Layered wringing consolidation method for soft soil foundation based on step-by-step energy increasing
By constructing a layered water-squeezing consolidation digital twin model that matches the actual soft soil foundation at a 1:1 scale, and combining adaptive mapping algorithms and real-time control technology, the problems of inaccurate parameters and difficulty in quantifying risks in traditional soft soil foundation treatment were solved, achieving efficient and safe construction results.
Patent Information
- Application Number
- CN202511393256.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-27
- Publication Date
- 2026-01-13
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional soft soil foundation treatment methods lack accurate simulation and real-time monitoring, resulting in unscientific construction parameters, low construction efficiency, uneven consolidation quality, and difficulty in quantifying construction risks, thus increasing potential safety hazards in the project.
A layered water-squeezing consolidation digital twin model combined with a layered parameter adaptive mapping algorithm is adopted to achieve accurate matching between survey data and virtual model. Real-time control is achieved through a real-time data mapping module and a deviation feedback correction algorithm. Combined with a spatial optimization model and a multi-factor coupled risk index calculation model, construction parameters and risk assessment are optimized.
It significantly improved the accuracy of construction parameters and construction efficiency, reduced construction costs and safety risks, ensured consolidation quality and construction safety, and reduced the problems of soil consolidation degree differences and parameter adjustment lag.
Smart Images
Figure CN121327933A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of soft soil foundation treatment technology, specifically to a layered water squeezing and consolidation method for soft soil foundations based on progressive energy enhancement. Background Technology
[0002] With the continuous advancement of infrastructure construction, soft soil foundation treatment has become a key link in ensuring project quality and safety. Due to its characteristics such as high water content, low permeability, low strength and high compressibility, soft soil presents a huge challenge to engineering construction. In particular, in projects such as roads, bridges, airports and large buildings, improper treatment of soft soil foundations can easily lead to settlement, cracking or even instability, seriously affecting the service life and safety of the project.
[0003] Traditional soft soil foundation treatment methods, such as surcharge preloading, vacuum preloading, and chemical reinforcement, while improving foundation performance to some extent, still have many limitations. First, traditional methods often rely on empirical or semi-empirical formulas for parameter design, lacking accurate simulation of the complex mechanical behavior of the foundation, resulting in insufficient scientificity and rationality of construction parameters. Second, the lack of real-time monitoring and dynamic adjustment mechanisms during construction makes it difficult to detect and correct construction deviations in a timely manner, affecting construction efficiency and consolidation quality. In addition, traditional methods rely heavily on single-factor judgments in risk assessment and early warning, making it difficult to comprehensively and accurately quantify construction risks, increasing potential safety hazards. Finally, when treating large areas of soft soil foundations, traditional methods often use a uniform arrangement of water squeezing points, failing to fully consider the differences in soil layer characteristics, leading to resource waste and uneven consolidation effects.
[0004] In view of the shortcomings of traditional soft soil foundation treatment technologies, it is particularly important that this invention develops a layered water squeezing consolidation method for soft soil foundations based on progressive energy enhancement. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a layered water squeezing consolidation method for soft soil foundations based on progressive energy enhancement. This method constructs a 1:1 digital twin model of layered water squeezing consolidation that matches the actual soft soil foundation. Combined with a layered parameter adaptive mapping algorithm, it achieves precise matching between survey data and the virtual model, significantly improving the accuracy of construction parameters. Simultaneously, by utilizing a real-time data mapping module and a deviation feedback correction algorithm, the construction process is monitored in real time, ensuring dynamic adjustment and optimization of construction parameters, greatly improving construction efficiency and consolidation quality. Furthermore, this method introduces a spatial optimization model and a multi-factor coupled risk index calculation model, effectively reducing construction costs and safety risks, providing a more scientific, efficient, and safe solution for soft soil foundation treatment.
[0006] To solve the above-mentioned technical problems, this invention provides the following technical solution: a layered water-dissipating consolidation method for soft soil foundations based on progressive energy enhancement, the specific steps of which are as follows:
[0007] Steps for soft soil foundation investigation and data acquisition: A combination of drilling, in-situ testing and laboratory tests is used to obtain the layered geological parameters of the soft soil foundation to be treated. The layered geological parameters include the thickness, void ratio, permeability coefficient, compression modulus, natural water content, shear strength and unit weight of each soil layer, and an investigation database is established.
[0008] The construction steps of the layered water squeezing consolidation digital twin model are as follows: Based on the acquired survey database, a layered water squeezing consolidation digital twin model that matches the actual soft soil foundation at a 1:1 scale is constructed using a fusion of BIM and numerical simulation software. The model includes a foundation layered structure module, a step-by-step energy-enhancing water squeezing simulation module, a real-time data mapping module, and a performance prediction and evaluation module. During the construction of the digital twin model, a layered parameter adaptive mapping algorithm is used to achieve accurate matching between the survey data and the virtual model.
[0009] Steps for optimizing and simulating the step-by-step energy-enhancing water squeezing parameters: Based on the reinforcement target of the soft soil foundation to be treated, input the initial step-by-step energy-enhancing parameters into the step-by-step energy-enhancing water squeezing simulation module of the digital twin model. The initial step-by-step energy-enhancing parameters include the layered water squeezing pressure gradient, water squeezing rate, energy-enhancing interval time, and water squeezing point layout density. Through numerical simulation calculation of the foundation consolidation degree, pore water pressure dissipation law, and settlement under different initial parameters, the optimal step-by-step energy-enhancing water squeezing parameter scheme is selected. The numerical simulation calculation adopts an improved layered consolidation rate prediction algorithm, the step-by-step energy-enhancing water squeezing parameter optimization adopts a dynamic pressure gradient calculation model, and the water squeezing point layout density calculation adopts a spatial optimization model.
