil layer; on the basis of the parameter calibration, the field monitoring profile is selected, and the corresponding numerical calculation model is established to simulate and analyze the temporal and spatial distribution of the temperature field, moisture field, and displacement field of the field monitoring profile under actual working conditions, and compared and verified with the monitoring data; by comparing and analyzing the degree of agreement between the numerical simulation results and the monitoring data, the simulation accuracy and applicability of the established multi-field coupled three-dimensional numerical analysis model are quantitatively evaluated; the accuracy evaluation indicators of the multi-field coupled three-dimensional numerical analysis model include the average relative error, the root mean square error, and the goodness of fit coefficient, and the applicability evaluation indicators include the computational efficiency and convergence; the multi-field coupled three-dimensional numerical analysis model is adjusted and optimized according to the evaluation results.

[0016] In an optional embodiment, based on the acquired engineering geological parameters, a multi-field coupled three-dimensional numerical analysis model reflecting the complex coupling mechanism of the frozen soil layer is established, based on the continuous medium mechanics and porous medium theory, a thermal-hydraulic-mechanical coupling control equation is established, a constitutive model is selected, and a volume fraction average model is used to establish a functional relationship between specific heat capacity and thermal conductivity and temperature and volume ice content, a power function attenuation model is used to characterize the relationship between permeability and pore ice saturation, a volume fraction weighted average formula is used to estimate the linear expansion coefficient, a hyperbolic model is used to describe the change of elastic modulus with temperature and ice content, the thermal-hydraulic-mechanical coupling control equation is solved by a finite element method, a fully implicit solution format is used, and the steps of realizing the coupled solution of multiple field physical quantities in each time step by an iterative algorithm include:

[0017] Based on the continuous medium mechanics and porous medium theory, combined with the law of conservation of energy, mass and momentum, the heat conduction equation, water transport equation and stress balance equation in the frozen soil layer are established respectively. The heat conduction equation takes into account the influence of heat conduction and phase change latent heat release, the water transport equation uses the linear seepage law to describe the pore water seepage, and the stress balance equation introduces temperature strain and frost heave strain. The three equations are coupled to form the control equation group of the thermal-hydraulic-mechanical coupling problem of the frozen soil layer.

[0018] The established thermal-hydraulic-mechanical coupling control equations are numerically solved. First, the partial differential equations are discretized into finite element equations using the weighted residual method. Then, the time derivative terms are discretized using two-point back-difference, and the time integral parameters are introduced to form a fully implicit iterative format. Finally, an iterative algorithm is used to achieve the coupled solution of the temperature field, pore water pressure field, and displacement field in each time step. The stiffness matrix and load vector are updated through known physical quantities, and the unknown physical quantities are iteratively calculated until the convergence criteria of each physical quantity are met at the same time.

[0019] After the iteration converges, the temperature, pore water pressure and displacement variables of the current time step are updated and saved, and the next time step is entered, and the coupled iterative solution process is repeated until the transient multi-field simulation within the preset time period is completed, and the spatiotemporal evolution law of the thermal-hydraulic-mechanical characteristic parameters of the frozen soil layer is obtained.

[0020] In an optional embodiment, the steps of using a verified multi-field coupled three-dimensional numerical analysis model to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and extracting the frost heave, thaw and sink deformation time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and different depths include:

[0021] According to the freezing pipe arrangement scheme and freezing temperature curve actually adopted in the project, the corresponding cold source load is set in the multi-field coupled three-dimensional numerical analysis model. The freezing pipe adopts a combination of two-stage freezing and intermittent freezing. By performing transient heat transfer analysis, the three-dimensional temperature field distribution of the frozen soil layer at different freezing times is obtained, and the temperature cloud map of typical sections and typical time points is extracted. The circumferential and radial distribution characteristics of the soil temperature during the freezing process are analyzed. By comparing the temperature field evolution process at different freezing times, the key time nodes in the frozen soil development process are extracted, and the spatial position map of the zero-degree isotherm at different times is drawn to study the expansion and contraction process of the zero-degree isotherm over time.

[0022] Based on the results of the evolution of the temperature field during the simulated freezing construction process, the evolution of the physical and mechanical parameters of frozen soil caused by temperature changes was considered, and a thermal-mechanical coupling analysis was performed. The thermal expansion theory was used to describe the frost heave deformation, and the freeze-thaw cycle damage model was used to describe the thaw settlement deformation. The temporal and spatial distribution law of the soil displacement field during the freezing process was obtained. The deformation characteristics of the three typical stages, namely the initial freezing period, the slow freezing period, and after the foundation pit excavation, were paid attention to. The surface uplift distribution curves at different times during the freezing process were extracted, and the uplift contour map was drawn. The spatial distribution characteristics of the surface frost heave deformation were analyzed. Combined with the uplift time-history curve, the uplift rate and uplift amount were quantitatively evaluated over time. The thaw settlement and deformation characteristics caused by foundation pit excavation and cavern excavation were analyzed, and the surface settlement contour map after excavation was drawn. The difference in surface deformation before and after excavation was compared. Combined with the time-history curves of the cavern vault settlement and the side wall inward displacement, the time development law of soil deformation below the excavation surface was studied. The deformation recovery process from the end of freezing to the complete melting of the soil was simulated, the thaw settlement development rate and duration were analyzed, and the surface settlement and cavern deformation contour maps at the end of thaw settlement were drawn.

[0023] An orthogonal numerical test scheme was designed, and multiple freezing temperatures, freezing times, freezing pipe spacings and excavation depths of foundation pits were selected for numerical simulation calculations. The deformation cloud maps and time curves of frost heave and thaw settlement under different freezing temperature conditions were compared, and the influence of freezing temperature on deformation was quantitatively analyzed. The surface uplift and cavern deformation under different freezing time conditions were compared, and the influence of freezing time on frost heave and thaw settlement deformation was quantitatively analyzed. The frost heave deformation rate and maximum deformation under different freezing pipe spacing conditions were compared, and the influence of freezing pipe spacing on frost heave deformation was quantitatively analyzed. The duration and settlement of thaw settlement under different excavation depths were compared, and the influence of excavation depth on thaw settlement deformation was quantitatively analyzed. Based on the numerical simulation results, a quantitative relationship between construction process parameters and frost heave and thaw settlement deformation indicators was constructed.

[0024] In an optional embodiment, based on simulation analysis, comprehensively considering the construction progress requirements, environmental condition restrictions, and structural safety requirements, the steps of determining the target threshold range of frozen soil layer frost heave deformation control and the target threshold range of thaw settlement deformation control through a multi-objective optimization algorithm include:

[0025] The maximum ground uplift, maximum cavern deformation, total freezing period and total cost are determined as optimization targets, and the functional relationship between them and freezing temperature, freezing time, freezing pipe spacing and excavation depth is established. The maximum ground uplift and maximum cavern deformation are obtained by fitting the numerical simulation results, the total freezing period is calculated according to the freezing construction curve, and the total cost takes into account material costs, energy costs and labor costs.

[0026] According to engineering experience and previous numerical simulation results, the value ranges of the four optimization variables, namely, freezing temperature, freezing time, freezing pipe spacing and excavation depth, are set;

[0027] An improved non-dominated sorting genetic algorithm is used, with the optimization objective function as the fitness function and the value range of the optimization variable as the constraint condition. By setting a reasonable population size, crossover probability and mutation probability, a set of Pareto optimal solutions is obtained after multiple iterations. Each solution in the solution set is a balance between the optimization objectives that cannot be further improved, reflecting the trade-off between frost heave, thaw settlement deformation and total cost under different construction schemes.

[0028] On the basis of the Pareto optimal solution set, according to the importance of deformation control and the tolerance of total cost in actual engineering, one or several control thresholds that meet the requirements are selected as recommended solutions;

[0029] By using the engineering analogy method, the deformation control effects of the recommended control threshold scheme and existing similar projects were compared to evaluate the rationality of the threshold. The whole freezing construction process simulation was re-conducted under the guidance of the recommended control threshold scheme by using the numerical simulation method.

[0030] According to the recommended control threshold scheme, three deformation warning levels are divided into mild, moderate and severe levels, and corresponding construction parameter adjustment measures, unloading measures and reinforcement measures are formulated. In the actual construction process, the early warning and emergency control mechanism are promptly activated through real-time comparison of deformation monitoring data with the recommended control threshold scheme.

[0031] In an optional embodiment, an improved non-dominated sorting genetic algorithm is used, the optimization objective function is used as the fitness function, the value range of the optimization variable is used as the constraint condition, and a reasonable population size, crossover probability and mutation probability are set. After multiple iterations, the steps of obtaining a set of Pareto optimal solutions include:

[0032] The freezing temperature, freezing time, freezing pipe spacing and excavation depth are taken as optimization variables, the maximum surface uplift, the maximum cavern deformation, the total freezing period and the total cost are taken as optimization targets, and the initial population of size N is randomly generated by real number coding. Each individual in the population corresponds to a set of optimization variable value combinations, representing a freezing construction plan.

[0033] Perform fast non-dominated sorting on the current population, and divide it into different non-dominated levels according to the Pareto dominance relationship between individuals. Individuals in the same level do not dominate each other. In each non-dominated level, calculate the crowding distance of individuals on each target, and obtain the total crowding degree of individuals to maintain population diversity.

[0034] Parent individuals are selected from the current population through binary tournament selection, and simulated binary crossover and polynomial mutation operations are used to generate offspring with the same size as the parent. Crossover and mutation are designed to search and perturb optimization variables.

[0035] Re-execute fast non-dominated sorting and crowding calculation on the merged parent and child generations, and select N optimal individuals from them based on non-dominated relations and crowding to generate a new generation of population; when the number of iterations reaches the preset maximum number of generations, the optimization is terminated, and the non-dominated solution set of the final population is output as the Pareto optimal solution set. Each solution in the solution set corresponds to a frozen construction plan that takes into account both deformation control and cost saving;

[0036] Engineering test data and finite element analysis are used to establish a quantitative relationship between optimization objectives and optimization variables, and a fitness evaluation model is constructed; the optimal solution set can be displayed in real time in the decision support system, and the optimization plan can be recommended to guide the freezing construction, and the freezing parameters can be dynamically adjusted based on monitoring data to achieve real-time control of deformation.

[0037] In an optional embodiment, the data processing and analysis system performs real-time analysis and fusion of the monitoring data uploaded by the wireless sensor network, extracts data parameters, and compares them with preset frost heave deformation control target thresholds and thaw settlement deformation control target thresholds in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the steps of outputting corresponding construction control decision instructions include:

[0038] Define unified data analysis rules for deployed wireless sensors, which include key fields of wireless sensor data collection and corresponding data processing algorithms. Use data analysis rules to analyze the original monitoring data uploaded by wireless sensors, extract key fields and form structured monitoring data.

[0039] Multiple groups of structured monitoring data collected by similar wireless sensors are summarized, and a weighted average fusion algorithm is used to perform fusion calculation on each monitoring data. In the weighted average fusion algorithm, the weight of the wireless sensor is determined according to its measurement performance, and the sum of the weights of similar wireless sensors participating in the fusion calculation is 1; the fused monitoring data is written into the time series data table of the monitoring database together with the corresponding timestamp at the set time interval.

[0040] A second aspect of the embodiment of the present invention provides a construction control system for the frost heave and thaw settlement characteristics of frozen soil layers, including:

[0041] The first unit is used to obtain multiple engineering geological characteristic parameters of the construction site; based on the obtained multiple engineering geological characteristic parameters, a multi-field coupled three-dimensional numerical analysis model simulating the heat conduction, water migration, and stress deformation of the frozen soil layer is established. By conducting indoor frost heave and thaw settlement characteristic tests and field monitoring tests, multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer are obtained, and the comparative data are used to verify the established multi-field coupled three-dimensional numerical analysis model;

[0042] The second unit is used to use the verified multi-field coupling three-dimensional numerical analysis model to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and to extract the time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and at different depths; on the basis of simulation analysis, comprehensively consider the construction progress requirements, environmental conditions, and structural safety requirements, and determine the target threshold range of the frozen soil layer frost heave deformation control and the target threshold range of the thaw deformation control through a multi-objective optimization algorithm; deploy a wireless sensor network in the frozen soil layer to monitor the temporal and spatial distribution data of the temperature field, moisture field, deformation field, and stress field of the frozen soil layer in real time, and upload the real-time monitoring data to the data processing and analysis system;

[0043] The third unit is used for data processing and analysis system to perform real-time analysis and fusion of monitoring data uploaded by the wireless sensor network, extract data parameters, and compare them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the corresponding construction control decision instructions are output.

