A method for predicting ground temperature dissipation based on shield butt curtain grouting-freezing

Abstract

The present application relates to geotechnical engineering, tunnel construction and underground engineering monitoring technical field, disclose a kind of based on shield docking curtain grouting-freezing stratum temperature dissipation prediction method, comprising: calculating maximum temperature rise and thermal influence radius determine temperature sensor layout position, and utilize temperature sensor to collect stratum temperature and draw monitoring temperature time curve, and divide stratum temperature change stage after grouting, determine stage conversion point time and grouting end time, and construct segmented temperature dissipation prediction equation;According to the error between maximum temperature rise and measured temperature rise, correct maximum temperature rise as temperature threshold value to determine target temperature, and match segmented temperature dissipation prediction equation to output stratum temperature dissipation time after grouting;Stratum temperature dissipation time after grouting is used to determine freezing start plan;According to the geological condition parameters of grouting area, output cross-project data migration library.The present application can realize the quantitative prediction of stratum temperature at any time in the future and the time required to reduce to target temperature.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering, tunnel construction and underground engineering monitoring technology, and more specifically, to a method for predicting ground temperature dissipation based on shield tunneling and curtain grouting-freezing. Background Technology

[0002] In recent years, with the development and utilization of deep underground space, cross-river and cross-sea tunnel projects have extended into deep and complex strata, and the length of shield tunnels has also shown a significant upward trend. Based on this, the underground docking technology for shield tunnels has become a new development trend. Controlling the stability of the surrounding rock during underground docking has become a key technical challenge. In underground docking construction of shield tunnels, advanced curtain grouting technology is often used to pre-reinforce the strata in the docking area. The grouting material (mainly cement-based grout) generates significant heat of hydration during solidification, causing a sharp rise in temperature in the reinforced area and surrounding strata, forming a localized high-temperature field. If this high-temperature field is not fully dissipated before subsequent freezing construction, it will seriously interfere with the development of the freezing front, significantly increase freezing energy consumption, prolong freezing time, and even lead to the failure of the freezing wall to close, endangering project safety.

[0003] Currently, the understanding of the formation temperature field after grouting in engineering practice is mostly at the qualitative or empirical judgment stage, lacking systematic prediction and quantitative analysis methods, and mainly faces the following technical bottlenecks:

[0004] (1) Lack of quantitative prediction of heat source: Existing technology only focuses on temperature monitoring after grouting, and cannot conduct a preliminary assessment of the total amount of hydration heat and temperature rise based on design parameters such as grout volume and grout ratio before grouting construction, resulting in insufficient construction risk pre-control.

[0005] (2) The monitoring methods are limited and the regularity analysis model is weak: intermittent temperature measurements are usually only taken at a few points, which cannot obtain the full picture of the spatiotemporal dynamic evolution of the temperature field. The analysis of the temperature dissipation law mostly adopts a single empirical model, which fails to distinguish the different physical mechanisms of the hydration heat release period and the natural heat dissipation period, resulting in low prediction accuracy and reliability.

[0006] (3) Lack of predictive ability and insufficient basis for decision-making: Due to the lack of reliable quantitative models, it is impossible to scientifically predict the time required for the formation temperature to drop to meet the requirements of freezing construction, resulting in the freezing start time arrangement being blind and affecting the safety and efficiency of the project.

[0007] (4) Disconnect between design and construction feedback: Temperature monitoring data failed to be effectively linked to grouting design parameters, and failed to provide dynamic feedback for correcting prediction models and guiding construction decisions, thus failing to form an intelligent closed loop.

[0008] Therefore, there is an urgent need for a systematic method that can predict heat sources from the design stage, accurately monitor them on-site, and reliably predict the formation temperature dissipation law after grouting based on physical mechanisms.

[0009] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0010] To address the problems in related technologies, this invention proposes a method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing, in order to overcome the aforementioned technical problems existing in the current related technologies.

[0011] Therefore, the specific technical solution adopted by the present invention is as follows:

[0012] In a first aspect, the present invention provides a method for predicting ground temperature dissipation based on shield tunneling and curtain grouting-freezing, comprising:

[0013] The maximum temperature rise and thermal influence radius are calculated based on the grouting parameters. The location of the temperature sensor is determined according to the thermal influence radius. The temperature sensor is used to collect the formation temperature and plot the monitoring temperature-time curve.

[0014] The temperature-time curves were used to divide the formation temperature change stages after grouting. Based on the stage division results, the stage transition point time and the grouting end time were determined, and a segmented temperature dissipation prediction equation was constructed.

[0015] Based on the error between the maximum temperature rise and the measured temperature rise, the maximum temperature rise is corrected as the temperature threshold to determine the target temperature, and the segmented temperature dissipation prediction equation is matched to output the formation temperature dissipation time after grouting.

[0016] A freezing start plan is determined based on the quantitative indicators of the formation temperature dissipation time and constraint conditions after grouting, which is used for the construction treatment of the grouting area after grouting.

[0017] Based on the geological condition parameters of the grouting area, a geological similarity is constructed, and a cross-project data migration library is output for predicting the dissipation of formation temperature in subsequent grouting.

[0018] Preferably, the maximum temperature rise and heat-affected radius are calculated based on grouting parameters, the location of temperature sensors is determined according to the heat-affected radius, and the formation temperature is collected using the temperature sensors to plot a monitoring temperature-time curve, including:

[0019] Obtain the cement mass ratio and grout density in the grout during the grouting process, and calculate the grout hydration heat per unit volume by combining the cement hydration heat and the water mass ratio in the grout.