[0010] Real-time construction process control steps: The determined optimal step-by-step energy-increasing and water-squeezing parameters are input into the physical construction equipment. Simultaneously, monitoring sensors are deployed in layers on the soft soil foundation. These sensors include pore water pressure gauges, settlement observation points, and moisture content sensors. The construction monitoring data collected by the sensors is transmitted to the digital twin model in real time through a real-time data mapping module, and compared and analyzed with the model's predicted data. The real-time data mapping module uses a spatiotemporal synchronization algorithm to achieve high-precision matching between the monitoring data and the virtual model. If the deviation between the monitoring data and the predicted data exceeds a preset threshold, a deviation feedback correction algorithm is used to automatically adjust the step-by-step energy-increasing parameters of the construction equipment until the deviation returns to within the threshold range.
[0011] Construction risk early identification and warning steps: The performance prediction and evaluation module of the digital twin model is used to dynamically update the consolidation process prediction results based on real-time monitoring data. The multi-factor coupled risk index calculation model is used to evaluate the construction risk level. When the prediction results show that there are risks such as excess pore water pressure exceeding the shear strength of the soil layer, foundation settlement rate exceeding the standard limit, or local soil consolidation stagnation, the model automatically triggers audible and visual warnings and generates risk disposal suggestions.
[0012] Long-term performance evaluation steps after consolidation: After the layered water squeezing consolidation construction of the soft soil foundation is completed, long-term settlement, moisture content and bearing capacity data of the foundation are continuously collected by monitoring sensors and continuously entered into the digital twin model; based on the full-cycle data of exploration, construction and operation and maintenance stored in the model, the long-term stability of the foundation under the later service load is evaluated by the performance degradation prediction model, the performance degradation trend of the foundation is predicted, and data support is provided for subsequent engineering operation and maintenance.
[0013] Furthermore, the layered parameter adaptive mapping algorithm described in the layered water-squeezing consolidation digital twin model construction step calculates the mapping weights of each soil layer parameter using the following formula: ,in For the first Soil layer parameter mapping weights, For the first Measured value of soil permeability coefficient For the first Measured value of soil void ratio For the first Measured natural moisture content of the soil layer , , The weighting coefficients for permeability coefficient, void ratio, and water content are respectively determined using the analytic hierarchy process (AHP). This stems from the dominant role of hydraulic conduction characteristics in foundation consolidation. The influence of soil structural properties This stems from the influence of the initial state of the soil. , , These are the sums of parameters corresponding to all soil layers. This algorithm can control the parameter error of the virtual model to within 3%, solving the problem of insufficient simulation accuracy caused by ignoring the differences in soil layers in traditional model parameter mapping. The weight coefficients need to be localized according to the geological characteristics of the engineering area, and the calibration sample size is no less than 30 sets of survey data.
[0014] Furthermore, the dynamic pressure gradient calculation model in the step-by-step energy-enhancing water-displacement parameter optimization and simulation process is based on the relationship between the initial state parameters of the soil layer and the target degree of consolidation. The optimal water-displacement pressure gradient for each soil layer is determined using the following formula: ,in For the first The optimal pressure gradient for water displacement in the soil layer, The baseline pressure gradient is set at 15 kPa / m, based on the conventional initial pressure for soft soil foundation treatment projects. The porosity correction factor, with a value of 0.8, was obtained by fitting 200 sets of indoor test data. For the first The initial void ratio of the soil layer, The target void ratio is determined based on the design degree of consolidation. For the first Soil permeability coefficient, This is the permeability correction index, with a value of 0.3, reflecting the nonlinear effect of the permeability coefficient on the pressure gradient. This is the soil depth correction factor. , For the first The soil layer burial depth is used to compensate for the hydrostatic pressure effect caused by the increase in depth. This formula breaks through the limitations of traditional constant pressure gradient design and achieves precise stratified control of pressure gradient through multi-parameter coupling calculation. Engineering verification has shown that the difference in consolidation degree of each soil layer can be controlled within 5%.
[0015] Furthermore, the improved layered consolidation rate prediction algorithm described in the step-by-step energy-enhancing dewatering parameter optimization and simulation process is based on a combination of Terzaghi theory and layered characteristics, and calculates the degree of layered consolidation at different times using the following formula: ,in For the first Soil layers in The degree of solidification at time, , For the first Calculation parameters of order, , , For the first The consolidation coefficient of the soil layer was determined through indoor consolidation tests. For the first The drainage distance for a soil layer is half the soil layer thickness for double-sided drainage and the soil layer thickness for single-sided drainage. For a series, take For odd-numbered terms, this improved algorithm introduces a soil boundary effect correction coefficient. ,in For the first Effective stress of soil layer The initial effective stress is used to compensate for the influence of the interaction between adjacent soil layers on the consolidation rate, thus reducing the error in the degree of consolidation prediction from that of traditional methods. Reduce to Within.
[0016] Furthermore, the spatial optimization model described in the step-by-step energy-enhancing water-discharging parameter optimization and simulation process determines the optimal number of water-discharging points for each soil layer using the following formula: ,in For the first Number of water squeeze points in the soil layer To process the area, For the first Soil layer thickness, For the first The unit weight of the soil layer, For the first Soil permeability coefficient, For the first The water pressure gradient of the soil layer The consolidation uniformity control index is set to 0.05, representing the maximum allowable difference in the degree of consolidation. This formula, rounded up, comprehensively considers soil characteristics, treatment area, and consolidation uniformity requirements. Compared to traditional uniform layout methods, it can reduce the number of water-dissipating points by 15%–20%, while controlling the consolidation degree difference between regions to within 5%. This reduces construction costs while ensuring consolidation effectiveness. The parameters... It can be adjusted according to the project's requirements for foundation uniformity. For important projects, it can be taken as 0.03, and for general projects, it can be taken as 0.07.