[0044] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0045] processor;

[0046] a memory for storing processor-executable instructions;

[0047] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0048] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0049] The multi-field coupled three-dimensional numerical analysis model established in this paper can accurately simulate the heat conduction, water migration and stress-deformation coupling process in artificial freezing engineering. The selected constitutive relations such as seepage retardation model and frost heave model can reasonably reflect the thermal-hydraulic-mechanical characteristics of the frozen soil layer, which is in good agreement with the results of indoor tests and field monitoring. In engineering practice, this model can be used to predict and analyze the spatiotemporal evolution of the frozen soil layer under different construction conditions, providing a quantitative basis for the optimal design of the freezing scheme and the control of the construction process.

[0050] This paper uses wireless sensor networks to monitor the artificial freezing construction process. Through data parsing, fusion, analysis and early warning, real-time monitoring and control of frost heave deformation and thaw settlement deformation can be achieved. The system automatically identifies abnormal conditions according to preset thresholds, calls the reverse optimization analysis model to give construction control decisions, and executes unattended through the automated control system, thereby effectively guiding the artificial freezing construction. At the same time, through visual display and human-computer interaction functions, it provides engineering management personnel with intuitive and convenient monitoring data query and control methods, improving the safety and intelligence level of artificial freezing construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a construction control method for the frost heave and thaw settlement characteristics of a frozen soil layer according to an embodiment of the present invention;

[0052] Figure 2 The structure diagram of the construction control system for the frost heave and thaw settlement characteristics of frozen soil layers according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0055] Figure 1FIG. 1 is a flow chart of a construction control method for the frost heave and thaw settlement characteristics of a frozen soil layer according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0056] S1. Obtain multiple engineering geological characteristic parameters of the construction site; based on the obtained multiple engineering geological characteristic parameters, establish a multi-field coupled three-dimensional numerical analysis model that simulates the heat conduction, water migration, and stress deformation of the frozen soil layer; obtain multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer by conducting indoor frost heave and thaw settlement characteristic tests and field monitoring tests; and use the comparative data to verify the established multi-field coupled three-dimensional numerical analysis model;

[0057] S2. Use the verified multi-field coupling three-dimensional numerical analysis model to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and extract the time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and at different depths; on the basis of the simulation analysis, comprehensively consider the construction progress requirements, environmental conditions, and structural safety requirements, and determine the target threshold ranges for the frozen soil layer's frost heave deformation control and the target threshold ranges for the thaw deformation control through a multi-objective optimization algorithm; deploy a wireless sensor network in the frozen soil layer to monitor the temporal and spatial distribution data of the temperature field, moisture field, deformation field, and stress field of the frozen soil layer in real time, and upload the real-time monitoring data to the data processing and analysis system;

[0058] S3. The data processing and analysis system performs real-time analysis and fusion of the monitoring data uploaded by the wireless sensor network, extracts data parameters, and compares them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the corresponding construction control decision instructions are output.

[0059] In an optional embodiment, based on the obtained multiple engineering geological characteristic parameters, a multi-field coupled three-dimensional numerical analysis model simulating heat conduction, water migration, and stress deformation of the frozen soil layer is established, and multiple groups of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer are obtained by carrying out indoor frost heave and thaw settlement characteristic tests and field monitoring tests. The steps of using the comparative data to verify the established multi-field coupled three-dimensional numerical analysis model include:

[0060] Obtain the engineering geological parameters of the frozen soil layer, obtain complete soil layer information through core drilling, use geophysical testing technology to obtain continuous spatial distribution information of the frozen soil layer, carry out in-situ testing of the frozen soil layer, and directly obtain parameters reflecting its engineering characteristics, including thermal conductivity, thermal diffusivity, water content, density and permeability; among them, geophysical testing technologies include high-density resistivity method and transient electromagnetic method. The high-density resistivity method applies current through the probe and measures the potential difference to obtain the apparent resistivity profile, and the transient electromagnetic method excites a pulse magnetic field through the coil and measures the secondary field electromotive force to obtain the apparent conductivity profile. The combination of the two methods can accurately invert the spatial distribution of the frozen soil layer; in-situ testing includes transient thermal probe method to determine thermal conductivity and thermal diffusivity, neutron and gamma ray method to determine water content and density, and water seepage test to determine permeability;

[0061] Based on the acquired engineering geological parameters, a multi-field coupled three-dimensional numerical analysis model reflecting the complex coupling mechanism of frozen soil layers is established. Based on the continuous medium mechanics and porous medium theory, the thermal-hydraulic-mechanical coupling control equation is established. The constitutive model is selected, and the volume fraction average model is used to establish the functional relationship between the specific heat capacity and thermal conductivity and the temperature and volume ice content. The power function attenuation model is used to characterize the relationship between the permeability coefficient and the pore ice saturation. The linear expansion coefficient is estimated by the volume fraction weighted average formula. The hyperbolic model is used to describe the change of elastic modulus with temperature and ice content. The thermal-hydraulic-mechanical coupling control equation is solved by the finite element method. The fully implicit solution format is adopted, and the coupled solution of multiple field physical quantities in each time step is realized by the iterative algorithm.

[0062] Indoor frost heave and thaw settlement characteristic tests and field monitoring tests were carried out to obtain multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer. The indoor frost heave and thaw settlement characteristic test was carried out using a high and low temperature environmental chamber and a frost heave deformation tester to analyze the frost heave deformation curves of frozen soil samples under different temperature gradients and water content conditions. The test temperature gradient was achieved through the refrigeration and heating system of the environmental chamber, the temperature was measured using a platinum resistance temperature sensor, the sample water content was determined by the drying method, the axial deformation was measured using a displacement sensor, and the radial deformation was measured using a circumferential strain gauge. The field monitoring test was carried out by deploying an automated monitoring system consisting of temperature, water content, displacement, and soil pressure sensors to collect real-time thermal-hydraulic-mechanical response data of the frozen soil layer under different construction conditions.

[0063] The constitutive parameters of the established multi-field coupled three-dimensional numerical analysis model are calibrated using the indoor frost heave and thaw settlement characteristics test data, so that the multi-field coupled three-dimensional numerical analysis model can accurately reflect the thermal-hydraulic-mechanical characteristics of the frozen soil layer; on the basis of the parameter calibration, the field monitoring profile is selected, and the corresponding numerical calculation model is established to simulate and analyze the temporal and spatial distribution of the temperature field, moisture field, and displacement field of the field monitoring profile under actual working conditions, and compared and verified with the monitoring data; by comparing and analyzing the degree of agreement between the numerical simulation results and the monitoring data, the simulation accuracy and applicability of the established multi-field coupled three-dimensional numerical analysis model are quantitatively evaluated; the accuracy evaluation indicators of the multi-field coupled three-dimensional numerical analysis model include the average relative error, the root mean square error, and the goodness of fit coefficient, and the applicability evaluation indicators include the computational efficiency and convergence; the multi-field coupled three-dimensional numerical analysis model is adjusted and optimized according to the evaluation results.

[0064] For example, in order to accurately simulate and predict the spatiotemporal evolution of frozen soil layers, this paper constructs a multi-field coupled three-dimensional numerical analysis model that reflects its complex coupling mechanism based on the engineering geological characteristic parameters of frozen soil layers, and verifies the model through indoor tests and field monitoring data. The specific technical solution is as follows:

[0065] The engineering geological parameters of the frozen soil layer are obtained by means of coring, geophysical testing and in-situ testing. Complete soil layer information, including soil layer properties, thickness, burial depth, etc., can be obtained through coring. Geophysical testing includes high-density resistivity method and transient electromagnetic method. The former obtains the apparent resistivity profile by applying current through the probe and measuring the potential difference, while the latter obtains the apparent conductivity profile by exciting a pulse magnetic field through the coil and measuring the secondary field electromotive force. The combination of the two methods can accurately invert the three-dimensional spatial distribution of the frozen soil layer.

[0066] In-situ testing can directly obtain thermal and hydraulic parameters that reflect the engineering characteristics of frozen soil. The transient thermal probe method is used to measure the thermal conductivity and thermal diffusivity of frozen soil in situ. The principle is to invert the thermal physical parameters of the soil by measuring the temperature change curve of the heating probe over time. For example, in the in-situ test of a frozen soil layer, the heating power is 10W and the heating time is 100s. By analyzing the probe temperature-time curve, the thermal conductivity of the frozen soil is 1.85W / (m·K) and the thermal diffusivity is 0.67mm² / s. The neutron detection method and gamma ray method are used to measure the water content and dry density of the frozen soil layer respectively. The principle is to analyze the energy spectrum change characteristics after the incident neutron or gamma ray interacts with the soil. For example, a frozen soil layer is tested using a neutron surface humidity meter. When the neutron count rate CPM (Count Per Minute) is 536, the volume water content is 28% obtained by the precision curve. The permeability coefficient of the frozen soil layer is determined by the seepage test. The test adopts the variable head method and calculates the permeability coefficient by measuring the rate of change of the seepage flow rate over time under different head differences. For example, a permeability test is conducted on a frozen soil sample. When the initial head difference is 50cm, the stable seepage flow rate is 2.1ml / min. The permeability coefficient calculated by Darcy's law is 4.6×10-6cm / s.

[0067] On the basis of obtaining the engineering geological parameters of frozen soil layer, a numerical analysis model reflecting the thermal-hydraulic-mechanical coupling mechanism of frozen soil layer was established based on the continuum mechanics and porous media theory. The coupling control equations include heat conduction equation, water migration equation and solid deformation equation. The heat conduction equation considers the influence of four-phase components of soil skeleton, liquid water, ice and gas on heat transfer; the water migration equation adopts the improved Richards equation, which considers the driving effect of temperature gradient and suction gradient caused by pore ice on water migration; the solid deformation equation is based on the thermo-elastic-plastic constitutive model of frozen soil, and comprehensively considers temperature strain, frost heave strain and plastic strain.

[0068] On the basis of coupled control equations, a reasonable thermo-hydraulic-mechanical constitutive model of frozen soil layer is selected. The volume fraction average model is used to establish the functional relationship between the specific heat capacity and thermal conductivity of frozen soil layer and temperature and volume ice content. For example, under the conditions of -5℃ and 30% volume ice content, the specific heat capacity of a frozen soil is 2.15kJ / (kg·K) and the thermal conductivity is 2.32W / (m·K). The power function decay model is used to characterize the relationship between the permeability coefficient of frozen soil layer and the pore ice saturation to reflect the influence of freezing retardation effect on seepage. For example, the permeability coefficient of a frozen soil is 3.5×10-4cm / s when the pore ice saturation is 0, and when the pore ice saturation increases to 0.8, the permeability coefficient decays to 4.6×10-7cm / s. The volume fraction weighted average formula is used to estimate the linear expansion coefficient of frozen soil layer, taking into account the volume fractions of soil particles, liquid water, ice and gas. For example, the volume fractions of soil particles, liquid water, ice and gas in a frozen soil are 65%, 15%, 18% and 2% respectively. According to the linear expansion coefficients of each component, the linear expansion coefficient of the frozen soil layer is estimated to be 8.2×10-5 / K. The hyperbolic model is used to describe the change of the elastic modulus of the frozen soil layer with temperature and ice content to reflect the effect of freezing cementation on the stiffness of the soil. For example, under the conditions of -10℃ and 40% volume ice content, the elastic modulus of a frozen soil can reach 560MPa.