[0020] The total heat of slurry hydration is calculated based on the effective heat release coefficient of hydration and the heat of slurry hydration per unit volume, and the maximum temperature rise is calculated by combining the total heat of slurry hydration with the volume of the heat-affected zone.

[0021] The thermal influence radius is calculated based on the equivalent radius of the grouting body, the thermal diffusivity of the formation, and the expected time of hydration heat release. The thermal influence radius is set as the distance threshold between the grouting hole and the temperature detection hole.

[0022] The spatial layout scheme of the temperature sensor is determined by using a distance threshold. Based on the spatial layout scheme, the temperature sensor is installed and activated to collect formation temperature data at a fixed frequency, thus obtaining the formation temperature data.

[0023] The formation temperature data was converted and adjusted to be time series data of the monitored temperature relative to the end of grouting, and a curve was plotted using the time series data to obtain the monitoring temperature time curve.

[0024] Preferably, the formation temperature change stages after grouting are divided using monitored temperature-time curves. Based on the stage division results, the stage transition point time and the grouting end time are determined, and a segmented temperature dissipation prediction equation is constructed, including:

[0025] The first and second derivatives of the monitoring temperature-time curve are calculated based on the differential vectors of the monitoring temperature and the monitoring time, and the stage division results of the monitoring temperature-time curve are determined based on the first and second derivatives.

[0026] Based on the stage division results, the stage transition point time and grouting end time are determined. The temperature dissipation prediction equations for each stage in the stage division results are output using the stage transition point time and grouting end time. The stage model parameter set is determined based on the temperature dissipation prediction equations. The stage transition point time includes the first transition point time and the second transition point time.

[0027] Preferably, the phase division results include the hydration heat release-dominant period, the heat release and dissipation transition period, and the natural heat dissipation-dominant period;

[0028] When the hydration heat release period is dominant, it means that the first derivative is greater than zero and the hydration heat release rate is greater than the formation heat dissipation rate.

[0029] When in the heat release and heat dissipation transition period, it means that the first derivative is less than zero, the absolute value of the first derivative is less than the target value, the second derivative meets the preset conditions, and the hydration heat release rate is less than the formation heat dissipation rate.

[0030] When the natural heat dissipation period is dominant, it means that the first derivative is greater than zero, the absolute value of the first derivative is greater than the target value, and the temperature drop rate is higher than the set value, thus ending the hydration heat release.

[0031] Temperature dissipation prediction equations include saturated growth prediction equations, linear decay prediction equations, and exponential decay prediction equations.

[0032] The saturated growth prediction equation indicates that the fitting time is greater than or equal to the grouting end time, and the fitting time is less than or equal to the first transition point time.

[0033] The linear decay prediction equation indicates that the fitting time is greater than the first transition point time and less than or equal to the second transition point time.

[0034] The exponential decay prediction equation indicates that the fitting time is greater than the time of the second transition point.

[0035] Preferably, based on the error between the maximum temperature rise and the measured temperature rise, the maximum temperature rise is corrected as a temperature threshold to determine the target temperature, and the segmented temperature dissipation prediction equation is matched to output the formation temperature dissipation time after grouting, including:

[0036] Compare the maximum temperature rise with the measured temperature rise, and compare the comparison result with the theoretical value to determine whether there is an error in the maximum temperature rise. If there is an error, the maximum temperature rise is corrected and the corrected maximum temperature rise is used as the target threshold. If there is no error, no correction is required.

[0037] The target temperature is given based on the target threshold, and the target temperature is matched with the temperature dissipation prediction equation to determine the time when the target temperature is reached. The freezing start time is determined based on the time when the target temperature is reached and the maximum margin time. At the same time, the time when the target temperature is reached is used as the formation temperature dissipation time after grouting.

[0038] Preferably, a freezing start plan is determined based on the formation temperature dissipation time and constraint condition quantitative indicators, which is used for the construction treatment of the grouting area after grouting, including:

[0039] Establish quantitative indicators for constraints that include freezing effect constraints, construction cost constraints, and safety risk constraints, determine the weight coefficients of the quantitative indicators for constraints, and establish a multi-objective optimization function in combination with the maximum structural deformation rate;

[0040] The freezing period and freezing brine temperature that affect construction costs and safety are determined based on the freezing start time, and a combination of decision variables is generated based on the freezing period and freezing brine temperature.

[0041] The comprehensive evaluation index of each decision variable combination is calculated using a multi-objective optimization function, and the decision variable combination corresponding to the largest comprehensive evaluation index is selected as the freezing start plan. The optimal freezing start time, optimal freezing period and optimal freezing brine temperature are determined for the construction treatment of the grouting area after grouting.

[0042] Preferably, a geological similarity is constructed based on the geological condition parameters of the grouting area, and a cross-project data migration library is output for predicting the dissipation of formation temperature in subsequent grouting, including:

[0043] Geological condition parameters such as the rock type, water content, porosity and thermal conductivity of the grouting area are obtained as evaluation indicators, and weight coefficients of the evaluation indicators are defined. Based on the evaluation indicators and weight coefficients, a geological similarity equation is constructed to describe the geological condition.

[0044] The geological similarity equation, the temperature dissipation prediction equation, and the freezing start-up plan are combined to form a cross-project data migration library. The geological condition parameters of the project to be predicted are matched with the cross-project data migration library, and the prediction results of the dissipation of formation temperature in the subsequent grouting of the project to be predicted are output.