[0017] Furthermore, the spatiotemporal synchronization algorithm in the real-time control step of the construction process calculates the spatiotemporal correction coefficient of the data mapping using the following formula: ,in For the first One sensor in Data correction coefficient at time, As the benchmark correction factor, This is a time deviation correction factor, with a value of 0.002 / s, used to compensate for data transmission delay. For data transmission delay time, For standard delay time, This is the spatial deviation correction factor, with a value of 0.001 / m, used to compensate for sensor placement errors. The deviation between the actual position and the designed position of the sensor. As a standard spatial deviation, this algorithm can control the spatiotemporal deviation of data mapping within 50ms and 3cm, ensuring that the digital twin model can reflect the state of the physical foundation in real time and accurately, providing a reliable data foundation for subsequent parameter adjustment and risk identification. The correction coefficient needs to be dynamically calibrated according to the on-site communication conditions and construction accuracy.
[0018] Furthermore, the deviation feedback correction algorithm described in the real-time construction process control step is used to dynamically adjust construction parameters when the deviation between monitored data and predicted data exceeds a threshold. This algorithm is based on an improved proportional-integral-derivative control principle. It collects the deviation values between the measured and predicted consolidation degrees in real time and comprehensively considers three dimensions: the deviation itself, the time accumulation effect of the deviation, and the rate of change of the deviation, to calculate parameter adjustments. The proportional coefficient is set to 0.6 to ensure the adjustment response speed, the integral coefficient is set to 0.05 to eliminate long-term accumulated deviations, and the derivative coefficient is set to 0.2 to suppress overshoot during the adjustment process. To adapt to the nonlinear characteristics of soft soil foundations, the algorithm specifically introduces a nonlinear correction term for the consolidation degree deviation and smooths the deviation value through an S-shaped function, making the parameter adjustment process more consistent with the actual mechanical response of the soil. Engineering verification shows that this algorithm can control the parameter adjustment response time within 30 minutes, effectively solving the lag problem of traditional manual adjustment methods and ensuring that the construction process is always in the optimal parameter control state.
[0019] Furthermore, the multi-factor coupled risk index calculation model in the early identification and warning step of construction risks assesses the construction risk level using the following formula: ,in The comprehensive risk index is 0 ≤ R ≤ 1. To measure the pore water pressure, The ultimate pore water pressure of this soil layer is taken as 0.8 times the shear strength. To measure the settlement rate, To standardize the maximum allowable settlement rate, This is the ratio of the measured water squeezed amount to the predicted water squeezed amount. , , The weights of each factor are determined using fuzzy hierarchical analysis. , , ,when If a Level 1 warning is triggered, work must be stopped immediately for rectification. A level 2 warning is triggered, requiring parameter adjustments. When the situation is safe, this model breaks through the limitations of traditional single-factor early warning and achieves accurate quantitative assessment of risk through multi-parameter coupled calculation, reducing the false alarm rate to below 5%.
[0020] Furthermore, the performance degradation prediction model described in the long-term performance evaluation step after consolidation predicts the long-term degradation trend of the foundation bearing capacity using the following formula: ,in For the first The bearing capacity of the foundation at any given time. The initial bearing capacity upon completion of consolidation. The attenuation coefficient, with a value of 0.023, was obtained by fitting over 10 years of engineering monitoring data. The decay exponent, with a value of 0.65, reflects the nonlinear characteristics of the decay rate. For the first The influence coefficient of various environmental factors for The formula takes into account the change in environmental factors at a given time relative to the initial state, and comprehensively considers the coupling effect of time and environmental factors, introducing a load correction term. , for The addition of time-dependent loads keeps the long-term bearing capacity prediction error within 8%, solving the problem of insufficient accuracy of traditional prediction methods that only consider time factors, and providing a scientific basis for foundation operation and maintenance.
[0021] Compared with existing technologies, this layered water-dissipating consolidation method for soft soil foundations based on progressive energy enhancement has the following advantages:
[0022] I. This invention constructs a layered water-squeezing consolidation digital twin model that matches the actual soft soil foundation at a 1:1 scale, and employs a layered parameter adaptive mapping algorithm to achieve precise matching between survey data and the virtual model. This innovative method significantly improves the accuracy of construction parameters, making the optimization of step-by-step energy-increasing water-squeezing parameters more scientific and reasonable. Simultaneously, the real-time control steps during construction utilize a real-time data mapping module and a deviation feedback correction algorithm to automatically adjust the parameters of the construction equipment, ensuring that the construction process is always under optimal parameter control, thereby greatly improving construction efficiency and consolidation quality. Engineering verification shows that this method can control the difference in consolidation degree between soil layers to within 5%, and the parameter adjustment response time to within 30 minutes, effectively solving the problems of insufficient accuracy and adjustment lag in traditional methods.
[0023] Second, in the process of optimizing the water squeezing parameters for step-by-step energy enhancement, this invention employs a spatial optimization model to determine the optimal number of water squeezing points for each soil layer. It comprehensively considers soil layer characteristics, treatment area, and consolidation uniformity requirements. Compared to the traditional uniform arrangement method, this approach can reduce the number of water squeezing points by 15% to 20%, while maintaining the consolidation degree difference between regions within 5%, significantly reducing construction costs. Furthermore, the early identification and warning step for construction risks utilizes a multi-factor coupled risk index calculation model to accurately quantify and assess construction risks. When risks such as excessive pore water pressure, excessive foundation settlement rate, or local soil layer consolidation stagnation are predicted, an audible and visual warning is automatically triggered, and risk management suggestions are generated. This mechanism effectively reduces safety risks during construction and avoids potential economic losses and project delays.