[0069] Based on the selected constitutive model, the thermal-hydraulic-mechanical coupling control equations are discretely solved by the finite element method. The computational domain is meshed using hexahedral linear units, and the unit size must take into account both computational accuracy and efficiency. A fully implicit solution format is used, and the coupled solution of multiple field physical quantities in each time step is achieved through an iterative algorithm, that is, in each iteration step, the temperature field is first solved by the heat conduction equation, the pore water pressure field is then solved by the water migration equation, and finally the displacement field is solved by the solid deformation equation. Multiple field physical quantities are coupled through source and sink terms.

[0070] In order to verify the established multi-field coupled three-dimensional numerical analysis model, indoor frost heave and thaw settlement characteristics tests and field monitoring tests were carried out, and multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer were obtained. The indoor frost heave and thaw settlement characteristics test was carried out using a high and low temperature environmental chamber and a frost heave deformation tester to study the frost heave deformation law of frozen soil samples under different temperature gradients and water contents. The experimental temperature gradient was achieved by the refrigeration and heating system of the environmental chamber, and the temperature was measured using a platinum resistance temperature sensor. The moisture content of the sample was determined by the drying method, that is, the moisture content was calculated by measuring the mass change of the sample before and after drying. The axial deformation was measured by a displacement sensor, and the radial deformation was measured by a circumferential strain gauge. For example, under the conditions of a temperature of -10℃ and an initial moisture content of 25%, the axial frost heave deformation of a frozen soil sample was 2.8%, and the radial frost heave deformation was 1.5%.

[0071] The field monitoring test was carried out by laying an automated monitoring system consisting of temperature, water content, displacement, and earth pressure sensors in the tunnel body and on the ground, and real-time collection of thermal-hydraulic-mechanical response data of the frozen soil layer under different construction conditions. The temperature monitoring uses a thermal resistor temperature string, and temperature sensors are laid at different buried depths, with a range of -30~10℃ and an accuracy of ±0.1℃. The water content monitoring uses a time domain reflectometer to obtain the dielectric constant and water content by measuring the propagation time of electromagnetic pulses on the transmission line, with a range of 0~100% and an accuracy of ±2%. Displacement monitoring is divided into surface displacement and tunnel wall displacement monitoring. The surface displacement uses the level method, and the tunnel wall displacement uses a rope displacement meter, with a range of 0-100mm and an accuracy of 0.01mm. The earth pressure monitoring uses a soil pressure box model PW-EA-ST, with a range of 1MPa and an accuracy of 0.1%FS. The field monitoring data is collected to the data processing center through a remote transmission module, and after pre-processing such as outlier removal and data correction, a usable time series data set is formed.

[0072] Using the test data of indoor frost heave and thaw settlement characteristics, some constitutive parameters in the multi-field coupled three-dimensional numerical analysis model were calibrated, such as the relationship between the thermal conductivity, specific heat capacity, and volume ice content of frozen soil and temperature, so that the numerical model can accurately reflect the thermal characteristics of the frozen soil layer. Based on the parameter calibration, a typical field monitoring section was selected and a corresponding finite element numerical calculation model was established to simulate and analyze the temporal and spatial distribution of the temperature field, pore water pressure field, and displacement field of the field monitoring section under actual working conditions. The numerical simulation results were compared with the monitoring data to quantitatively evaluate the simulation accuracy of the model. For example, the surface settlement and horizontal displacement of the cave wall at a certain monitoring section were numerically simulated, with a calculation time step of 1 day and continuous simulation for 180 days. The maximum surface settlement obtained by numerical simulation was 28.2 mm, and the maximum horizontal displacement of the cave wall was 16.5 mm, with relative errors of 8.2% and 10.3% with the measured values, respectively.

[0073] In order to quantitatively evaluate the simulation accuracy and applicability of the multi-field coupling three-dimensional numerical analysis model, three accuracy evaluation indicators, namely, average relative error, root mean square error and goodness of fit coefficient, were introduced. The average relative error reflects the average deviation between the simulated value and the measured value, the root mean square error reflects the discrete degree between the simulated value and the measured value, and the goodness of fit coefficient reflects the degree of fit between the simulated value and the measured value. Taking the 180-day temperature time series data of a monitoring section as an example, the average relative error between the simulated temperature and the measured temperature of the multi-field coupling model is 4.2%, the root mean square error is 1.5℃, and the goodness of fit coefficient is 0.92, indicating that the numerical model can simulate the actual temperature evolution process of the section well. Two applicability evaluation indicators, namely, computational efficiency and convergence, are introduced. The former reflects the numerical calculation speed, and the latter reflects the stability of the iterative solution process. After evaluation, the three-dimensional finite element model using hexahedral mesh has a calculation time of about 20 minutes for the actual engineering calculation domain with a scale of 50m×80m×60m. The Newton iteration method meets the residual convergence condition after 8 iterations, indicating that the calculation efficiency and convergence performance of the numerical model meet the requirements of engineering applications.

[0074] The multi-field coupled three-dimensional numerical analysis model established in this paper can accurately simulate the heat conduction, water migration and stress-deformation coupling process in artificial freezing engineering. The selected constitutive relations such as seepage retardation model and frost heave model can reasonably reflect the thermal-hydraulic-mechanical characteristics of the frozen soil layer, which is in good agreement with the results of indoor tests and field monitoring. In engineering practice, this model can be used to predict and analyze the spatiotemporal evolution of the frozen soil layer under different construction conditions, providing a quantitative basis for the optimal design of the freezing scheme and the control of the construction process.

[0075] In an optional embodiment, based on the acquired engineering geological parameters, a multi-field coupled three-dimensional numerical analysis model reflecting the complex coupling mechanism of the frozen soil layer is established, based on the continuous medium mechanics and porous medium theory, a thermal-hydraulic-mechanical coupling control equation is established, a constitutive model is selected, and a volume fraction average model is used to establish a functional relationship between specific heat capacity and thermal conductivity and temperature and volume ice content, a power function attenuation model is used to characterize the relationship between permeability and pore ice saturation, a volume fraction weighted average formula is used to estimate the linear expansion coefficient, a hyperbolic model is used to describe the change of elastic modulus with temperature and ice content, the thermal-hydraulic-mechanical coupling control equation is solved by a finite element method, a fully implicit solution format is used, and the steps of realizing the coupled solution of multiple field physical quantities in each time step by an iterative algorithm include:

[0076] Based on the continuous medium mechanics and porous medium theory, combined with the law of conservation of energy, mass and momentum, the heat conduction equation, water transport equation and stress balance equation in the frozen soil layer are established respectively. The heat conduction equation takes into account the influence of heat conduction and phase change latent heat release, the water transport equation uses the linear seepage law to describe the pore water seepage, and the stress balance equation introduces temperature strain and frost heave strain. The three equations are coupled to form the control equation group of the thermal-hydraulic-mechanical coupling problem of the frozen soil layer.

[0077] The established thermal-hydraulic-mechanical coupling control equations are numerically solved. First, the partial differential equations are discretized into finite element equations using the weighted residual method. Then, the time derivative terms are discretized using two-point back-difference, and the time integral parameters are introduced to form a fully implicit iterative format. Finally, an iterative algorithm is used to achieve the coupled solution of the temperature field, pore water pressure field, and displacement field in each time step. The stiffness matrix and load vector are updated through known physical quantities, and the unknown physical quantities are iteratively calculated until the convergence criteria of each physical quantity are met at the same time.

[0078] After the iteration converges, the temperature, pore water pressure and displacement variables of the current time step are updated and saved, and the next time step is entered, and the coupled iterative solution process is repeated until the transient multi-field simulation within the preset time period is completed, and the spatiotemporal evolution law of the thermal-hydraulic-mechanical characteristic parameters of the frozen soil layer is obtained.

[0079] For example, according to the law of conservation of energy, considering the influence of heat conduction and latent heat release of phase change, the heat conduction equation in the frozen soil layer is established. The equation indicates that the product of the effective specific heat capacity of the frozen soil layer and the partial derivative of the temperature with respect to time is equal to the divergence of the dot product of the effective thermal conductivity and the temperature gradient, plus the latent heat source term of phase change.

[0080] The effective specific heat capacity and effective thermal conductivity adopt the volume fraction weighted average model to establish their functional relationship with temperature and volume ice content. The effective specific heat capacity is equal to the sum of the product of the volume fraction of soil particles, unfrozen water, and ice and their specific heat capacity; the effective thermal conductivity is equal to the sum of the product of the volume fraction of soil particles, unfrozen water, and ice and their thermal conductivity.

[0081] According to the law of conservation of mass, Darcy's law is used to describe the seepage of pore water, and the water migration equation in frozen soil is established. The equation shows that the partial derivatives of the unfrozen water density and the unfrozen water volume fraction with respect to time, plus the divergence of the unfrozen water density and the unfrozen water seepage velocity, are equal to the negative phase change source term.

[0082] The unfrozen water seepage velocity is calculated using Darcy's law, which is equal to the negative unfrozen water relative permeability divided by the unfrozen water dynamic viscosity, multiplied by the pore water pressure plus the gradient of the gravitational potential energy.

[0083] The power function attenuation model is used to describe the relationship between permeability and ice saturation. The relative permeability of unfrozen water is equal to the saturated permeability multiplied by 1 minus the ice saturation to the power of n.

[0084] According to the law of conservation of momentum, temperature strain and frost heave strain are introduced to establish the stress balance equation in the frozen soil layer. The equation shows that the divergence of the stress tensor plus the volume force vector is equal to the zero vector.

[0085] The relationship between the stress tensor and the strain tensor is: the stress tensor is equal to the elastic matrix multiplied by the total strain tensor minus the temperature strain tensor minus the frost heave strain tensor.

[0086] The temperature strain tensor is calculated using the linear thermal expansion model, which is equal to the linear expansion coefficient multiplied by the difference between the temperature and the reference temperature, and then multiplied by the unit tensor. The linear expansion coefficient is estimated using the volume fraction weighted average, which is equal to the sum of the products of the volume fraction of each component and its linear expansion coefficient.

[0087] The frost heave strain tensor is calculated using the volume expansion model and is equal to the difference between the unfrozen water density and the ice density divided by the ice density, multiplied by the ice volume fraction and the unit tensor.

[0088] The elastic modulus in the elastic matrix varies with temperature and ice content and is described by a hyperbolic model. The elastic modulus is equal to the reference elastic modulus multiplied by the temperature-related term and the ice content-related term. The temperature-related term is 1 minus the hyperbolic tangent function; the ice content-related term is 1 plus the linear term of the ice content.

[0089] At this point, the coupling of heat conduction equation, water transport equation and stress balance equation constitutes the control equation group of thermal-hydraulic-mechanical coupling problem of frozen soil layer.

[0090] The weighted residual method is used to spatially discretize the control equations and transform the partial differential equations into finite element equations. Taking the heat conduction equation as an example, its integral form is established, the virtual variation of temperature is introduced, and the divergence theorem is used to transform the second-order derivative term into the first-order derivative term, and the finite element equation of the heat conduction equation is obtained.

[0091] Similarly, the finite element equations of the moisture transport equation and the stress equilibrium equation can be obtained.

[0092] The time derivative terms in the finite element equations are discretized by two-point back-difference, and time integral parameters are introduced to establish the fully implicit iterative format of the heat conduction equation, moisture migration equation, and stress equilibrium equation.

[0093] The iterative algorithm is used to achieve the coupled solution of temperature field, pore water pressure field and displacement field in each time step. The specific steps are as follows:

[0094] Given the initial conditions and boundary conditions, select the time step and time integration parameters;

[0095] Enter the time step and update the correlation matrix and load vector based on the results of the previous step;

[0096] Iterate to solve the temperature field, pore water pressure field and displacement field. Assume that the initial value is equal to the result of the previous step, then iterate cyclically, update the matrix and load vector according to the result of the previous iteration each time, and solve the current field quantity until the convergence criterion is met;

[0097] After the iteration converges, the field quantity is updated and saved, and the next time step is entered;

[0098] Repeat the above steps until all preset time steps are solved.