[0045] Secondly, the present invention also provides a formation temperature dissipation prediction system based on shield tunneling curtain grouting-freezing, the system comprising:

[0046] The time curve plotting module is used to calculate the maximum temperature rise and heat-affected radius based on grouting parameters, determine the placement of temperature sensors based on the heat-affected radius, and use the temperature sensors to collect formation temperature and plot the monitoring temperature time curve.

[0047] The dissipation prediction module is used to divide the formation temperature change stages after grouting by monitoring the temperature-time curve, determine the stage transition point time and the grouting end time based on the stage division results, and construct the segmented temperature dissipation prediction equation.

[0048] The dissipation time output module is used to correct the maximum temperature rise as a temperature threshold based on the error between the maximum temperature rise and the measured temperature rise, determine the target temperature, and match the segmented temperature dissipation prediction equation to output the formation temperature dissipation time after grouting.

[0049] The start-up plan determination module is used to determine the freezing start-up plan based on the formation temperature dissipation time and constraint conditions after grouting and quantitative indicators, and is used for the construction treatment of the grouting area after grouting.

[0050] The migration library output module is used to construct geological similarity based on the geological condition parameters of the grouting area and output a cross-project data migration library for predicting the dissipation of formation temperature in subsequent grouting.

[0051] Thirdly, the present invention also proposes an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the above-described method when executed by the processor.

[0052] Fourthly, the present invention also provides a computer-readable storage medium on which a computer program is stored, and which, when executed by a processor, implements the above-described method.

[0053] The beneficial effects of this invention are as follows:

[0054] 1. This invention first assesses the formation temperature rise potential based on the grouting volume and grout mix ratio using a grout hydration heat model. Then, a distributed temperature monitoring sensor network is deployed in the grouting area and surrounding strata according to the pre-assessed temperature field influence range to continuously monitor the formation temperature field caused by cement hydration heat after grouting. Simultaneously, based on the collected temperature time series data, a three-stage temperature dissipation identification and piecewise fitting modeling method is proposed. Combined with a hydration heat release physical model, a coupled inversion analysis is performed to accurately identify the dominant stage of hydration heat and its endpoint. The dominant stage of natural heat dissipation is modeled, and by establishing piecewise temperature dissipation equations, quantitative predictions of the formation temperature at any future time and the time required to drop to the target temperature are achieved. This constitutes a complete technical closed loop from heat source design prediction to on-site monitoring feedback and dissipation law prediction, providing direct and reliable scientific basis for key parameter decisions in subsequent freezing method construction.

[0055] 2. This invention introduces a hydration heat model based on slurry volume, which can assess the risk of temperature rise based on design parameters before construction, realizing the transformation from passive monitoring to proactive prediction. It provides a scientific basis for monitoring scheme design and construction early warning. Furthermore, by establishing an optimization model under multiple constraints, it achieves the optimal combination decision of freezing start time and freezing parameters, effectively controlling construction costs and improving the overall benefits of the project while ensuring freezing effect and construction safety, thus breaking through the limitations of single temperature index decision-making.

[0056] 3. This invention proposes a three-stage identification and piecewise fitting method, and adopts a mechanism-guided model that better reflects the actual physical process. This significantly improves the accuracy and reliability of predicting the long-term dissipation trend of formation temperature. At the same time, it connects the entire chain of design parameter pre-evaluation, on-site dynamic monitoring, model coupling inversion, accurate prediction, construction decision-making, construction parameter optimization, and cross-project reuse. It provides a complete intelligent decision support system for grouting-freezing connection construction. By establishing an optimization model under multiple constraints, it achieves the optimal combination decision of freezing start time and freezing parameters, effectively controlling construction costs and improving the overall benefits of the project while ensuring freezing effect and construction safety. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart of a method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing, according to an embodiment of the present invention.

[0059] Figure 2 This is a schematic diagram of a formation temperature dissipation prediction system based on shield tunneling curtain grouting-freezing, according to an embodiment of the present invention.

[0060] Figure 3 This is a schematic diagram of the hardware operating environment involved in the embodiments of the present invention;

[0061] Figure 4 This is a temperature-time curve after grouting is completed in the first stage of a method for predicting the dissipation of ground temperature based on shield tunneling curtain grouting-freezing, according to an embodiment of the present invention.

[0062] Figure 5 This is a temperature-time curve after grouting is completed in the second stage of a ground temperature dissipation prediction method based on shield tunneling curtain grouting-freezing according to an embodiment of the present invention.

[0063] Figure 6 This is a temperature-time curve after grouting is completed in the third stage of a method for predicting the dissipation of ground temperature based on shield tunneling curtain grouting-freezing, according to an embodiment of the present invention.

[0064] Figure 7 This is a grouting and monitoring borehole design plan in a method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing, according to an embodiment of the present invention.

[0065] Figure 8 This is a cross-sectional view of the temperature sensor location in a formation temperature dissipation prediction method based on shield tunneling curtain grouting-freezing according to an embodiment of the present invention.

[0066] Figure 9 This is a temperature-time curve of actual monitoring by a temperature sensor in a method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing, according to an embodiment of the present invention.

[0067] In the picture:

[0068] 1. Time curve plotting module; 2. Dissipation prediction construction module; 3. Dissipation time output module; 4. Startup plan determination module; 5. Migration library output module. Detailed Implementation

[0069] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0070] According to an embodiment of the present invention, a method for predicting ground temperature dissipation based on shield tunneling and curtain grouting-freezing is provided.