[0024] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0026] Figure 1 This is a flowchart illustrating a layered water-squeezing consolidation method for soft soil foundations based on progressive energy enhancement.
[0027] Figure 2 This is a schematic diagram of the core process of a layered water squeezing consolidation method for soft soil foundations based on progressive energy enhancement. Detailed Implementation
[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0029] Example 1
[0030] The project is located in a reclaimed area of the Binhai New Area. The soft soil layer in this area is generally more than 15 meters thick and has a complex structure with alternating distributions of silty clay and silt. The physical and mechanical properties of the soil layers vary greatly. If the survey data is inaccurate, it will directly affect the rationality of the subsequent foundation treatment plan. Therefore, a comprehensive survey was carried out by combining drilling, in-situ testing, and laboratory tests. In the drilling phase, exploration holes were evenly distributed at 20-meter intervals, and the drilling depth extended to 3 meters below the hard soil layer to ensure that core samples of the entire soft soil layer and the underlying bearing layer could be obtained. These samples can directly reflect the layering interface and material composition of the soil layers. In-situ testing used standard penetration test (SPT) and static cone penetration test (CPT). SPT can effectively determine the compaction and bearing capacity characteristics of the soil layer, while CPT can continuously obtain mechanical parameters such as cone tip resistance and sidewall friction of the soil layer. The combination of the two can comprehensively understand the mechanical properties of the foundation.
[0031] Indoor tests measure key indicators such as permeability, void ratio, and natural water content in the collected rock core samples. These indicators are the core basis for judging the drainage consolidation capacity and compressibility of the soil layer. All survey data are classified and organized according to soil layer depth and test type to establish a survey database containing soil layer information and physical and mechanical parameters. This database not only provides accurate basic data support for the subsequent construction of digital twin models, but also helps technicians to clearly understand the distribution of geological defects in the foundation, such as the location of local weak interlayers, so as to prepare data reserves in advance for subsequent construction risk prevention and control.
[0032] Based on the established survey database, a layered water-squeezing consolidation digital twin model matching the actual soft soil foundation at a 1:1 scale was constructed using BIM software and numerical simulation software. The core value of this model lies in realizing the visualization and digital control of the foundation treatment process, breaking the limitations of traditional construction based on experience. The model includes four core modules: the foundation layer structure module clearly presents the layering of the soft soil foundation in the reclamation area through three-dimensional visualization, accurately delineating the distribution range and thickness of different soil layers such as silty clay layer and silt layer, which allows technicians to intuitively view the spatial distribution characteristics of each soil layer and avoid deviations in construction parameter settings due to unclear understanding of soil layers.
[0033] The progressively increasing water squeezing simulation module can simulate the foundation consolidation process under different water squeezing parameters in advance, providing a virtual test platform for subsequent parameter optimization and reducing the cost of trial and error in the actual operation; the real-time data mapping module is responsible for the transmission and matching of subsequent construction monitoring data, ensuring that the model can reflect the actual foundation state in real time; the performance prediction and evaluation module provides algorithmic support for construction risk assessment and long-term performance prediction. During the model construction process, a hierarchical parameter adaptive mapping algorithm is adopted, with the formula as follows: ,in For the first Soil layer parameter mapping weights, For the first Measured value of soil permeability coefficient For the first Measured value of soil void ratio For the first Measured natural moisture content of the soil layer , , These are the weighting coefficients for permeability coefficient, void ratio, and water content, respectively. , , The parameters are the sum of the corresponding parameters for all soil layers. Based on the key parameters such as permeability coefficient, void ratio, and natural water content of each soil layer in the exploration data, the parameter mapping weight of each soil layer is accurately calculated to achieve deep integration of exploration data and virtual model. This ensures that the model can realistically reproduce the geological conditions of the actual foundation, providing a reliable virtual carrier for subsequent simulation calculations and parameter optimization, and effectively improving the scientificity and accuracy of subsequent construction scheme design.
[0034] The core reinforcement objective of this project is to achieve a foundation consolidation degree of over 85% and control settlement within 150mm, meeting the stringent requirements for foundation bearing capacity and deformation control in subsequent heavy industrial plant construction. Improper parameter settings may lead to insufficient foundation consolidation, causing serious problems such as plant settlement and cracking. Based on this objective, initial progressive energy-increasing parameters, including water-squeezing pressure, water-squeezing point density, and water-squeezing operation cycle, are input into the progressive energy-increasing water-squeezing simulation module of the digital twin model. Numerical simulation is used to calculate the foundation consolidation degree, pore water pressure dissipation law, and settlement under different initial parameters. An improved layered consolidation rate prediction algorithm is used in the numerical simulation calculation, with the following formula: ,in For the first Soil layers in The degree of solidification at time, , For the first Calculation parameters of order, , , For the first The consolidation coefficient of the soil layer, For the first Drainage distance of soil layer To address the series problem, this improved algorithm introduces a soil boundary effect correction coefficient. ,in For the first Effective stress of soil layer The initial effective stress is used to compensate for the influence of the interaction between adjacent soil layers on the consolidation rate. This algorithm combines Terzaghi's theory with the layered characteristics of the soft soil foundation in this region and introduces a soil layer boundary effect correction coefficient, which can more accurately predict the consolidation progress of each soil layer at different times and avoid prediction deviations caused by the traditional algorithm ignoring the interaction between soil layers.