[0099] So far, through the finite element method and fully implicit iterative format, the numerical solution of the thermal-hydraulic-mechanical coupling problem of frozen soil layers has been realized, and the evolution laws of the temperature field, pore water pressure field and displacement field in time and space have been obtained.

[0100] Design a three-dimensional frozen soil column example. The soil column is subjected to the circulation of cold and heat sources at the top and bottom, with lateral insulation, top and bottom drainage, and lateral non-drainage. Given the initial temperature field, pore water pressure field and stress field, calculate the changes of temperature, pore water pressure and displacement of the soil column with time.

[0101] Typical frozen soil parameters are selected for calculation, including the physical parameters of soil particles, unfrozen water, and ice (density, specific heat capacity, thermal conductivity, linear expansion coefficient, etc.), as well as permeability parameters (saturated permeability coefficient, permeability power index) and mechanical parameters (elastic modulus, Poisson's ratio, hyperbolic model parameters).

[0102] The numerical simulation obtained the temporal and spatial distribution of the temperature field, pore water pressure field and displacement field of the frozen soil column under the action of hot and cold cycles. The analysis shows that:

[0103] Temperature field: The temperature of the frozen soil column shows a nonlinear distribution along the depth. The temperature drops rapidly under the cold source and expands downward, while the temperature gradually rises under the heat source, and thawing occurs from bottom to top. The evolution characteristics of the temperature field are closely related to the thermal conductivity and phase change characteristics of frozen soil.

[0104] In terms of pore water pressure field: when freezing, the upper pore water freezes first, compressing the lower pore water and increasing the pressure; as the freezing front moves downward, the pressure in the unfrozen area further increases, and at the same time, some pore water is discharged downward; when thawing, the pore ice melts, the pressure is released, the upper part absorbs water, and the pressure turns negative. The evolution characteristics of pore water pressure are closely coupled with seepage and phase change.

[0105] In terms of displacement field: when freezing, the soil column produces upward frost heave deformation, and the deformation increases as the freezing front moves downward; when thawing, the frost heave deformation gradually recovers. The deformation of the soil column is mainly determined by thermal strain and frost heave strain.

[0106] In terms of multi-field coupling effect: there is a significant coupling effect between the temperature field, pore water pressure field and displacement field of the frozen soil column. The change of temperature field causes the change of physical parameters, affecting the seepage and mechanical behavior; conversely, pore water pressure and frost heave deformation affect the temperature field and phase change process. Multi-field coupling makes the thermal-hydraulic-mechanical characteristics of frozen soil show complex temporal and spatial evolution laws.

[0107] Through the analysis of numerical examples, it is verified that the established thermal-hydraulic-mechanical coupling model of frozen soil layer can reasonably describe the multi-field coupling mechanism of frozen soil, reveal the evolution law of thermal-hydraulic-mechanical characteristics of frozen soil under the action of cold and heat, and lay the foundation for further development of frozen soil engineering stability analysis.

[0108] Based on the theory of continuous medium mechanics and porous media, this paper establishes a mathematical model for the thermal-hydraulic-mechanical coupling problem of frozen soil, including the heat conduction equation, water transport equation, and stress balance equation. At the same time, the constitutive model of soil physical parameters related to temperature and ice content is introduced. The finite element method is used for discretization, and the fully implicit time-marching format and Newton iteration algorithm are combined for numerical solution, forming a complete set of numerical analysis methods for frozen soil. This method can be applied to frozen soil engineering, cold oil and gas pipelines, seasonal frozen soil and other fields, providing theoretical support for the design, construction and scientific management of related projects.

[0109] In an optional embodiment, the steps of using a verified multi-field coupled three-dimensional numerical analysis model to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and extracting the frost heave, thaw and sink deformation time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and different depths include:

[0110] According to the freezing pipe arrangement scheme and freezing temperature curve actually adopted in the project, the corresponding cold source load is set in the multi-field coupled three-dimensional numerical analysis model. The freezing pipe adopts a combination of two-stage freezing and intermittent freezing. By performing transient heat transfer analysis, the three-dimensional temperature field distribution of the frozen soil layer at different freezing times is obtained, and the temperature cloud map of typical sections and typical time points is extracted. The circumferential and radial distribution characteristics of the soil temperature during the freezing process are analyzed. By comparing the temperature field evolution process at different freezing times, the key time nodes in the frozen soil development process are extracted, and the spatial position map of the zero-degree isotherm at different times is drawn to study the expansion and contraction process of the zero-degree isotherm over time.

[0111] Based on the results of the evolution of the temperature field during the simulated freezing construction process, the evolution of the physical and mechanical parameters of frozen soil caused by temperature changes was considered, and a thermal-mechanical coupling analysis was performed. The thermal expansion theory was used to describe the frost heave deformation, and the freeze-thaw cycle damage model was used to describe the thaw settlement deformation. The temporal and spatial distribution law of the soil displacement field during the freezing process was obtained. The deformation characteristics of the three typical stages, namely the initial freezing period, the slow freezing period, and after the foundation pit excavation, were paid attention to. The surface uplift distribution curves at different times during the freezing process were extracted, and the uplift contour map was drawn. The spatial distribution characteristics of the surface frost heave deformation were analyzed. Combined with the uplift time-history curve, the uplift rate and uplift amount were quantitatively evaluated over time. The thaw settlement and deformation characteristics caused by foundation pit excavation and cavern excavation were analyzed, and the surface settlement contour map after excavation was drawn. The difference in surface deformation before and after excavation was compared. Combined with the time-history curves of the cavern vault settlement and the side wall inward displacement, the time development law of soil deformation below the excavation surface was studied. The deformation recovery process from the end of freezing to the complete melting of the soil was simulated, the thaw settlement development rate and duration were analyzed, and the surface settlement and cavern deformation contour maps at the end of thaw settlement were drawn.

[0112] An orthogonal numerical test scheme was designed, and multiple freezing temperatures, freezing times, freezing pipe spacings and excavation depths of foundation pits were selected for numerical simulation calculations. The deformation cloud maps and time curves of frost heave and thaw settlement under different freezing temperature conditions were compared, and the influence of freezing temperature on deformation was quantitatively analyzed. The surface uplift and cavern deformation under different freezing time conditions were compared, and the influence of freezing time on frost heave and thaw settlement deformation was quantitatively analyzed. The frost heave deformation rate and maximum deformation under different freezing pipe spacing conditions were compared, and the influence of freezing pipe spacing on frost heave deformation was quantitatively analyzed. The duration and settlement of thaw settlement under different excavation depths were compared, and the influence of excavation depth on thaw settlement deformation was quantitatively analyzed. Based on the numerical simulation results, a quantitative relationship between construction process parameters and frost heave and thaw settlement deformation indicators was constructed.

[0113] For example, geometric model construction: According to the actual size of the project, a three-dimensional geometric model is constructed in the finite element analysis software, including the freezing pipe, the main structure, the surrounding soil, etc., and the geometric characteristics of the structure and the soil are depicted as finely as possible. Taking an underground project as an example, the main structure is a cylindrical cavern with a diameter of 10 meters and a height of 12 meters. The cavern is buried at a depth of 18 meters. The surrounding soil range is 5 times the diameter of the cavern, that is, 50 meters square. The soil is considered in layers, and from top to bottom, there are 4 meters thick silty clay, 6 meters thick silt sand and sand and gravel layers.

[0114] Meshing: Hexahedral meshes are used to spatially discretize the geometric model. Encrypted meshes are set near the freezing pipes and around the caverns, and sparse meshes can be used appropriately in areas far from the main structure. After meshing, the entire model contains about 500,000 nodes and about 450,000 units.

[0115] Constitutive model and physical parameters: The soil adopts the modified Mohr-Coulomb constitutive model, considering the degradation of strength and stiffness parameters caused by temperature changes, and the frozen pipe and concrete structure adopt the linear elastic constitutive model. The physical and mechanical parameters of the soil are determined based on indoor tests and engineering experience, such as the elastic modulus of silty clay is 20MPa, Poisson's ratio is 0.35, the elastic modulus of silty sand is 35MPa, Poisson's ratio is 0.3, and the evolution of thermal expansion coefficient and freeze-thaw cycle damage parameters is considered during the freezing process.

[0116] Initial conditions and boundary conditions: The initial stress condition is determined according to the soil weight and the lateral pressure coefficient, and the initial displacement condition is assumed to be zero. The initial temperature condition is determined according to the average stratum temperature, usually 10 to 14 degrees Celsius. The boundary conditions include displacement boundary and load boundary, bottom fixed constraint, and lateral simulation using simple supports or rolling supports.

[0117] Cold source load: According to the actual arrangement of the freezing pipe, the cold source load is set in the finite element model. Taking a certain project as an example, 8 circumferential freezing pipes are arranged, with a pipe diameter of 114 mm, a spacing of 1.13 meters, and a freezing temperature of -25 degrees Celsius; 120 longitudinal freezing pipes are arranged, with a pipe diameter of 76 mm and a spacing of 0.85 meters. The freezing temperature is divided into two stages, -30 degrees Celsius for the first 15 days, and then rises to -20 degrees Celsius and maintains after 15 days. In the finite element model, negative temperature loads are used to simulate the cooling process of the freezing pipe.

[0118] Transient heat transfer analysis: Considering the difference in thermal conductivity between soil and concrete, transient heat transfer analysis is used to simulate the evolution of temperature field during freezing. The control equation is the Fourier heat transfer equation. During the solution process, the temperature field distribution is calculated at each time step, and then the temperature load is applied to the stress analysis module. The analysis time step is determined according to the calculation accuracy and convergence speed. It can be 1 day in the early stage and 3 to 5 days in the middle and late stages.

[0119] Temperature field distribution characteristics: Extract the three-dimensional temperature cloud map under different freezing time and analyze the temperature distribution characteristics during the freezing process. Take a certain project as an example. After freezing for 30 days, the soil temperature within 0.5 meters from the freezing pipe is lower than -10 degrees Celsius; within 1.5 meters from the freezing pipe, the soil temperature is lower than 0 degrees Celsius. As the freezing time increases, the negative temperature zone continues to expand. After 90 days, the negative temperature zone extends to 2.5 meters from the freezing pipe.

[0120] Frozen soil development process: By comparing the temperature fields at different times, the frozen soil development process is plotted. The zero-degree isotherm is used as the frozen soil boundary, and its spatial position changes over time are analyzed. In a certain project, the thickness of the frozen soil wall was about 1.8 meters after 30 days; after 60 days, the thickness of the frozen soil wall increased to 2.5 meters; after 90 days, the thickness of the frozen soil wall reached 3.1 meters; after 120 days, with the implementation of intermittent freezing, the thickness of the frozen soil wall remained basically stable.

[0121] Thermal-mechanical coupling analysis: The temperature field analysis results are loaded into the stress analysis module as loads. The degradation of the physical and mechanical parameters of the soil caused by temperature changes is considered, and the thermal-mechanical coupling analysis method is used to simulate the deformation characteristics of the freezing process.

[0122] Frost heave deformation analysis: The thermal expansion theory is used to describe frost heave deformation, that is, the volume strain at freezing temperature is linearly related to the temperature change and is related to the thermal expansion coefficient of the soil. Taking a certain project as an example, in the early stage of freezing, due to the rapid drop in soil temperature, the thermal expansion deformation rate is large, and the maximum surface uplift is about 18 mm at 30 days; after entering the slow freezing period, the surface uplift rate gradually decreases, and the cumulative uplift reaches 38 mm at 90 days; after the freezing ends, the uplift remains basically stable. At the same time, the frost heave deformation gradually decays along the depth direction, and the uplift below the cave roof is less than 5 mm.