[0071] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, the method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing according to an embodiment of the present invention includes:

[0072] Step S1: Calculate the maximum temperature rise and thermal influence radius based on the grouting parameters, determine the location of the temperature sensor based on the thermal influence radius, and use the temperature sensor to collect the formation temperature and plot the monitoring temperature-time curve.

[0073] In one embodiment, the process of calculating the maximum temperature rise and heat-affected radius based on grouting parameters, determining the location of temperature sensors based on the heat-affected radius, and using the temperature sensors to collect formation temperature data to plot a monitoring temperature-time curve includes:

[0074] Obtain the cement mass ratio and grout density in the grout during the grouting process, and calculate the grout hydration heat per unit volume by combining the cement hydration heat and the water mass ratio in the grout.

[0075] The total heat of slurry hydration is calculated based on the effective heat release coefficient of hydration and the heat of slurry hydration per unit volume, and the maximum temperature rise is calculated by combining the total heat of slurry hydration with the volume of the heat-affected zone.

[0076] The thermal influence radius is calculated based on the equivalent radius of the grouting body, the thermal diffusivity of the formation, and the expected time of hydration heat release. The thermal influence radius is set as the distance threshold between the grouting hole and the temperature detection hole.

[0077] The spatial layout scheme of the temperature sensor is determined by using a distance threshold. Based on the spatial layout scheme, the temperature sensor is installed and activated to collect formation temperature data at a fixed frequency, thus obtaining the formation temperature data.

[0078] The formation temperature data was converted and adjusted to be time series data of the monitored temperature relative to the end of grouting, and a curve was plotted using the time series data to obtain the monitoring temperature time curve.

[0079] It should be explained that in the process of pre-evaluating the hydration heat source intensity based on grouting parameters, the calculation of the total hydration heat is constrained by the grout mix ratio (water-cement ratio), grouting volume, and heat loss coefficient, specifically satisfying the following formula:

[0080] ;

[0081] ;

[0082] Among them: Q v Q represents the heat of hydration per unit volume of slurry. t ρ represents the total heat of hydration of the injected slurry. g H represents the density of the slurry. c The final heat of hydration per unit mass of cement is represented by W, the mass percentage of water in the grout is represented by C, the mass percentage of cement in the grout is represented by η, and the effective heat of hydration release coefficient is represented by η, which ranges from 0.8 to 0.9 and is used to account for heat loss under actual formation conditions.

[0083] In the process of estimating the temperature rise and the radius of thermal influence, the grouting body is set as the columnar heat source. Based on the average heat capacity of the formation and the volume of the thermally affected zone, the maximum temperature rise ΔT is initially calculated. max and thermally affected radius R t :

[0084] ;

[0085] ;

[0086] Where, ΔT max R represents the maximum temperature rise. t B represents the radius of thermal influence. s V represents the average heat capacity of the formation. t R0 represents the volume of the heat-affected zone, R0 represents the equivalent radius of the grouting body (range 2-3 m), α represents the formation thermal diffusivity (range 0.9-1), and t0 represents the thermal diffusivity of the formation. h This indicates the expected duration of the main heat release period of hydration, ranging from 6 to 7 days.

[0087] like Figures 4 to 9 As shown, the temperature field significantly affects the radius R. t Based on this, a three-dimensional spatial layout scheme for the temperature sensors was designed, with the distance between the grouting holes and the temperature monitoring holes designed to be R. t To ensure that the temperature sensors are within the range affected by the heat of slurry hydration, the spacing of the temperature sensors within a single hole is constrained by the following formula:

[0088] ;

[0089] Where, ds This indicates the maximum permissible spacing in the sensing and monitoring direction.

[0090] Before grouting begins, all temperature sensors are activated to continuously collect formation temperature data at a fixed frequency. This data is then transmitted in real-time to the data center via a data transmission system, generating a time-temperature curve. The monitored temperature-time series data is then converted and adjusted to monitored temperature-time series data after grouting is completed. Preliminary stage identification is performed based on the curve's trend and slope changes, and segmented monitored temperature-time curves after grouting are plotted. Figures 4 to 6 As shown.

[0091] Step S2: Use the temperature-time curve to divide the formation temperature change stages after grouting, determine the stage transition point time and grouting end time based on the stage division results, and construct a segmented temperature dissipation prediction equation.

[0092] In one embodiment, the formation temperature change stages after grouting are divided using monitored temperature-time curves. Based on the stage division results, the stage transition point time and the grouting end time are determined, and a segmented temperature dissipation prediction equation is constructed, including:

[0093] The first and second derivatives of the monitoring temperature-time curve are calculated based on the differential vectors of the monitoring temperature and the monitoring time, and the stage division results of the monitoring temperature-time curve are determined based on the first and second derivatives.

[0094] Based on the stage division results, the stage transition point time and grouting end time are determined. The temperature dissipation prediction equations for each stage in the stage division results are output using the stage transition point time and grouting end time. The stage model parameter set is determined based on the temperature dissipation prediction equations. The stage transition point time includes the first transition point time and the second transition point time.