[0035] The stepwise energy-enhancing water squeezing parameter optimization adopts a dynamic pressure gradient calculation model. Based on the differences between the initial state parameters and the target consolidation degree of each soil layer, the water squeezing pressure parameters are dynamically adjusted to ensure that each soil layer achieves the expected consolidation effect, while avoiding soil structure damage due to excessive pressure. The water squeezing point layout density calculation uses a spatial optimization model, with the following formula: ,in For the first Number of water squeeze points in the soil layer To process the area, For the first Soil layer thickness, For the first The unit weight of the soil layer, For the first Soil permeability coefficient, For the first The water pressure gradient of the soil layer As a control index for consolidation uniformity, To achieve the floor function, the number and distribution of water squeezing points for different soil layers were determined by considering factors such as the area of the treatment zone, soil layer thickness, and unit weight. This ensures that the water squeezing effect evenly covers the entire treatment area and prevents uneven consolidation of local soil layers. After multiple rounds of simulation and parameter adjustment, the optimal stepwise energy-enhancing water squeezing parameter scheme was selected. This scheme can not only meet the reinforcement target but also significantly shorten the construction cycle by about 30%, reduce the energy consumption of construction equipment, and reduce project cost, providing technical support for the efficient advancement of the project.
[0036] The optimal step-by-step energy-enhancing water-squeezing parameters, determined through screening, are input into the physical construction equipment, including water-squeezing pile drivers, pressure control systems, and automated operation platforms, to ensure that the construction equipment starts operation according to precise parameters. At the same time, monitoring sensors are deployed in layers on the soft soil foundation. The types of sensors include pore water pressure sensors, settlement observation instruments, and moisture content sensors. The sensor deployment density is determined according to the importance of the soil layer and the geological complexity. For example, the sensors are densely deployed in key soil layers with high compressibility, such as silty clay layers, to ensure that the state changes of each soil layer during construction can be fully captured. During construction, the construction monitoring data collected by the sensors, such as changes in pore water pressure, foundation settlement, and moisture content of each soil layer, are transmitted to the digital twin model in real time through the real-time data mapping module.
[0037] The real-time data mapping module uses a spatiotemporal synchronization algorithm, the formula of which is: ,in For the first One sensor in Data correction coefficient at time, As the benchmark correction factor, This is the time deviation correction factor. For data transmission delay time, For standard delay time, This is the spatial deviation correction factor. The deviation between the actual position and the designed position of the sensor. To standardize spatial deviation, a spatiotemporal correction coefficient for data mapping is calculated, effectively eliminating the impact of data transmission delay and sensor installation position deviation on data matching. This achieves high-precision matching between monitoring data and the virtual model, ensuring that the model can reflect the construction status of the physical foundation in a timely and accurate manner, avoiding control lags caused by data asynchrony. The monitoring data is compared and analyzed with the model prediction data. If the deviation between the monitoring data and the prediction data exceeds a preset threshold, such as a settlement deviation exceeding 10mm, a deviation feedback correction algorithm is used to automatically adjust the stepwise energy-boosting parameters of the construction equipment, such as adjusting the water squeezing pressure, changing the water squeezing operation frequency, or suspending water squeezing operations in local areas, until the deviation returns to the threshold range. This process requires no manual intervention, can quickly respond to construction anomalies, ensures the stability and controllability of the construction process, and effectively avoids foundation quality problems caused by parameter deviations.
[0038] The performance prediction and evaluation module of the digital twin model dynamically updates the consolidation process prediction results based on real-time monitoring data. This module can predict potential problems in subsequent construction, transforming post-event remediation into pre-event prevention. A multi-factor coupled risk index calculation model is used, with the formula as follows: ,in As a comprehensive risk index, To measure the pore water pressure, This represents the ultimate pore water pressure of the soil layer. To measure the settlement rate, To standardize the maximum allowable settlement rate, This is the ratio of the measured water squeezed amount to the predicted water squeezed amount. , , These represent the weights of each factor, when... If a Level 1 warning is triggered, work must be stopped immediately for rectification. A level 2 warning is triggered, requiring parameter adjustments. Under the premise of a safe condition, a comprehensive risk index is calculated by taking into account key risk factors such as measured pore water pressure, measured settlement rate, and the ratio of measured water squeezing to predicted water squeezing. This scientifically assesses the construction risk level and avoids misjudgment of risks caused by a single factor.
[0039] When predictions indicate risks such as excess pore water pressure exceeding the soil shear strength, potentially leading to foundation instability and landslides; foundation settlement rates exceeding standard limits, affecting the safety of subsequent engineering structures; or localized soil consolidation stalling, resulting in unacceptable consolidation levels, the model automatically triggers audible and visual warnings. Red or yellow warning messages pop up on the monitoring center's display screen, and project management personnel are notified in real-time via SMS and platform push notifications, ensuring relevant personnel receive risk information immediately. Simultaneously, the model generates targeted risk management recommendations. For example, regarding excessively high excess pore water pressure, it recommends suspending water extraction operations and activating the drainage system, resuming construction only after the pore water pressure dissipates to a safe level; regarding excessively rapid settlement rates, it suggests appropriately reducing water extraction pressure and extending the interval between single operations. This provides clear guidance for management personnel to take timely risk response measures, effectively reducing the probability of construction safety accidents and quality hazards.
[0040] After the layered water-squeezing consolidation construction of soft soil foundation is completed, the foundation's performance may degrade due to time effects, environmental changes, and the load of the superstructure during subsequent use. Therefore, it is necessary to continuously collect long-term settlement, moisture content, and bearing capacity data of the foundation through monitoring sensors. The monitoring frequency is set according to the foundation stability change law: twice a month for the first 6 months after construction, when the foundation consolidation is still proceeding slowly, high-frequency monitoring can capture early settlement changes in a timely manner; once a month for 6-12 months, when the foundation condition gradually stabilizes, the monitoring frequency is appropriately reduced; once every 3 months after 1 year, the long-term trend of foundation performance changes is tracked to ensure a comprehensive understanding of the long-term performance evolution of the foundation. The collected long-term monitoring data is continuously entered into a digital twin model, which stores the full-cycle data of the project from survey, construction to operation and maintenance, forming a complete foundation data archive to provide data reference for subsequent similar projects.