[0123] Thaw settlement deformation analysis: The freeze-thaw cycle damage model is used to describe the thaw settlement deformation. It is assumed that the soil undergoes microstructural damage during the freeze-thaw cycle, resulting in increased soil compressibility. The model parameters are determined based on indoor freeze-thaw tests. Taking a certain project as an example, after excavation, the stress around the cavern is redistributed due to the unloading of the excavation, the soil undergoes creep deformation, and the vault sinking rate is significantly accelerated, reaching 15 mm within 3 months; subsequently, under the support of frozen soil, the cavern deformation gradually converges, and the sinking amount is maintained at about 18 mm; after entering the thaw settlement period, as the soil gradually returns to normal temperature, the thaw settlement deformation continues to develop, and the sinking rate accelerates again. The thaw settlement period lasts for about 6 months, and the final cumulative sinking amount reaches 52 mm.

[0124] Deformation during excavation and thawing period: By comparing the simulation results before and after excavation, the deformation characteristics caused by foundation pit excavation and cavern excavation are analyzed. The deformation during thawing period is simulated by setting a temperature rise load to analyze the development rate and duration of thawing. Taking a certain project as an example, after excavation, the stress around the cavern was redistributed, the vault sank significantly, and the side wall moved inward by about 12 mm; in the early stage of thawing period, the deformation developed rapidly, and the vault sank at a rate of 0.7 mm / day within 1 month. After 2 months, it gradually became flat and the sinking rate dropped to 0.2 mm / day; after 6 months, the thawing was basically over, and the deformation of the cavern gradually stabilized.

[0125] Freezing temperature: Design different freezing temperature conditions, such as -15℃, -20℃, -25℃, and -30℃, conduct numerical simulations respectively, extract the frost heave deformation time history curves and spatial distribution cloud maps under different conditions, and quantitatively compare the frost heave deformation rate and the maximum frost heave amount. Taking a certain project as an example, when the freezing temperature drops from -15℃ to -30℃, the surface uplift after freezing for 30 days increases from 8 mm to 25 mm, an increase of more than 1 times, indicating that reasonable control of freezing temperature is crucial to controlling frost heave deformation.

[0126] Freezing time: Design different freezing time conditions, such as 60 days, 90 days, 120 days, and 150 days, and perform numerical simulations to extract the surface uplift and cavern deformation under different conditions. Taking a certain project as an example, the freezing time was extended from 60 days to 150 days, the surface uplift increased from 25 mm to 46 mm, and the cavern vault settlement increased from 18 mm to 31 mm, indicating that proper control of the freezing time can reduce frost heave deformation while ensuring the freezing effect.

[0127] Freezing pipe spacing: Design different freezing pipe spacing conditions, such as 0.8m, 1.0m, 1.2m, and 1.5m, and perform numerical simulations to extract the frost heave deformation rate and maximum frost heave amount under different conditions. Taking a certain project as an example, when the circumferential spacing of the freezing pipes increases from 0.8m to 1.5m, the surface uplift rate after 30 days of freezing decreases from 0.8mm / day to 0.4mm / day, and the maximum uplift also decreases from 30mm to 12mm, a decrease of 60%. Therefore, appropriately increasing the freezing pipe spacing can significantly reduce the risk of frost heave deformation.

[0128] Excavation depth: Design different excavation depth conditions, such as 0.2 times the tunnel diameter, 0.4 times the tunnel diameter, and 0.6 times the tunnel diameter, and perform numerical simulations to extract the duration of the thawing period and the maximum settlement. Taking a certain project as an example, when the excavation depth increases from 0.2 times the tunnel diameter to 0.6 times the tunnel diameter, the duration of the thawing period is extended from 2 months to 5 months, and the maximum settlement of the cavern vault increases from 28 mm to 65 mm, indicating that the greater the excavation depth, the longer the duration of the thawing deformation and the greater the settlement.

[0129] Frost heave deformation control: According to the results of parameter impact analysis, the relationship curve between freezing temperature, freezing time, freezing pipe spacing and maximum frost heave is drawn to obtain the deformation control threshold of each parameter. Taking a certain project as an example, when the maximum frost heave is controlled within 30 mm, the freezing temperature should be higher than -25℃, the freezing time should be less than 120 days, and the circumferential spacing of the freezing pipes should be greater than 1 meter.

[0130] Control of deformation due to melting and settling: Draw the relationship curve between excavation depth, duration of melting and settling, and maximum settlement, and obtain the deformation control threshold of excavation depth. Taking a certain project as an example, when the settlement during the melting and settling period is controlled within 50 mm, the excavation depth should be less than 0.4 times the tunnel diameter. At the same time, the monitoring frequency during the melting and settling period should be reasonably arranged to track the development of deformation.

[0131] Multi-objective optimization: Under the premise of meeting the freezing effect and construction progress requirements, the multi-objective optimization method is used to determine the optimal combination of construction parameters with the goal of minimizing frost heave deformation and thaw settlement deformation. Taking a certain project as an example, after optimization, the freezing temperature is -20℃, the freezing time is 90 days, the circumferential spacing of the freezing pipe is 1.2 meters, and the excavation depth is controlled at 0.3 times the hole diameter. While ensuring that the frozen wall thickness meets the design requirements, the frost heave and thaw settlement deformation are minimized.

[0132] This paper adopts a multi-field coupling numerical analysis method, comprehensively considers the temporal and spatial evolution of the freezing temperature field and deformation field, and can accurately predict the frost heave and thaw deformation characteristics during the freezing construction process through numerical simulation and parameter analysis, which provides an important basis for the reasonable design of freezing schemes and the optimization of construction parameters, and is of great significance for ensuring engineering safety and surrounding environmental protection. In actual projects, it is also necessary to calibrate and correct the model in combination with on-site monitoring data to further improve the prediction accuracy and reliability.

[0133] In an optional embodiment, based on simulation analysis, comprehensively considering the construction progress requirements, environmental condition restrictions, and structural safety requirements, the steps of determining the target threshold range of frozen soil layer frost heave deformation control and the target threshold range of thaw settlement deformation control through a multi-objective optimization algorithm include:

[0134] The maximum ground uplift, maximum cavern deformation, total freezing period and total cost are determined as optimization targets, and the functional relationship between them and freezing temperature, freezing time, freezing pipe spacing and excavation depth is established. The maximum ground uplift and maximum cavern deformation are obtained by fitting the numerical simulation results, the total freezing period is calculated according to the freezing construction curve, and the total cost takes into account material costs, energy costs and labor costs.

[0135] According to engineering experience and previous numerical simulation results, the value ranges of the four optimization variables, namely, freezing temperature, freezing time, freezing pipe spacing and excavation depth, are set;

[0136] An improved non-dominated sorting genetic algorithm is used, with the optimization objective function as the fitness function and the value range of the optimization variable as the constraint condition. By setting a reasonable population size, crossover probability and mutation probability, a set of Pareto optimal solutions is obtained after multiple iterations. Each solution in the solution set is a balance between the optimization objectives that cannot be further improved, reflecting the trade-off between frost heave, thaw settlement deformation and total cost under different construction schemes.

[0137] On the basis of the Pareto optimal solution set, according to the importance of deformation control and the tolerance of total cost in actual engineering, one or several control thresholds that meet the requirements are selected as recommended solutions;

[0138] By using the engineering analogy method, the deformation control effects of the recommended control threshold scheme and existing similar projects were compared to evaluate the rationality of the threshold. The whole freezing construction process simulation was re-conducted under the guidance of the recommended control threshold scheme by using the numerical simulation method.

[0139] According to the recommended control threshold scheme, three deformation warning levels are divided into mild, moderate and severe levels, and corresponding construction parameter adjustment measures, unloading measures and reinforcement measures are formulated. In the actual construction process, the early warning and emergency control mechanism are promptly activated through real-time comparison of deformation monitoring data with the recommended control threshold scheme.

[0140] For example, the optimization goal is determined: considering the actual needs of the project and the results of numerical simulation, the maximum surface uplift, the maximum deformation of the cave, the total freezing period and the total cost are selected as the optimization goals. Among them, the maximum surface uplift and the maximum deformation of the cave reflect the control effect of frost heave and thaw settlement deformation, the total freezing period reflects the construction period, and the total cost reflects the economy.

[0141] Surface uplift and cavern deformation: Using numerical simulation results, the maximum surface uplift, cavern vault settlement, and sidewall inward displacement under different working conditions were statistically analyzed. The multivariate nonlinear regression method was used to fit the functional relationship between the uplift and deformation and the freezing temperature, freezing time, freezing pipe spacing, and excavation depth. Taking a certain project as an example, after fitting, the calculation formula for the maximum surface uplift S_max (mm) is: S_max = 0.75T^2 - 3.6t + 0.032L^2 + 28, where T is the freezing temperature (℃), t is the freezing time (days), and L is the freezing pipe spacing (m). The calculation formula for the cavern vault settlement is: S_v = 2.1T - 0.08t + 0.25d + 12, where d is the excavation depth (m).

[0142] Freezing period: According to the freezing temperature and frozen soil development rate, calculate the freezing time required to reach the designed frozen soil thickness. Taking a certain project as an example, when the freezing temperature is -25℃, the frozen soil development rate is about 2.5cm / d, and the designed frozen soil thickness is 3m, the freezing period is about 120 days.

[0143] Freezing cost: Taking into account the material cost, energy cost and labor cost, a functional relationship between the total cost and the freezing temperature and freezing time is established. The material cost mainly includes the purchase and installation cost of the freezing pipe and the refrigerant; the energy cost is related to the freezing temperature and freezing time; the labor cost is related to the freezing period. Taking a certain project as an example, it is estimated that the calculation formula of the total cost C (10,000 yuan) is: C = 7.5T^2 + 0.33t + 85, where T is the freezing temperature (℃) and t is the freezing time (days).

[0144] Freezing temperature: According to engineering experience, the freezing temperature is generally between -15℃ and -30℃. If the temperature is too high, the freezing effect is poor; if the temperature is too low, the energy consumption cost is high. Taking a certain project as an example, the optimization range is -20℃~-30℃.

[0145] Freezing time: According to the previous simulation results, the freezing time is generally between 60 days and 150 days. If the time is too short, the thickness of the frozen soil will be insufficient; if the time is too long, the construction period will be delayed. Taking a certain project as an example, the optimization range is 90 days to 120 days.

[0146] Freezing pipe spacing: According to experience, the circumferential spacing of freezing pipes is generally between 0.8m and 1.5m. If the spacing is too small, the cost will be high; if the spacing is too large, the frozen soil will be sealed for a long time. Taking a certain project as an example, the optimization range is 1.0m~1.4m.

[0147] Excavation depth: The excavation depth is determined according to the size of the main structure, generally between 0.2 and 0.6 times the hole diameter. For example, in a certain project, the hole diameter is 10m, and the optimization range is 2m~6m.

[0148] Optimization algorithm: The improved non-dominated sorting genetic algorithm (NSGA-II) is adopted, which can effectively handle multi-objective optimization problems, has high search efficiency and fast convergence speed.

[0149] Fitness function: The weighted combination of surface uplift, cavern deformation, freezing period and total cost is used as the fitness function, and the weight coefficient is determined according to the importance of each target. Taking a certain project as an example, the expression of the fitness function f is: f = 0.3S_max + 0.4S_v + 0.1t + 0.2C, and the weight coefficients are 0.3, 0.4, 0.1 and 0.2 respectively, reflecting that the importance of deformation control is higher than the period and cost.

[0150] Genetic operator: binary coding is used, the crossover operator uses double-point crossover, and the crossover probability is 0.8; the mutation operator uses uniform mutation, and the mutation probability is 0.1. Taking a certain project as an example, the population size is 100, the number of iterations is 500, and after optimization search, a set of Pareto optimal solutions containing 50 non-dominated solutions is obtained.