[0095] In one embodiment, the phase division results include a hydration heat release-dominant period, a heat release and dissipation transition period, and a natural heat dissipation-dominant period;

[0096] When the hydration heat release period is dominant, it means that the first derivative is greater than zero and the hydration heat release rate is greater than the formation heat dissipation rate; when the heat release and heat dissipation transition period is dominant, it means that the first derivative is less than zero, the absolute value of the first derivative is less than the target value, the second derivative meets the preset conditions, and the hydration heat release rate is less than the formation heat dissipation rate; when the natural heat dissipation period is dominant, it means that the first derivative is greater than zero, the absolute value of the first derivative is greater than the target value, and the temperature drop rate is higher than the set value, thus ending the hydration heat release period.

[0097] The temperature dissipation prediction equation includes a saturated growth prediction equation, a linear decay prediction equation, and an exponential decay prediction equation. The saturated growth prediction equation indicates that the fitting time is greater than or equal to the grouting end time and less than or equal to the first transition point time. The linear decay prediction equation indicates that the fitting time is greater than the first transition point time and less than or equal to the second transition point time. The exponential decay prediction equation indicates that the fitting time is greater than the second transition point time.

[0098] Based on the temperature-time curve T(t) and the first derivative A T (t) = dT / dt and the second derivative S T (t) = d 2 T / dt 2 The process is divided into three stages, where dT represents the differential variable of the monitored temperature and dt represents the differential variable of the monitored time; when A T (t)≥0, the temperature continues to rise, the hydration heat release rate is greater than the formation heat dissipation rate, which is divided into the first stage, namely the hydration heat release-dominated period; when A T (t) < 0, but |A T (t) | Smaller (less than the target value), S T (t) approaches zero (meeting preset conditions), the temperature slowly decreases, and the hydration heat release rate is slightly less than the formation heat dissipation rate, which is divided into the second stage, namely the heat release-heat dissipation transition period; when A T (t) < 0 and |A T (t)| increases significantly, temperature drops rapidly, hydration heat release basically ends, and it is divided into the third stage, namely the natural heat dissipation period.

[0099] Based on the three-stage division, the stage transition time t1 and t2, namely the first transition time and the second transition time, and the grouting end time t0 are determined.

[0100] Where T(t) represents the temperature as a function of time, t represents time, and A T (t) represents the first derivative of temperature with respect to time, S T (t) represents the second derivative of temperature with respect to time.

[0101] Step S3: Based on the error between the maximum temperature rise and the measured temperature rise, correct the maximum temperature rise as the temperature threshold to determine the target temperature, and match the segmented temperature dissipation prediction equation to output the formation temperature dissipation time after grouting.

[0102] In one embodiment, based on the error between the maximum temperature rise and the measured temperature rise, the maximum temperature rise is corrected as a temperature threshold to determine the target temperature, and the segmented temperature dissipation prediction equation is matched to output the formation temperature dissipation time after grouting, including:

[0103] Compare the maximum temperature rise with the measured temperature rise, and compare the comparison result with the theoretical value to determine whether there is an error in the maximum temperature rise. If there is an error, the maximum temperature rise is corrected and the corrected maximum temperature rise is used as the target threshold. If there is no error, no correction is required.

[0104] The target temperature is given based on the target threshold, and the target temperature is matched with the temperature dissipation prediction equation to determine the time when the target temperature is reached. The freezing start time is determined based on the time when the target temperature is reached and the maximum margin time. At the same time, the time when the target temperature is reached is used as the formation temperature dissipation time after grouting.

[0105] The first stage of fitting, i.e., t0≤t≤t1, adopts a saturated growth model, and the functional relationship specifically satisfies the following requirements:

[0106] ;

[0107] Where T(t0) represents the temperature monitored at the end of grouting, ΔT m This represents the measured peak temperature rise, where e is the natural constant and a is the hydration heat growth coefficient.

[0108] The second stage of fitting, i.e., t1 < t ≤ t2, uses a linear decay model, and the functional relationship specifically satisfies the following requirements:

[0109] ;

[0110] Where t1 represents the time of the segmentation point between the first and second stages, and k represents the slope of the curve in the second stage.

[0111] The third-stage fitting, i.e., t > t2, uses an exponential decay model, and the functional relationship specifically satisfies the following requirements:

[0112] ;

[0113] Among them, T d t1 represents the initial formation temperature, t2 represents the time of the segmentation point between the second and third stages, and b is the formation temperature decay coefficient.

[0114] The complete piecewise prediction equation specifically satisfies the following constraints:

[0115] ;

[0116] Determine the parameter set Nold={a, k, b} for the three-stage model.

[0117] Compared with the theoretically predicted peak temperature rise ΔT max The measured peak temperature rise ΔT represents the actual temperature rise. m When |ΔT max -ΔT m | / min{ΔT maxΔT m If the theoretical value is greater than or equal to 0.3, then the theoretical prediction has an error and the theoretical prediction maximum temperature rise function needs to be corrected, while also meeting the following requirements:

[0118] ;

[0119] ;

[0120] Where μ represents the correction factor for the predicted maximum temperature rise. This indicates the maximum predicted temperature rise after correction. If it is less than 0.3, no correction is needed.

[0121] Given target temperature T target Since the target temperature is mostly reflected in the third stage, the time t to achieve the target temperature is... target The following constraints must be met:

[0122] ;

[0123] Among them, T target t represents the target temperature. target Indicates the time required to reach the target temperature.

[0124] The subsequent freeze-start time window must satisfy the following constraints:

[0125] ;

[0126] Where, Δt safe This indicates the safety margin time, ranging from 3 to 5 days, Δt. max This indicates the maximum margin of time, ranging from 5 to 10 days.

[0127] Step S4: Determine the freezing start plan based on the temperature dissipation time of the formation after grouting and the quantitative indicators of the constraint conditions, which will be used for the construction treatment of the grouting area after grouting.