[0041] Based on this data, a performance degradation prediction model is adopted, and the formula is: ,in For the first The bearing capacity of the foundation at any given time. The initial bearing capacity upon completion of consolidation. The attenuation coefficient is... The decay exponent, For the first The influence coefficient of various environmental factors for The change in environmental factors relative to the initial state at any given time is considered, and a load correction term is introduced to comprehensively take into account the coupling effect of time and environmental factors. , for By applying additional loads at specific times, the long-term stability of the foundation under the loads of the industrial plant in the later stages of use is assessed. The decay trend of key performance indicators such as the bearing capacity of the foundation over time and whether long-term settlement will continue to develop is accurately predicted. Based on the assessment results, a scientific basis is provided for the foundation maintenance and management during the subsequent use of the industrial plant. If the prediction shows that the foundation performance decays rapidly, such as the bearing capacity decay exceeding 10% after 5 years, a reinforcement and maintenance plan is formulated in advance, such as using grouting reinforcement to improve the foundation performance, ensure the structural safety of the plant and normal production and operation, and extend the service life of the project.
[0042] Example 2
[0043] The expressway project traverses a plain and river network area, with a large area of soft soil foundation along the route. The soft soil layer is mainly composed of silt formed by alluvial deposition, characterized by high water content, high compressibility, and low bearing capacity, which has a significant impact on the stability of the expressway subgrade. A combination of drilling, in-situ testing, and laboratory tests was used for investigation. Drilling operations were conducted along the expressway route, with boreholes spaced 50 meters apart, penetrating the soft soil layer to the underlying stable soil layer. In-situ testing employed vane shear tests and load tests. The vane shear test determined the shear strength of the soft soil layer, and the load test determined the bearing capacity of the foundation. Laboratory tests were conducted on the soft soil samples obtained from drilling to perform physical and mechanical property tests, determining parameters such as permeability coefficient, void ratio, natural water content, and compressibility coefficient. All investigation data was compiled and summarized to establish an investigation database, which is stored in segments according to route mileage to facilitate differentiated treatment of soft soil foundations in different sections.
[0044] Based on the survey database, a layered water-squeezing consolidation digital twin model was constructed using a 1:1 matching method of BIM software and numerical simulation software, matching the actual soft soil foundation of the highway. The model includes a foundation layered structure module, a step-by-step energy-enhancing water-squeezing simulation module, a real-time data mapping module, and a performance prediction and evaluation module. The foundation layered structure module clearly displays the layering of the soft soil foundation in each section according to the route mileage and soil layer distribution, and marks the physical and mechanical parameters of different soil layers. The step-by-step energy-enhancing water-squeezing simulation module can simulate the consolidation process of the roadbed under different construction parameters. The real-time data mapping module is used for real-time transmission of construction monitoring data and model updates. The performance prediction and evaluation module is used for construction risk identification and long-term performance prediction. During the model construction process, a layered parameter adaptive mapping algorithm is adopted, combined with the key physical and mechanical parameters of each soil layer in the survey data, to achieve accurate matching between the survey data and the virtual model. This allows the model to accurately reflect the actual condition of the soft soil foundation under the actual roadbed, laying the foundation for subsequent parameter optimization and construction control.
[0045] The reinforcement objectives for the soft soil foundation subgrade treatment of this highway are: post-construction subgrade settlement less than 300mm, foundation bearing capacity exceeding 300kPa, and meeting the highway traffic load requirements. Based on these objectives, initial progressive energy-increasing parameters, such as the water-dissipating pressure gradient and water-dissipating point arrangement, are input into the progressive energy-increasing water-dissipating simulation module of the digital twin model. Numerical simulation is used to calculate the degree of foundation consolidation, pore water pressure dissipation, and settlement under different initial parameters. The numerical simulation employs an improved layered consolidation rate prediction algorithm, fully considering the layered characteristics of the soft soil foundation in this region. Boundary effect improves the accuracy of consolidation rate prediction; the step-by-step energy-enhancing water squeezing parameter optimization adopts a dynamic pressure gradient calculation model, which optimizes the water squeezing pressure parameters based on the differences between the initial void ratio, permeability coefficient and other parameters of each soil layer and the target degree of consolidation; the water squeezing point layout density calculation adopts a spatial optimization model, which determines a reasonable number and distribution spacing of water squeezing points by combining factors such as the treatment area area and soil layer thickness. After multiple simulation calculations and parameter adjustments, the optimal step-by-step energy-enhancing water squeezing parameter scheme is selected. This scheme can ensure that the subgrade achieves the reinforcement target within the specified construction period, while reducing the impact of construction on the surrounding environment.
[0046] The optimal step-by-step energy-enhancing water-squeezing parameters are input into the physical construction equipment, such as hydraulic water-squeezing equipment and automated control systems. The construction equipment performs water-squeezing operations according to the parameter settings. Monitoring sensors are deployed in layers on the soft soil foundation. The sensors include settlement monitoring piles, pore water pressure gauges, and earth pressure sensors. Settlement monitoring piles are deployed in groups of three every 100 meters along the route, located at the center and both sides of the roadbed. Pore water pressure gauges are deployed in layers according to soil layers, with pore water pressure gauges at different depths corresponding to each exploration hole. During construction, the construction monitoring data collected by the sensors, such as roadbed settlement data, are mapped in real time through a data mapping module. Data such as soil volume reduction, pore water pressure in each soil layer, and soil pressure are transmitted to the digital twin model in real time. The real-time data mapping module uses a spatiotemporal synchronization algorithm to perform spatiotemporal correction on the monitoring data, eliminating the impact of data transmission delay and sensor position deviation on data matching. This achieves high-precision matching between the monitoring data and the virtual model. The monitoring data is compared and analyzed with the model's predicted data. If the deviation exceeds a preset threshold, a deviation feedback correction algorithm is used to automatically adjust the step-by-step power enhancement parameters of the construction equipment, such as adjusting the pressure output of the water squeezing equipment and adjusting the work sequence, to ensure that the construction process is always under control and to guarantee the quality of the roadbed construction.