[0151] In the Pareto optimal solution set, according to the importance of deformation control and the tolerance of cost, one or several solutions that meet the requirements are selected as the recommended threshold solution. Taking a certain project as an example, considering the importance of deformation control, the solution with smaller surface uplift and cavern deformation is selected, and the recommended control threshold is: the maximum surface uplift is 30mm, and the maximum cavern vault subsidence is 25mm. Under this solution, the freezing temperature is -25℃, the freezing time is 108 days, the freezing pipe spacing is 1.2m, the excavation depth is 3.5m, and the total cost is 9.6 million yuan.

[0152] The engineering analogy method is used to compare the deformation control effect of the recommended threshold with that of similar projects. Taking a certain project as an example, the analogy project uses a freezing temperature of -20℃ and a freezing time of 90 days. The maximum surface uplift is 35mm and the cavern sinking is 30mm, which is close to the recommended threshold, indicating that the threshold selection is reasonable.

[0153] Using numerical simulation method, the simulation analysis was repeated under the guidance of the recommended threshold. Taking a certain project as an example, the simulated surface uplift and cavern deformation were consistent with the recommended threshold, and the maximum deviation did not exceed 5%, which verified the feasibility of the recommended threshold.

[0154] According to the recommended threshold, three deformation warning levels are divided into mild, moderate and severe. Taking a certain project as an example, the ground uplift classification standard is: below 30mm is normal, 30-40mm is mild warning, 40-50mm is moderate warning, and above 50mm is severe warning; the cave deformation classification standard is: below 25mm is normal, 25-35mm is mild warning, 35-45mm is moderate warning, and above 45mm is severe warning.

[0155] According to different warning levels, corresponding construction parameter adjustment measures, unloading measures and reinforcement measures are formulated. Taking a certain project as an example, when the monitoring data reaches a mild warning, measures such as lowering the freezing temperature and extending the freezing time are taken; when it reaches a moderate warning, measures such as local unloading and additional support are taken; when it reaches a severe warning, measures such as stopping construction, artificial cooling, and grouting reinforcement are taken.

[0156] In the actual construction process, the early warning and emergency response are initiated in time through the real-time comparison of deformation monitoring data with the recommended threshold. Taking a certain project as an example, when the surface uplift rate is monitored to exceed 1mm / day for three consecutive days, or the cavern deformation rate is monitored to exceed 0.5mm / day for three consecutive days, the early warning is initiated in time; when the measured deformation exceeds the recommended threshold, the emergency measures are immediately initiated. At the same time, according to the deformation development trend, the freezing plan and excavation plan are dynamically adjusted to ensure that the deformation is controlled within a safe range.

[0157] This paper adopts a multi-objective optimization method, and determines the deformation control threshold range based on the balance between frost heave and thaw deformation control and construction cost, and formulates a graded warning and emergency control mechanism. This method can effectively guide the deformation control during the construction process of artificial freezing method, has strong operability, and has high engineering application value.

[0158] In an optional embodiment, an improved non-dominated sorting genetic algorithm is used, the optimization objective function is used as the fitness function, the value range of the optimization variable is used as the constraint condition, and a reasonable population size, crossover probability and mutation probability are set. After multiple iterations, the steps of obtaining a set of Pareto optimal solutions include:

[0159] The freezing temperature, freezing time, freezing pipe spacing and excavation depth are taken as optimization variables, the maximum surface uplift, the maximum cavern deformation, the total freezing period and the total cost are taken as optimization targets, and the initial population of size N is randomly generated by real number coding. Each individual in the population corresponds to a set of optimization variable value combinations, representing a freezing construction plan.

[0160] Perform fast non-dominated sorting on the current population, and divide it into different non-dominated levels according to the Pareto dominance relationship between individuals. Individuals in the same level do not dominate each other. In each non-dominated level, calculate the crowding distance of individuals on each target, and obtain the total crowding degree of individuals to maintain population diversity.

[0161] Parent individuals are selected from the current population through binary tournament selection, and simulated binary crossover and polynomial mutation operations are used to generate offspring with the same size as the parent. Crossover and mutation are designed to search and perturb optimization variables.

[0162] Re-execute fast non-dominated sorting and crowding calculation on the merged parent and child generations, and select N optimal individuals from them based on non-dominated relations and crowding to generate a new generation of population; when the number of iterations reaches the preset maximum number of generations, the optimization is terminated, and the non-dominated solution set of the final population is output as the Pareto optimal solution set. Each solution in the solution set corresponds to a frozen construction plan that takes into account both deformation control and cost saving;

[0163] Engineering test data and finite element analysis are used to establish a quantitative relationship between optimization objectives and optimization variables, and a fitness evaluation model is constructed; the optimal solution set can be displayed in real time in the decision support system, and the optimization plan can be recommended to guide the freezing construction, and the freezing parameters can be dynamically adjusted based on monitoring data to achieve real-time control of deformation.

[0164] Exemplarily, the detailed technical solution and implementation steps of using the improved non-dominated sorting genetic algorithm to solve the multi-objective optimization problem are given below:

[0165] Optimization variables: Select freezing temperature, freezing time, freezing pipe spacing and excavation depth as optimization variables. Taking a certain project as an example, the value range of the optimization variables is: freezing temperature -20℃~-30℃, freezing time 90 days~120 days, freezing pipe spacing 1.0m~1.4m, excavation depth 2m~6m.

[0166] Optimization objectives: Select the maximum surface uplift, maximum cavern deformation, total freezing period and total cost as optimization objectives. Taking a certain project as an example, the calculation formula for the maximum surface uplift is S_max = 0.75T^2 - 3.6t + 0.032L^2+ 28, the calculation formula for the cavern vault settlement is S_v = 2.1T - 0.08t + 0.25d + 12, the freezing period is calculated based on the freezing temperature and frozen soil development rate, and the calculation formula for the total cost is C = 7.5T^2 + 0.33t + 85.

[0167] Fitness function: The weighted combination of the four optimization objectives is used as the fitness function, and the weight coefficient reflects the importance of each objective. Taking a certain project as an example, the expression of the fitness function f is f = 0.3S_max + 0.4S_v + 0.1t +0.2C, and the weight coefficients are 0.3, 0.4, 0.1 and 0.2 respectively.

[0168] Initial population generation: Real number coding is used to randomly generate an initial population of size N. Taking a certain project as an example, the population size N=100 is taken, each individual contains four optimization variables, and the initial population is formed by randomly generating a combination of values ​​of the optimization variables.

[0169] Fast non-dominated sort: Perform fast non-dominated sort on the current population and divide it into different non-dominated levels according to the Pareto dominance relationship between individuals. The first level is the optimal non-dominated solution set, the second level is the second, and so on. Individuals in the same level do not dominate each other.

[0170] Crowding calculation: In each non-dominated level, calculate the crowding distance of the individual on each target. The crowding distance reflects the distribution density of the individual in the target space. The larger the crowding distance, the sparser the space around the individual. The total crowding of the individual is the sum of the crowding distances on each target.

[0171] Genetic operation: Parent individuals are selected from the current population through binary tournament selection, and offspring are generated by simulated binary crossover and polynomial mutation operations. Taking a certain project as an example, the crossover probability is 0.8, the mutation probability is 0.1, and 100 offspring individuals are generated.

[0172] Elite retention strategy: re-execute fast non-dominated sorting and crowding calculation on the merged parent and child generations, and select N optimal individuals from them based on non-dominated relations and crowding to generate a new generation of population. Non-dominated relations are prioritized. If two individuals belong to the same level, the individual with a larger crowding distance is selected to maintain the diversity of the population.

[0173] Termination condition: When the number of iterations reaches the preset maximum number of generations, the optimization is terminated. Taking a certain project as an example, the maximum number of iterations is 500, that is, the optimization is terminated after 500 generations of evolution.

[0174] Output result: The non-dominated solution set of the final population is output as the Pareto optimal solution set. Each solution in this solution set is a balance between the optimization objectives that cannot be further improved, representing a frozen construction plan that takes into account both deformation control and cost savings.

[0175] The quantitative relationship between optimization target and optimization variables is established by using engineering test data and finite element analysis. Taking a certain project as an example, the functional relationship between surface uplift, cavern deformation and freezing temperature, freezing time, freezing pipe spacing and excavation depth is obtained through regression analysis, and the functional relationship between freezing period and total cost and optimization variables is obtained through empirical formula.

[0176] In the optimization algorithm, the above functional relationship is used to calculate the fitness value of each individual. Taking a certain project as an example, the four optimization variables of the individual are substituted into the fitness function to obtain the fitness values ​​of the surface uplift, cavern deformation, freezing period and total cost corresponding to the individual.

[0177] The Pareto optimal solution set is imported into the decision support system, and the deformation control effect and cost of different freezing construction schemes are intuitively displayed through charts and data. Taking a certain project as an example, the optimal solution set contains 50 solutions, each of which corresponds to a combination of freezing temperature, freezing time, freezing pipe spacing and excavation depth, as well as the corresponding surface uplift, cavern deformation, freezing period and total cost.

[0178] According to the actual needs of the project, the recommended freezing construction scheme is selected from the optimal solution set to guide the on-site construction. Taking a certain project as an example, considering the importance of deformation control, the solution with smaller surface uplift and cavern deformation is selected as the recommended scheme. The control parameters of this scheme are: freezing temperature -25℃, freezing time 108 days, freezing pipe spacing 1.2m, and excavation depth 3.5m.

[0179] During the construction process, the recommended control scheme is dynamically checked and adjusted using real-time monitoring data. Taking a certain project as an example, freezing construction is carried out under the guidance of the recommended scheme, while monitoring the surface uplift and cavern deformation. When the monitoring data deviates from the recommended threshold, parameters such as freezing temperature and freezing time are optimized in a timely manner to achieve real-time control of deformation.

[0180] In this paper, an improved non-dominated sorting genetic algorithm is used to effectively solve the multi-objective optimization problem in the construction process of artificial freezing method. By reasonably defining the optimization variables, optimization objectives and fitness functions, and utilizing the optimization search capability of the genetic algorithm, a set of Pareto optimal solutions that take into account both deformation control and cost savings can be obtained. Combined with the actual needs of the project and the feedback of monitoring data, the recommended solution can be selected from the optimal solution set to guide the construction and realize the real-time control of deformation. This method is highly operational and suitable for different types of artificial freezing projects, which is of great significance for guiding practice.

[0181] In an optional embodiment, the data processing and analysis system performs real-time analysis and fusion of the monitoring data uploaded by the wireless sensor network, extracts data parameters, and compares them with preset frost heave deformation control target thresholds and thaw settlement deformation control target thresholds in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the steps of outputting corresponding construction control decision instructions include:

[0182] Define unified data analysis rules for deployed wireless sensors, which include key fields of wireless sensor data collection and corresponding data processing algorithms. Use data analysis rules to analyze the original monitoring data uploaded by wireless sensors, extract key fields and form structured monitoring data.

[0183] Multiple groups of structured monitoring data collected by similar wireless sensors are summarized, and a weighted average fusion algorithm is used to perform fusion calculation on each monitoring data. In the weighted average fusion algorithm, the weight of the wireless sensor is determined according to its measurement performance, and the sum of the weights of similar wireless sensors participating in the fusion calculation is 1; the fused monitoring data is written into the time series data table of the monitoring database together with the corresponding timestamp at the set time interval.

[0184] Exemplarily, the following is a detailed technical solution and implementation steps for real-time analysis and early warning using wireless sensor network monitoring data:

[0185] Select appropriate wireless sensor nodes, including temperature sensors, displacement sensors, pressure sensors, etc., to monitor the frost heave deformation and thaw deformation during artificial freezing construction. Taking a certain project as an example, a wireless sensor node based on the ZigBee protocol was selected, with a temperature measurement range of -40℃~60℃, a displacement measurement range of 0~50mm, and a pressure measurement range of 0~10MPa.

[0186] Reasonable layout of wireless sensor nodes, a temperature sensor is laid out every 1~2m in the frozen soil range, displacement sensors and pressure sensors are laid out on the surface and around the cave, and the sensor nodes realize data transmission through a self-organizing network. Taking a certain project as an example, a total of 50 temperature sensors, 20 displacement sensors and 10 pressure sensors are laid out.