[0128] In one embodiment, a freezing start plan is determined based on the formation temperature dissipation time after grouting and quantitative indicators of constraint conditions. This plan is used for construction treatment of the grouting area after grouting, including:

[0129] Establish quantitative indicators for constraints that include freezing effect constraints, construction cost constraints, and safety risk constraints, determine the weight coefficients of the quantitative indicators for constraints, and establish a multi-objective optimization function in combination with the maximum structural deformation rate;

[0130] The freezing period and freezing brine temperature that affect construction costs and safety are determined based on the freezing start time, and a combination of decision variables is generated based on the freezing period and freezing brine temperature.

[0131] The comprehensive evaluation index of each decision variable combination is calculated using a multi-objective optimization function, and the decision variable combination corresponding to the largest comprehensive evaluation index is selected as the freezing start plan. The optimal freezing start time, optimal freezing period and optimal freezing brine temperature are determined for the construction treatment of the grouting area after grouting.

[0132] Define quantitative indicators for the constraints, including freezing effect constraints, construction cost constraints, and safety risk constraints. Establish a multi-objective optimization function with the objectives of achieving optimal freezing effect, lowest construction cost, and lowest safety risk, specifically satisfying the following requirements:

[0133] ;

[0134] Where: F is the comprehensive evaluation index; ω1, ω2, and ω3 are the weighting coefficients for freezing effect, construction cost, and safety risk, respectively, with ω1 ranging from 0.4 to 0.6, ω2 ranging from 0.2 to 0.3, and ω3 ranging from 0.2 to 0.3; β is the compliance rate of the average thickness of the frozen wall, ranging from 0.9 to 1; C is the cost of freezing strata per unit volume. max V represents the maximum allowable unit volume frozen cost for the project; V is the frozen structural deformation rate during the construction period. max This represents the maximum allowable structural deformation rate during the frozen construction period.

[0135] Based on the known freeze startup time window The freezing period O and freezing brine temperature P, which affect construction costs and safety, were initially determined. The freezing period O, determined based on freezing design or experience, ranges from 60 to 90 days. The freezing brine temperature P ranges from -25 to -15℃. The optimal combination scheme was then determined: by traversing the combinations of decision variables using enumeration or a genetic algorithm, the comprehensive evaluation index F of each combination was calculated, and the combination with the largest F was selected as the optimal scheme. The best freezing start time t was then output. s Optimal freezing period O s and the optimal freezing brine temperature P s .

[0136] Step S5: Construct a geological similarity based on the geological condition parameters of the grouting area, and output a cross-project data migration library for predicting the dissipation of formation temperature in subsequent grouting.

[0137] In one embodiment, a geological similarity is constructed based on the geological condition parameters of the grouting area, and a cross-project data migration library is output for predicting the dissipation of formation temperature in subsequent grouting, including:

[0138] Geological condition parameters such as the rock type, water content, porosity and thermal conductivity of the grouting area are obtained as evaluation indicators, and weight coefficients of the evaluation indicators are defined. Based on the evaluation indicators and weight coefficients, a geological similarity equation is constructed to describe the geological condition.

[0139] The geological similarity equation, the temperature dissipation prediction equation, and the freezing start-up plan are combined to form a cross-project data migration library. The geological condition parameters of the project to be predicted are matched with the cross-project data migration library, and the prediction results of the dissipation of formation temperature in the subsequent grouting of the project to be predicted are output.

[0140] A geological condition similarity evaluation index system is established, selecting four core indicators: stratigraphic lithology type, stratigraphic water content w, stratigraphic porosity n, and stratigraphic thermal conductivity λ, to construct the geological similarity Y, which specifically meets the following constraints:

[0141] ;

[0142] Where Y represents the geological similarity between the project to be predicted and this embodiment, and x 11 x 12 x 13 x 14 This indicates the lithological type, water content, porosity, and thermal conductivity of the formation to be predicted for the project; x 01 x 02 x 03 x 04 γ1 represents the lithological type, formation water content, formation porosity, and formation thermal conductivity of the formation in this project; γ2, γ3, and γ4 represent the weights of the lithological type, formation water content, formation porosity, and formation thermal conductivity, respectively, with γ1 ∈ [0.3-0.4], γ2 ∈ [0.2-0.3], γ3 ∈ [0.2-0.3], and γ4 ∈ [0.1-0.2].

[0143] When Y ≥ 0.7, the geological conditions of the project to be predicted are considered highly similar to those of this project. The segmented temperature dissipation model parameters (first-stage saturated growth model coefficients, second-stage linear decay model slope, and third-stage exponential decay model coefficients) and the optimal combination of parameters for freezing construction are extracted for this project. When Y < 0.7, the model parameters are fine-tuned to meet the following constraints:

[0144] ;

[0145] Where, N new N represents the set of parameters for the optimized three-stage model. new = (anew, knowu, bnew), where δ is the parameter fine-tuning coefficient, with a value range of 0.1-0.3.

[0146] like Figure 2 As shown, according to another embodiment of the present invention, a formation temperature dissipation prediction system based on shield tunneling curtain grouting-freezing is also provided, the system comprising:

[0147] The time curve plotting module 1 is used to calculate the maximum temperature rise and heat-affected radius based on grouting parameters, determine the location of temperature sensors based on the heat-affected radius, and use the temperature sensors to collect formation temperature and plot the monitoring temperature time curve.