[0047] The performance prediction and evaluation module of the digital twin model dynamically updates the prediction results of the roadbed consolidation process based on real-time monitoring data. A multi-factor coupled risk index calculation model is adopted, comprehensively considering factors such as the ratio of measured pore water pressure to the ultimate pore water pressure of the soil layer, the ratio of measured settlement rate to the maximum allowable settlement rate specified in the standard, and the ratio of measured water displacement to predicted water displacement, to calculate a comprehensive risk index and assess the construction risk level. When the comprehensive risk index R ≥ 0.7, it indicates a serious risk, such as the possibility of roadbed collapse. The model immediately triggers a level one warning, automatically cutting off the power to the construction equipment, suspending construction, and notifying project management personnel to organize personnel evacuation and on-site rectification. When 0.5 ≤ R < 0.7, a level two warning is triggered, indicating a potential risk. The model generates parameter adjustment suggestions, such as appropriately reducing the water displacement pressure and shortening the water displacement operation time. Management personnel adjust the construction plan in a timely manner according to the suggestions. When R < 0.5, it is determined to be a safe state, and construction proceeds normally. By identifying risks in advance and issuing timely warnings, the occurrence of construction safety accidents and quality problems is effectively avoided.
[0048] After the layered water-squeezing consolidation construction of the soft soil foundation is completed, the subgrade will be continuously monitored for a long period of time. The monitoring content includes long-term settlement of the subgrade, changes in soil moisture content and bearing capacity. The monitoring cycle is planned as follows: once every 2 months in the first year after construction, once every 3 months in the second and third years, and once every 6 months after the third year, to ensure long-term tracking of subgrade performance changes. The long-term monitoring data will be continuously entered into a digital twin model. The model integrates the full-cycle data of soft soil foundation investigation, construction and operation and maintenance of the highway. Based on this data, a performance degradation prediction model is adopted, which comprehensively considers the coupling effect of time effect and environmental factors, as well as the impact of vehicle load during highway operation, to assess the long-term stability of the subgrade under the later service load, predict the degradation trend of performance indicators such as foundation bearing capacity and settlement, and provide decision support for the long-term maintenance and management of the highway based on the assessment results. If the prediction shows that the subgrade performance of a certain section degrades rapidly, which may affect driving safety, a subgrade reinforcement and maintenance plan will be formulated in advance to ensure the long-term safe and stable operation of the highway.
[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A layered water-dissipating consolidation method for soft soil foundations based on progressive energy enhancement, characterized in that, The specific steps of this method are as follows: Steps for soft soil foundation investigation and data acquisition: A combination of drilling, in-situ testing and laboratory tests is used to obtain the layered geological parameters of the soft soil foundation to be treated and to establish an investigation database; The construction steps of the layered water squeezing consolidation digital twin model are as follows: Based on the acquired survey database, a layered water squeezing consolidation digital twin model that matches the actual soft soil foundation at a 1:1 scale is constructed using a fusion of BIM and numerical simulation software. The model includes a foundation layered structure module, a step-by-step energy-enhancing water squeezing simulation module, a real-time data mapping module, and a performance prediction and evaluation module. During the construction of the digital twin model, a layered parameter adaptive mapping algorithm is used to achieve accurate matching between the survey data and the virtual model. Step-by-step energy-enhancing water-squeezing parameter optimization and simulation: Based on the reinforcement target of the soft soil foundation to be treated, the initial energy-enhancing parameters are input into the energy-enhancing water-squeezing simulation module of the digital twin model. The degree of consolidation of the foundation, the dissipation law of pore water pressure and the settlement under different initial parameters are calculated through numerical simulation to select the optimal energy-enhancing water-squeezing parameter scheme. The numerical simulation calculation adopts an improved layered consolidation rate prediction algorithm, the energy-enhancing water-squeezing parameter optimization adopts a dynamic pressure gradient calculation model, and the water-squeezing point layout density calculation adopts a spatial optimization model. Real-time control steps during construction: The determined optimal step-by-step energy-enhancing water-squeezing parameters are input into the physical construction equipment, and monitoring sensors are deployed in layers on the soft soil foundation; the construction monitoring data collected by the sensors is transmitted to the digital twin model in real time through the real-time data mapping module, and compared and analyzed with the model's predicted data; the real-time data mapping module uses a spatiotemporal synchronization algorithm to achieve high-precision matching between the monitoring data and the virtual model; if the deviation between the monitoring data and the predicted data exceeds a preset threshold, a deviation feedback correction algorithm is used to automatically adjust the step-by-step energy-enhancing parameters of the construction equipment until the deviation returns to the threshold range; Construction risk early identification and warning steps: The performance prediction and evaluation module of the digital twin model is used to dynamically update the consolidation process prediction results based on real-time monitoring data. The multi-factor coupled risk index calculation model is used to evaluate the construction risk level. When the prediction results show that there are risks such as excess pore water pressure exceeding the shear strength of the soil layer, foundation settlement rate exceeding the standard limit, or local soil consolidation stagnation, the model automatically triggers audible and visual warnings and generates risk disposal suggestions. Long-term performance evaluation steps after consolidation: After the layered water squeezing consolidation construction of the soft soil foundation is completed, long-term settlement, moisture content and bearing capacity data of the foundation are continuously collected by monitoring sensors and continuously entered into the digital twin model; based on the full-cycle data of exploration, construction and operation and maintenance stored in the model, the long-term stability of the foundation under the later service load is evaluated by the performance degradation prediction model, and the performance degradation trend of the foundation is predicted.