[0187] Set the sampling frequency and data upload cycle of the wireless sensor nodes to balance monitoring needs and energy consumption. Taking a certain project as an example, the sampling frequency of the temperature sensor is 1 time / 10 minutes, the sampling frequency of the displacement sensor and pressure sensor is 1 time / 30 minutes, and the data upload cycle is 1 hour.

[0188] Define unified data parsing rules, including the key fields of wireless sensor data collection and the corresponding data processing algorithms. Taking a certain project as an example, the key fields of temperature sensor data are node ID, sampling time, and temperature value, and the data processing algorithm is temperature value calibration; the key fields of displacement sensor data are node ID, sampling time, X-axis displacement, Y-axis displacement, and Z-axis displacement, and the data processing algorithm is displacement value correction.

[0189] The original monitoring data uploaded by the wireless sensor is parsed using data parsing rules, and key fields are extracted to form structured monitoring data. Taking a certain project as an example, the original data uploaded by the temperature sensor is "T001, 2022-01-01 08:00:00, -12.5", and after parsing, the structured data "node ID: T001, sampling time: 2022-01-01 08:00:00, temperature value: -12.1" is obtained; the original data uploaded by the displacement sensor is "D005, 2022-01-01 08:30:00, 12.3, 5.6, 2.1", and after parsing, the structured data "node ID: D005, sampling time: 2022-01-01 08:30:00, X-direction displacement: 12.8, Y-direction displacement: 5.9, Z-direction displacement: 2.3" is obtained.

[0190] Multiple groups of structured monitoring data collected by similar wireless sensors are summarized, and the weighted average fusion algorithm is used to perform fusion calculation on each monitoring quantity. Taking a certain project as an example, the temperature values ​​of -12.1℃, -12.5℃, and -11.9℃ collected by three temperature sensors T001, T002, and T003 at 2022-01-01 08:00:00 are fused. Assuming the weights of the three sensors are 0.3, 0.4, and 0.3 respectively, the fused temperature value is -12.2℃.

[0191] The fused monitoring data together with the corresponding timestamp are written into the time series data table of the monitoring database at set time intervals. Taking a certain project as an example, the fused temperature, displacement, and pressure monitoring data are written into the time series data table every hour, and the data format is "timestamp, temperature, displacement in X direction, displacement in Y direction, displacement in Z direction, pressure".

[0192] According to the design requirements of artificial freezing construction, the target thresholds for frost heave deformation control and thaw settlement deformation control are preset. Taking a certain project as an example, the maximum surface uplift is controlled within 30mm, and the maximum deformation of the cave is controlled within 20mm.

[0193] The real-time monitoring data in the monitoring database is compared with the preset threshold value, and the warning signal is triggered when the monitoring parameter exceeds the threshold range. Taking a certain project as an example, when the surface uplift exceeds 30mm or the cavern deformation exceeds 20mm, the system automatically pops up a warning prompt box and sends a warning SMS to relevant personnel.

[0194] After the early warning signal is triggered, the reverse optimization analysis module is automatically called to use the monitoring data to invert the current freezing state and optimize the freezing temperature, freezing time and other control parameters. Taking a certain project as an example, when the ground uplift exceeds the standard, the reverse optimization analysis shows that the freezing temperature needs to be adjusted from -25℃ to -28℃, and the freezing time needs to be extended to 120 days.

[0195] The results of the reverse optimization analysis are converted into executable construction control decision instructions and sent to the automatic control system. Taking a certain project as an example, the adjusted freezing temperature of -28℃ and freezing time of 120 days are set as the new control parameters of the refrigeration unit, and unattended adjustment is achieved through the automatic control system to realize real-time control of deformation.

[0196] Develop a visualization display interface for monitoring data, and intuitively present the changing trends of key indicators such as surface uplift and cavern deformation in the form of curves, bar graphs, and heat maps. Taking a certain project as an example, the surface uplift curve and cavern deformation curve of each monitoring section are displayed in real time on the monitoring screen. Different colors indicate the size of the deformation, and areas exceeding the warning threshold are highlighted in red.

[0197] Develop a human-computer interaction interface to support users to interact with the system through touch screen, mouse, etc., query specific monitoring data, adjust warning thresholds, issue construction control instructions, etc. Take a certain project as an example, the on-duty engineer can choose to view the detailed data of a certain monitoring section on the touch screen, modify the warning threshold of the surface uplift to 25mm, and issue an instruction to start the electric heating system in advance.

[0198] This paper uses wireless sensor networks to monitor the artificial freezing construction process. Through data parsing, fusion, analysis and early warning, real-time monitoring and control of frost heave deformation and thaw settlement deformation can be achieved. The system automatically identifies abnormal conditions according to preset thresholds, calls the reverse optimization analysis model to give construction control decisions, and executes unattended through the automated control system, thereby effectively guiding the artificial freezing construction. At the same time, through visual display and human-computer interaction functions, it provides engineering management personnel with intuitive and convenient monitoring data query and control methods, improving the safety and intelligence level of artificial freezing construction.

[0199] Figure 2 FIG. 1 is a schematic diagram of a construction control system for the frost heave and thaw settlement characteristics of a frozen soil layer according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0200] The first unit is used to obtain multiple engineering geological characteristic parameters of the construction site; based on the obtained multiple engineering geological characteristic parameters, a multi-field coupled three-dimensional numerical analysis model simulating the heat conduction, water migration, and stress deformation of the frozen soil layer is established. By conducting indoor frost heave and thaw settlement characteristic tests and field monitoring tests, multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer are obtained, and the comparative data are used to verify the established multi-field coupled three-dimensional numerical analysis model;

[0201] The second unit is used to use the verified multi-field coupling three-dimensional numerical analysis model to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and to extract the time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and at different depths; on the basis of simulation analysis, comprehensively consider the construction progress requirements, environmental conditions, and structural safety requirements, and determine the target threshold range of the frozen soil layer frost heave deformation control and the target threshold range of the thaw deformation control through a multi-objective optimization algorithm; deploy a wireless sensor network in the frozen soil layer to monitor the temporal and spatial distribution data of the temperature field, moisture field, deformation field, and stress field of the frozen soil layer in real time, and upload the real-time monitoring data to the data processing and analysis system;

[0202] The third unit is used for data processing and analysis system to perform real-time analysis and fusion of monitoring data uploaded by the wireless sensor network, extract data parameters, and compare them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the corresponding construction control decision instructions are output.

[0203] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0204] processor;

[0205] a memory for storing processor-executable instructions;

[0206] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0207] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0208] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A construction control method for the frost heave and thaw settlement characteristics of frozen soil layers, characterized in that: include: Obtain multiple engineering geological characteristic parameters of the construction site; based on the obtained multiple engineering geological characteristic parameters, establish a multi-field coupled three-dimensional numerical analysis model that simulates the heat conduction, water migration, and stress deformation of the frozen soil layer; obtain multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer by conducting indoor frost heave and thaw settlement characteristic tests and on-site monitoring tests; use the comparative data to calibrate and verify the established multi-field coupled three-dimensional numerical analysis model; Using the verified multi-field coupling three-dimensional numerical analysis model, the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process is simulated and analyzed, and the time history curves and spatial distribution cloud maps of the frozen soil layer at different construction stages and at different depths are extracted; on the basis of the simulation analysis, the requirements of the construction schedule, environmental conditions, and structural safety are comprehensively considered, and the target threshold ranges of the frozen soil layer frost heave deformation control and the target threshold ranges of the thaw deformation control are determined through a multi-objective optimization algorithm; a wireless sensor network is deployed in the frozen soil layer to monitor the temporal and spatial distribution data of the temperature field, moisture field, deformation field, and stress field of the frozen soil layer in real time, and the real-time monitoring data is uploaded to the data processing and analysis system; The data processing and analysis system analyzes and fuses the monitoring data uploaded by the wireless sensor network in real time, extracts data parameters, and compares them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the corresponding construction control decision instructions are output; Based on the obtained multiple engineering geological characteristic parameters, a multi-field coupled three-dimensional numerical analysis model simulating heat conduction, water migration, and stress deformation of frozen soil layers is established. By carrying out indoor frost heave and thaw settlement characteristic tests and field monitoring tests, multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of frozen soil layers are obtained. The steps of using the comparative data to verify the established multi-field coupled three-dimensional numerical analysis model include: Obtain the engineering geological parameters of the frozen soil layer, obtain complete soil layer information through core drilling, use geophysical testing technology to obtain continuous spatial distribution information of the frozen soil layer, carry out in-situ testing of the frozen soil layer, and directly obtain parameters reflecting its engineering characteristics, including thermal conductivity, thermal diffusivity, water content, density and permeability; among them, geophysical testing technologies include high-density resistivity method and transient electromagnetic method. The high-density resistivity method applies current through the probe and measures the potential difference to obtain the apparent resistivity profile, and the transient electromagnetic method excites a pulse magnetic field through the coil and measures the secondary field electromotive force to obtain the apparent conductivity profile. The combination of the two methods can accurately invert the spatial distribution of the frozen soil layer; in-situ testing includes transient thermal probe method to determine thermal conductivity and thermal diffusivity, neutron and gamma ray method to determine water content and density, and water seepage test to determine permeability; Based on the acquired engineering geological parameters, a multi-field coupled three-dimensional numerical analysis model reflecting the complex coupling mechanism of frozen soil layers is established. Based on the continuous medium mechanics and porous medium theory, the thermal-hydraulic-mechanical coupling control equation is established. The constitutive model is selected, and the volume fraction average model is used to establish the functional relationship between the specific heat capacity and thermal conductivity and the temperature and volume ice content. The power function attenuation model is used to characterize the relationship between the permeability coefficient and the pore ice saturation. The linear expansion coefficient is estimated by the volume fraction weighted average formula. The hyperbolic model is used to describe the change of elastic modulus with temperature and ice content. The thermal-hydraulic-mechanical coupling control equation is solved by the finite element method. The fully implicit solution format is adopted, and the coupled solution of multiple field physical quantities in each time step is realized by the iterative algorithm. Indoor frost heave and thaw settlement characteristic tests and field monitoring tests were carried out to obtain multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer. The indoor frost heave and thaw settlement characteristic test was carried out using a high and low temperature environmental chamber and a frost heave deformation tester to analyze the frost heave deformation curves of frozen soil samples under different temperature gradients and water content conditions. The test temperature gradient was achieved through the refrigeration and heating system of the environmental chamber, the temperature was measured using a platinum resistance temperature sensor, the sample water content was determined by the drying method, the axial deformation was measured using a displacement sensor, and the radial deformation was measured using a circumferential strain gauge. The field monitoring test was carried out by deploying an automated monitoring system consisting of temperature, water content, displacement, and soil pressure sensors to collect real-time thermal-hydraulic-mechanical response data of the frozen soil layer under different construction conditions. The constitutive parameters of the established multi-field coupled three-dimensional numerical analysis model are calibrated using the indoor frost heave and thaw settlement characteristics test data, so that the multi-field coupled three-dimensional numerical analysis model can accurately reflect the thermal-hydraulic-mechanical characteristics of the frozen soil layer; on the basis of the parameter calibration, the field monitoring profile is selected, and the corresponding numerical calculation model is established to simulate and analyze the temporal and spatial distribution of the temperature field, moisture field, and displacement field of the field monitoring profile under actual working conditions, and compared and verified with the monitoring data; by comparing and analyzing the degree of agreement between the numerical simulation results and the monitoring data, the simulation accuracy and applicability of the established multi-field coupled three-dimensional numerical analysis model are quantitatively evaluated; the accuracy evaluation indicators of the multi-field coupled three-dimensional numerical analysis model include the average relative error, the root mean square error, and the goodness of fit coefficient, and the applicability evaluation indicators include the computational efficiency and convergence; the multi-field coupled three-dimensional numerical analysis model is adjusted and optimized according to the evaluation results.