[0148] The dissipation prediction construction module 2 is used to divide the formation temperature change stages after grouting by monitoring the temperature-time curve, determine the stage transition point time and grouting end time based on the stage division results, and construct the segmented temperature dissipation prediction equation.

[0149] The dissipation time output module 3 is used to correct the maximum temperature rise as a temperature threshold based on the error between the maximum temperature rise and the measured temperature rise, determine the target temperature, and match the segmented temperature dissipation prediction equation to output the formation temperature dissipation time after grouting.

[0150] The start-up plan determination module 4 is used to determine the freezing start-up plan based on the formation temperature dissipation time and constraint conditions after grouting and quantitative indicators, and is used for the construction treatment of the grouting area after grouting.

[0151] The migration library output module 5 is used to construct geological similarity based on the geological condition parameters of the grouting area and output a cross-project data migration library for predicting the dissipation of formation temperature in subsequent grouting.

[0152] Furthermore, the present invention also provides an electronic device. For example... Figure 3 The diagram illustrates the hardware operating environment of an electronic device, which may include: a processor (e.g., CPU), memory, a user interface, a network interface, and a communication bus. The communication bus is used to enable communication between components. The user interface may include a display screen and an input unit such as a keyboard; optionally, the user interface may also include a standard wired interface or a wireless interface. The network interface may optionally include a standard wired interface or a wireless interface. The memory may be high-speed RAM or stable non-volatile memory, such as disk storage. Alternatively, the memory may be a storage device independent of the aforementioned processor.

[0153] Those skilled in the art will understand that Figure 3 The electronic devices shown do not constitute a limitation on electronic devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0154] like Figure 3As shown, a memory, as a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and device management programs. The operating system is a program that manages and controls the hardware and software resources of electronic devices, supporting the operation of electronic devices and other software or programs. Figure 3 In the electronic device shown, the user interface is mainly used to connect to the terminal and communicate with the terminal, such as receiving user signaling data sent by the terminal; the network interface is mainly used to communicate with the backend server; the processor can be used to call the program stored in the memory and execute the steps of the method or system described above.

[0155] Furthermore, the present invention also proposes a computer-readable storage medium storing a device management program, which, when executed by a processor, implements the steps of the method or system described above.

[0156] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as those of the above-described methods or systems, and will not be repeated here. Furthermore, to achieve the above objectives, the present invention also provides a computer program product, comprising: a computer program, which, when executed by a processor, implements the steps of the methods or systems described above.

[0157] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0158] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting ground temperature dissipation based on shield tunneling and curtain grouting-freezing, characterized in that, include: The maximum temperature rise and thermal influence radius are calculated based on the grouting parameters. The location of the temperature sensor is determined according to the thermal influence radius. The temperature sensor is used to collect the formation temperature and plot the monitoring temperature-time curve. The temperature-time curves were used to divide the formation temperature change stages after grouting. Based on the stage division results, the stage transition point time and the grouting end time were determined, and a segmented temperature dissipation prediction equation was constructed. Based on the error between the maximum temperature rise and the measured temperature rise, the maximum temperature rise is corrected as the temperature threshold to determine the target temperature, and the segmented temperature dissipation prediction equation is matched to output the formation temperature dissipation time after grouting. A freezing start plan is determined based on the quantitative indicators of the formation temperature dissipation time and constraint conditions after grouting, which is used for the construction treatment of the grouting area after grouting. Based on the geological condition parameters of the grouting area, a geological similarity is constructed, and a cross-project data migration library is output for predicting the dissipation of formation temperature in subsequent grouting.

2. The method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing as described in claim 1, characterized in that, The process of calculating the maximum temperature rise and thermal influence radius based on grouting parameters, determining the placement of temperature sensors based on the thermal influence radius, and using temperature sensors to collect formation temperature data and plot a monitoring temperature-time curve includes: Obtain the cement mass ratio and grout density in the grout during the grouting process, and calculate the grout hydration heat per unit volume by combining the cement hydration heat and the water mass ratio in the grout. The total heat of slurry hydration is calculated based on the effective heat release coefficient of hydration and the heat of slurry hydration per unit volume, and the maximum temperature rise is calculated by combining the total heat of slurry hydration with the volume of the heat-affected zone. The thermal influence radius is calculated based on the equivalent radius of the grouting body, the thermal diffusivity of the formation, and the expected time of hydration heat release. The thermal influence radius is set as the distance threshold between the grouting hole and the temperature detection hole. The spatial layout scheme of the temperature sensor is determined by using a distance threshold. Based on the spatial layout scheme, the temperature sensor is installed and activated to collect formation temperature data at a fixed frequency, thus obtaining the formation temperature data. The formation temperature data was converted and adjusted to be time series data of the monitored temperature relative to the end of grouting, and a curve was plotted using the time series data to obtain the monitoring temperature time curve.

3. The method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing as described in claim 1, characterized in that, The process of dividing the formation temperature change stages after grouting using monitored temperature-time curves, determining the stage transition point time and grouting end time based on the stage division results, and constructing a segmented temperature dissipation prediction equation includes: The first and second derivatives of the monitoring temperature-time curve are calculated based on the differential vectors of the monitoring temperature and the monitoring time, and the stage division results of the monitoring temperature-time curve are determined based on the first and second derivatives. Based on the stage division results, the stage transition point time and the grouting end time are determined. The temperature dissipation prediction equation for each stage in the stage division results is output using the stage transition point time and the grouting end time. The stage model parameter set is determined based on the temperature dissipation prediction equation. The stage transition point time includes the first transition point time and the second transition point time.