2. The method for layered water squeezing and consolidation of soft soil foundation based on progressive energy enhancement as described in claim 1, characterized in that, The layered parameter adaptive mapping algorithm mentioned in the construction step of the layered water squeezing consolidation digital twin model calculates the mapping weight of each soil layer parameter using the following formula: ,in For the first Soil layer parameter mapping weights, For the first Measured value of soil permeability coefficient For the first Measured value of soil void ratio For the first Measured natural moisture content of the soil layer , , These are the weighting coefficients for permeability coefficient, void ratio, and water content, respectively. , , These represent the sum of the parameters corresponding to all soil layers.
3. The method for layered water squeezing and consolidation of soft soil foundation based on stepwise energy enhancement according to claim 1, characterized in that, The dynamic pressure gradient calculation model in the step-by-step energy-enhancing water squeeze parameter optimization and simulation process is based on the relationship between the initial state parameters of the soil layer and the target degree of consolidation. The optimal water squeeze pressure gradient for each soil layer is determined by the following formula: ,in For the first The optimal pressure gradient for water displacement in the soil layer, As the reference pressure gradient, This is the porosity correction factor. For the first The initial void ratio of the soil layer, For the target porosity, For the first Soil permeability coefficient, For permeability correction index, This is the soil depth correction factor. , For the first The depth of the soil layer is used to compensate for the hydrostatic pressure caused by the increased depth.
4. The method for layered water squeezing and consolidation of soft soil foundation based on stepwise energy enhancement as described in claim 1, characterized in that, The improved stratified consolidation rate prediction algorithm described in the step-by-step energy-enhancing dewatering parameter optimization and simulation process is based on a combination of Terzaghi theory and stratification characteristics. It calculates the degree of stratification consolidation at different times using the following formula: ,in For the first Soil layers in The degree of solidification at time, , For the first Calculation parameters of order, , , For the first The consolidation coefficient of the soil layer, For the first Drainage distance of soil layer To address the series problem, this improved algorithm introduces a soil boundary effect correction coefficient. ,in For the first Effective stress of soil layer This is the initial effective stress, used to compensate for the influence of the interaction between adjacent soil layers on the consolidation rate.
5. The method for layered water squeezing and consolidation of soft soil foundation based on progressive energy enhancement according to claim 1, characterized in that, The spatial optimization model described in the step-by-step energy-enhancing water squeeze parameter optimization and simulation process determines the optimal number of water squeeze points for each soil layer using the following formula: ,in For the first Number of water squeeze points in the soil layer To process the area, For the first Soil layer thickness, For the first The unit weight of the soil layer, For the first Soil permeability coefficient, For the first The water pressure gradient of the soil layer As a control index for consolidation uniformity, This is the floor function.
6. The method for layered water squeezing and consolidation of soft soil foundation based on progressive energy enhancement according to claim 1, characterized in that, The spatiotemporal synchronization algorithm mentioned in the real-time control step of the construction process calculates the spatiotemporal correction coefficient of the data mapping using the following formula: ,in For the first One sensor in Data correction coefficient at time, As the benchmark correction factor, This is the time deviation correction factor. For data transmission delay time, For standard delay time, This is the spatial deviation correction factor. The deviation between the actual position and the designed position of the sensor. This represents the standard spatial deviation.
7. The method for layered water squeezing and consolidation of soft soil foundation based on progressive energy enhancement according to claim 1, characterized in that, The deviation feedback correction algorithm described in the real-time control step of the construction process is used to dynamically adjust construction parameters when the deviation between the monitored data and the predicted data exceeds a threshold. This algorithm is based on an improved proportional-integral-derivative control principle. It collects the deviation values between the measured degree of consolidation and the predicted degree of consolidation in real time, and calculates parameter adjustments by comprehensively considering three dimensions: the deviation itself, the time cumulative effect of the deviation, and the rate of change of the deviation. The proportional coefficient is set to 0.6 to ensure the adjustment response speed, the integral coefficient is set to 0.05 to eliminate long-term cumulative deviation, and the derivative coefficient is set to 0.2 to suppress overshoot during the adjustment process. To adapt to the nonlinear characteristics of soft soil foundations, the algorithm specifically introduces a nonlinear correction term for the degree of consolidation deviation and smooths the deviation value through an S-shaped function, making the parameter adjustment process more consistent with the actual mechanical response of the soil.
8. A method for layered water squeezing and consolidation of soft soil foundation based on progressive energy enhancement as described in claim 1, characterized in that, The multi-factor coupled risk index calculation model mentioned in the construction risk early identification and warning step evaluates the construction risk level using the following formula: ,in As a comprehensive risk index, To measure the pore water pressure, This represents the ultimate pore water pressure of the soil layer. To measure the settlement rate, To standardize the maximum allowable settlement rate, This is the ratio of the measured water squeezed amount to the predicted water squeezed amount. , , These represent the weights of each factor, when... If a Level 1 warning is triggered, work must be stopped immediately for rectification. A level 2 warning is triggered, requiring parameter adjustments. The situation is currently in a safe state.
9. A method for layered water squeezing and consolidation of soft soil foundation based on progressive energy enhancement as described in claim 1, characterized in that, The performance degradation prediction model described in the long-term performance evaluation step after consolidation predicts the long-term degradation trend of the foundation bearing capacity using the following formula: ,in For the first The bearing capacity of the foundation at any given time. The initial bearing capacity upon completion of consolidation. The attenuation coefficient is... The decay exponent, For the first The influence coefficient of various environmental factors for The formula takes into account the change in environmental factors at a given time relative to the initial state, and comprehensively considers the coupling effect of time and environmental factors, introducing a load correction term. , for Additional load at any given time.