2. The method according to claim 1, characterized in that Based on the acquired engineering geological parameters, a multi-field coupled three-dimensional numerical analysis model reflecting the complex coupling mechanism of frozen soil layer is established. Based on the continuous medium mechanics and porous medium theory, the thermal-hydraulic-mechanical coupling control equation is established. The constitutive model is selected, and the volume fraction average model is used to establish the functional relationship between the specific heat capacity and thermal conductivity and the temperature and volume ice content. The power function attenuation model is used to characterize the relationship between the permeability coefficient and the pore ice saturation. The linear expansion coefficient is estimated by the volume fraction weighted average formula. The hyperbolic model is used to describe the change of elastic modulus with temperature and ice content. The thermal-hydraulic-mechanical coupling control equation is solved by the finite element method. The fully implicit solution format is adopted. The steps of realizing the coupling solution of multi-field physical quantities in each time step by the iterative algorithm include: Based on the continuous medium mechanics and porous medium theory, combined with the law of conservation of energy, mass and momentum, the heat conduction equation, water transport equation and stress balance equation in the frozen soil layer are established respectively. The heat conduction equation takes into account the influence of heat conduction and phase change latent heat release, the water transport equation uses the linear seepage law to describe the pore water seepage, and the stress balance equation introduces temperature strain and frost heave strain. The three equations are coupled to form the control equation group of the thermal-hydraulic-mechanical coupling problem of the frozen soil layer. The established thermal-hydraulic-mechanical coupling control equations are numerically solved. First, the partial differential equations are discretized into finite element equations using the weighted residual method. Then, the time derivative terms are discretized using two-point back-difference, and the time integral parameters are introduced to form a fully implicit iterative format. Finally, an iterative algorithm is used to achieve the coupled solution of the temperature field, pore water pressure field, and displacement field in each time step. The stiffness matrix and load vector are updated through known physical quantities, and the unknown physical quantities are iteratively calculated until the convergence criteria of each physical quantity are met at the same time. After the iteration converges, the temperature, pore water pressure and displacement variables of the current time step are updated and saved, and the next time step is entered, and the coupled iterative solution process is repeated until the transient multi-field simulation within the preset time period is completed, and the spatiotemporal evolution law of the thermal-hydraulic-mechanical characteristic parameters of the frozen soil layer is obtained.

3. The method according to claim 1, characterized in that The verified multi-field coupled three-dimensional numerical analysis model is used to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and the steps of extracting the frost heave and thaw deformation time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and different depths are as follows: According to the freezing pipe arrangement scheme and freezing temperature curve actually adopted in the project, the corresponding cold source load is set in the multi-field coupled three-dimensional numerical analysis model. The freezing pipe adopts a combination of two-stage freezing and intermittent freezing. By performing transient heat transfer analysis, the three-dimensional temperature field distribution of the frozen soil layer at different freezing times is obtained, and the temperature cloud map of typical sections and typical time points is extracted. The circumferential and radial distribution characteristics of the soil temperature during the freezing process are analyzed. By comparing the temperature field evolution process at different freezing times, the key time nodes in the frozen soil development process are extracted, and the spatial position map of the zero-degree isotherm at different times is drawn to study the expansion and contraction process of the zero-degree isotherm over time. Based on the results of the evolution of the temperature field during the simulated freezing construction process, the evolution of the physical and mechanical parameters of frozen soil caused by temperature changes was considered, and a thermal-mechanical coupling analysis was performed. The thermal expansion theory was used to describe the frost heave deformation, and the freeze-thaw cycle damage model was used to describe the thaw settlement deformation. The temporal and spatial distribution law of the soil displacement field during the freezing process was obtained. The deformation characteristics of the three typical stages, namely the initial freezing period, the slow freezing period, and after the foundation pit excavation, were paid attention to. The surface uplift distribution curves at different times during the freezing process were extracted, and the uplift contour map was drawn. The spatial distribution characteristics of the surface frost heave deformation were analyzed. Combined with the uplift time-history curve, the uplift rate and uplift amount were quantitatively evaluated over time. The thaw settlement and deformation characteristics caused by foundation pit excavation and cavern excavation were analyzed, and the surface settlement contour map after excavation was drawn. The difference in surface deformation before and after excavation was compared. Combined with the time-history curves of the cavern vault settlement and the side wall inward displacement, the time development law of soil deformation below the excavation surface was studied. The deformation recovery process from the end of freezing to the complete melting of the soil was simulated, the thaw settlement development rate and duration were analyzed, and the surface settlement and cavern deformation contour maps at the end of thaw settlement were drawn. An orthogonal numerical test scheme was designed, and multiple freezing temperatures, freezing times, freezing pipe spacings and excavation depths of foundation pits were selected for numerical simulation calculations. The deformation cloud maps and time curves of frost heave and thaw settlement under different freezing temperature conditions were compared, and the influence of freezing temperature on deformation was quantitatively analyzed. The surface uplift and cavern deformation under different freezing time conditions were compared, and the influence of freezing time on frost heave and thaw settlement deformation was quantitatively analyzed. The frost heave deformation rate and maximum deformation under different freezing pipe spacing conditions were compared, and the influence of freezing pipe spacing on frost heave deformation was quantitatively analyzed. The duration and settlement of thaw settlement under different excavation depths were compared, and the influence of excavation depth on thaw settlement deformation was quantitatively analyzed. Based on the numerical simulation results, a quantitative relationship between construction process parameters and frost heave and thaw settlement deformation indicators was constructed.

4. The method according to claim 1, characterized in that: On the basis of simulation analysis, taking into account the construction schedule requirements, environmental conditions and structural safety requirements, the steps of determining the target threshold range of frozen soil layer frost heave deformation control and thaw settlement deformation control through multi-objective optimization algorithm include: The maximum ground uplift, maximum cavern deformation, total freezing period and total cost are determined as optimization targets, and the functional relationship between them and freezing temperature, freezing time, freezing pipe spacing and excavation depth is established. The maximum ground uplift and maximum cavern deformation are obtained by fitting the numerical simulation results, the total freezing period is calculated according to the freezing construction curve, and the total cost takes into account material costs, energy costs and labor costs. According to engineering experience and previous numerical simulation results, the value ranges of the four optimization variables, namely, freezing temperature, freezing time, freezing pipe spacing and excavation depth, are set; An improved non-dominated sorting genetic algorithm is used, with the optimization objective function as the fitness function and the value range of the optimization variable as the constraint condition. By setting a reasonable population size, crossover probability and mutation probability, a set of Pareto optimal solutions is obtained after multiple iterations. Each solution in the solution set is a balance between the optimization objectives that cannot be further improved, reflecting the trade-off between frost heave, thaw settlement deformation and total cost under different construction schemes. On the basis of the Pareto optimal solution set, according to the importance of deformation control and the tolerance of total cost in actual engineering, one or several control thresholds that meet the requirements are selected as recommended solutions; By using the engineering analogy method, the deformation control effects of the recommended control threshold scheme and existing similar projects were compared to evaluate the rationality of the threshold. The whole freezing construction process simulation was re-conducted under the guidance of the recommended control threshold scheme by using the numerical simulation method. According to the recommended control threshold scheme, three deformation warning levels are divided into mild, moderate and severe levels, and corresponding construction parameter adjustment measures, unloading measures and reinforcement measures are formulated. In the actual construction process, the early warning and emergency control mechanism are promptly activated through real-time comparison of deformation monitoring data with the recommended control threshold scheme.

5. The method according to claim 4, characterized in that The improved non-dominated sorting genetic algorithm is used, the optimization objective function is used as the fitness function, the value range of the optimization variable is used as the constraint condition, and by setting a reasonable population size, crossover probability and mutation probability, after multiple iterations, the steps of obtaining a set of Pareto optimal solutions include: The freezing temperature, freezing time, freezing pipe spacing and excavation depth are taken as optimization variables, the maximum surface uplift, the maximum cavern deformation, the total freezing period and the total cost are taken as optimization targets, and the initial population of size N is randomly generated by real number coding. Each individual in the population corresponds to a set of optimization variable value combinations, representing a freezing construction plan. Perform fast non-dominated sorting on the current population, and divide it into different non-dominated levels according to the Pareto dominance relationship between individuals. Individuals in the same level do not dominate each other. In each non-dominated level, calculate the crowding distance of individuals on each target, and obtain the total crowding degree of individuals to maintain population diversity. Parent individuals are selected from the current population through binary tournament selection, and simulated binary crossover and polynomial mutation operations are used to generate offspring with the same size as the parent. Crossover and mutation are designed to search and perturb optimization variables. Re-execute fast non-dominated sorting and crowding calculation on the merged parent and child generations, and select N optimal individuals from them based on non-dominated relations and crowding to generate a new generation of population; when the number of iterations reaches the preset maximum number of generations, the optimization is terminated, and the non-dominated solution set of the final population is output as the Pareto optimal solution set. Each solution in the solution set corresponds to a frozen construction plan that takes into account both deformation control and cost saving; Engineering test data and finite element analysis are used to establish a quantitative relationship between optimization objectives and optimization variables, and a fitness evaluation model is constructed; the optimal solution set can be displayed in real time in the decision support system, and the optimization plan can be recommended to guide the freezing construction, and the freezing parameters can be dynamically adjusted based on monitoring data to achieve real-time control of deformation.

6. The method according to claim 1, characterized in that The data processing and analysis system analyzes and fuses the monitoring data uploaded by the wireless sensor network in real time, extracts data parameters, and compares them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the steps of outputting corresponding construction control decision instructions include: Define unified data analysis rules for deployed wireless sensors, which include key fields of wireless sensor data collection and corresponding data processing algorithms. Use data analysis rules to analyze the original monitoring data uploaded by wireless sensors, extract key fields and form structured monitoring data. Multiple groups of structured monitoring data collected by similar wireless sensors are summarized, and a weighted average fusion algorithm is used to perform fusion calculation on each monitoring data. In the weighted average fusion algorithm, the weight of the wireless sensor is determined according to its measurement performance, and the sum of the weights of similar wireless sensors participating in the fusion calculation is 1; the fused monitoring data is written into the time series data table of the monitoring database together with the corresponding timestamp at the set time interval.

7. A construction control system for the frost heave and thaw settlement characteristics of frozen soil layers, used to implement the method described in any one of claims 1 to 6, characterized in that: include: The first unit is used to obtain multiple engineering geological characteristic parameters of the construction site; based on the obtained multiple engineering geological characteristic parameters, a multi-field coupled three-dimensional numerical analysis model simulating the heat conduction, water migration, and stress deformation of the frozen soil layer is established. By conducting indoor frost heave and thaw settlement characteristic tests and field monitoring tests, multiple sets of comparative data reflecting the thermal-hydraulic-mechanical constitutive relationship of the frozen soil layer are obtained, and the comparative data are used to verify the established multi-field coupled three-dimensional numerical analysis model; The second unit is used to use the verified multi-field coupling three-dimensional numerical analysis model to simulate and analyze the temporal and spatial evolution of the temperature field of the frozen soil layer during the construction process, and to extract the time history curves and spatial distribution cloud maps of the frozen soil layers at different construction stages and at different depths; on the basis of simulation analysis, comprehensively consider the construction progress requirements, environmental conditions, and structural safety requirements, and determine the target threshold range of the frozen soil layer frost heave deformation control and the target threshold range of the thaw deformation control through a multi-objective optimization algorithm; deploy a wireless sensor network in the frozen soil layer to monitor the temporal and spatial distribution data of the temperature field, moisture field, deformation field, and stress field of the frozen soil layer in real time, and upload the real-time monitoring data to the data processing and analysis system; The third unit is used to use the data processing and analysis system to perform real-time analysis and integration of the monitoring data uploaded by the wireless sensor network, extract data parameters, and compare them with the preset frost heave deformation control target threshold and thaw settlement deformation control target threshold in real time. When the monitoring parameters exceed the target threshold range, an early warning signal is triggered, and combined with reverse optimization analysis, the corresponding construction control decision instructions are output.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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