4. The method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing as described in claim 3, characterized in that, The phase division results include the hydration heat release-dominated period, the heat release and dissipation transition period, and the natural heat dissipation-dominated period; When the hydration heat release period is dominant, it means that the first derivative is greater than zero and the hydration heat release rate is greater than the formation heat dissipation rate. When in the heat release and heat dissipation transition period, it means that the first derivative is less than zero, the absolute value of the first derivative is less than the target value, the second derivative meets the preset conditions, and the hydration heat release rate is less than the formation heat dissipation rate. When the natural heat dissipation period is in full swing, it means that the first derivative is greater than zero, the absolute value of the first derivative is greater than the target value, and the temperature drop rate is higher than the set value, thus ending the hydration heat release. The temperature dissipation prediction equation includes a saturated growth prediction equation, a linear decay prediction equation, and an exponential decay prediction equation. The saturated growth prediction equation indicates that the fitting time is greater than or equal to the grouting end time, and the fitting time is less than or equal to the first transition point time. The linear decay prediction equation indicates that the fitting time is greater than the first transition point time and less than or equal to the second transition point time. The exponential decay prediction equation indicates that the fitting time is greater than the second transition point time.

5. The method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing as described in claim 1, characterized in that, The process of correcting the maximum temperature rise as a temperature threshold based on the error between the maximum and measured temperature rise to determine the target temperature, and matching the segmented temperature dissipation prediction equation to output the formation temperature dissipation time after grouting, includes: Compare the maximum temperature rise with the measured temperature rise, and compare the comparison result with the theoretical value to determine whether there is an error in the maximum temperature rise. If there is an error, the maximum temperature rise is corrected and the corrected maximum temperature rise is used as the target threshold. If there is no error, no correction is required. The target temperature is given based on the target threshold, and the target temperature is matched with the temperature dissipation prediction equation to determine the time when the target temperature is reached. The freezing start time is determined based on the time when the target temperature is reached and the maximum margin time. At the same time, the time when the target temperature is reached is used as the formation temperature dissipation time after grouting.

6. The method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing as described in claim 1, characterized in that, The freezing start plan, determined based on the quantitative indicators of formation temperature dissipation time and constraint conditions after grouting, is used for construction treatment of the grouting area after grouting, including: Establish quantitative indicators for constraints that include freezing effect constraints, construction cost constraints, and safety risk constraints, determine the weight coefficients of the quantitative indicators for constraints, and establish a multi-objective optimization function in combination with the maximum structural deformation rate; The freezing period and freezing brine temperature that affect construction costs and safety are determined based on the freezing start time, and a combination of decision variables is generated based on the freezing period and freezing brine temperature. The comprehensive evaluation index of each decision variable combination is calculated using a multi-objective optimization function, and the decision variable combination corresponding to the largest comprehensive evaluation index is selected as the freezing start plan. The optimal freezing start time, optimal freezing period and optimal freezing brine temperature are determined for the construction treatment of the grouting area after grouting.

7. The method for predicting ground temperature dissipation based on shield tunneling curtain grouting-freezing as described in claim 1, characterized in that, The process of constructing a geological similarity database based on the geological condition parameters of the grouting area and outputting a cross-project data migration library for predicting the dissipation of formation temperature in subsequent grouting includes: Geological condition parameters such as the rock type, water content, porosity and thermal conductivity of the grouting area are obtained as evaluation indicators, and weight coefficients of the evaluation indicators are defined. Based on the evaluation indicators and weight coefficients, a geological similarity equation is constructed to describe the geological condition. The geological similarity equation, the temperature dissipation prediction equation, and the freezing start-up plan are combined to form a cross-project data migration library. The geological condition parameters of the project to be predicted are matched with the cross-project data migration library, and the prediction results of the dissipation of formation temperature in the subsequent grouting of the project to be predicted are output.

8. A formation temperature dissipation prediction system based on shield tunneling curtain grouting-freezing, used to implement the formation temperature dissipation prediction method based on shield tunneling curtain grouting-freezing as described in any one of claims 1-7, characterized in that, The system includes: The time curve plotting module is used to calculate the maximum temperature rise and heat-affected radius based on grouting parameters, determine the placement of temperature sensors based on the heat-affected radius, and use the temperature sensors to collect formation temperature and plot the monitoring temperature time curve. The dissipation prediction module is used to divide the formation temperature change stages after grouting by monitoring the temperature-time curve, determine the stage transition point time and the grouting end time based on the stage division results, and construct the segmented temperature dissipation prediction equation. The dissipation time output module is used to correct the maximum temperature rise as a temperature threshold based on the error between the maximum temperature rise and the measured temperature rise, determine the target temperature, and match the segmented temperature dissipation prediction equation to output the formation temperature dissipation time after grouting. The start-up plan determination module is used to determine the freezing start-up plan based on the formation temperature dissipation time and constraint conditions after grouting and quantitative indicators, and is used for the construction treatment of the grouting area after grouting. The migration library output module is used to construct geological similarity based on the geological condition parameters of the grouting area and output a cross-project data migration library for predicting the dissipation of formation temperature in subsequent grouting.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the steps of the method for predicting ground temperature dissipation based on shield-tunnel docking curtain grouting-freezing as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for predicting ground temperature dissipation based on shield-tunnel docking curtain grouting-freezing as described in any one of claims 1 to 7.

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