Optimization design method and system for foundation pit support

Abstract

The invention discloses an optimal design method and system for foundation pit supporting, and aims to improve the design efficiency and safety of a supporting structure, collect on-site landform and soil physical and mechanical parameters, evaluate the influence of foundation pit excavation on the surrounding environment, analyze geological disaster risks, formulate preventive measures and select a proper supporting structure type. Finite element analysis software is used for simulating the performance of different supporting schemes, monitoring equipment is installed to monitor the state of a foundation pit in real time, a machine learning algorithm is used for establishing a prediction model, a design scheme is adjusted according to monitoring data, the final performance of a supporting system is evaluated, abnormal conditions are recorded, and the design process and the construction method are continuously improved according to project feedback. According to the method, a data acquisition and processing module, a design decision support module, a numerical simulation and optimization module, a monitoring and feedback control module, a construction management module and a later evaluation and feedback module are integrated, a closed-loop optimization design system is formed, key problems in support design are solved, and engineering efficiency and safety are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of foundation pit structures, and in particular to an optimization design method and system for foundation pit support. Background Art

[0002] As an important part of urban underground space engineering development, foundation pit excavation usually has great safety risks in its construction process. Monitoring and optimizing the design of deep foundation pit support structures are the key to ensuring the safety of deep foundation pit construction and engineering quality. In addition, the geological conditions at the construction site are complex and changeable, including but not limited to soil type, groundwater level changes, geological structure, etc. These factors will bring uncertainty to the support design. In traditional designs, there is often a lack of effective assessment of potential risks, such as landslides and collapses, which may lead to insufficient safety of the design. There is a lack of effective dynamic monitoring methods during the construction process, and it is impossible to detect potential problems in time and respond. This foundation pit support optimization design method can effectively solve various problems in the entire process from geological environment investigation to construction. Summary of the invention

[0003] An optimization design method for foundation pit support comprises the following steps: S1. Geological and environmental survey: Collect basic data on the site's topography, soil cohesion, friction angle, geological structure, soil properties, and groundwater level, and consider the impact of foundation pit excavation on surrounding buildings, roads, and pipeline infrastructure; S2. Risk assessment: Analyze potential risks of landslide, collapse, and groundwater level changes, evaluate technical difficulties and safety issues encountered during construction, and formulate prevention and response measures for various possible risks; S3. Design concept and preliminary plan: Support system selection: According to geological conditions and risk assessment results, determine which support structure to use (such as steel sheet piles, underground continuous walls, soil nail walls, anchor cables, etc.), make preliminary size estimates, and preliminarily determine the size and material of the support structure based on empirical formulas; S4. Numerical analysis and optimization: Use finite element analysis software to establish a three-dimensional model of the foundation pit and surrounding environment, boundary condition setting: define the stratum properties, support structure parameters, construction process, multiple scheme comparison: simulate the performance of different support schemes at different construction stages, and compare safety, economy and efficiency; S5. Real-time monitoring and feedback: Install monitoring equipment during the construction process: inclinometers and stress gauges to monitor the displacement and stress changes of the foundation pit in real time, collect monitoring data, and use machine learning algorithms to establish a foundation pit prediction model based on the corresponding data, and integrate the formula ,in, is the prediction result of foundation pit quality. represents the cumulative sedimentation, represents the water level value, F is the external force, k is the comprehensive stiffness value obtained through experiments and numerical simulations, which includes the influence of soil properties and supporting structure factors, and compares and analyzes the calculation results with the theoretical results according to the formula, adjusts the design plan according to the monitoring data, and takes reinforcement measures when necessary; S6. Construction planning and management: construction sequence planning, formulating a reasonable construction process, reducing the impact on the surrounding environment and residents, installing monitoring equipment, regularly checking the performance of the support structure and changes in the surrounding environment, and monitoring the construction process to ensure that it meets the design requirements and safety standards; S7. Post-evaluation and feedback: Evaluate the final performance of the support system, record any abnormalities, and update the design process and construction methods based on project feedback to improve overall project quality and efficiency.

[0004] Furthermore, an optimization design method for foundation pit support is provided. In step S4, a finite element analysis software is used to establish a three-dimensional model of the foundation pit and the surrounding environment, and boundary conditions are set: defining the stratum properties, support structure parameters, and construction process. The specific steps are as follows: S41. Establish geometric model: obtain the design drawings of the foundation pit, geological survey report, and support structure design information, and draw a three-dimensional model in the software according to the size and shape of the foundation pit, including the excavation range of the foundation pit, support structure, and surrounding soil; S42. Define material properties: Input the physical and mechanical parameters of different soil layers according to the geological report: elastic modulus, Poisson's ratio, internal friction angle, cohesion, input the elastic modulus, density, and Poisson's ratio of concrete or steel; S43. Set boundary conditions: apply appropriate displacement constraints to the bottom and sides of the model. The bottom usually fixes all degrees of freedom, while the sides only restrict horizontal displacement. Load conditions: Ground load: consider surface buildings and traffic loads. Groundwater pressure: determine the groundwater level based on hydrogeological data and apply corresponding water pressure. Support structure load: simulate the installation process of the support structure and gradually apply support reaction force. S44. High-precision meshing: Use the software's built-in adaptive meshing function and scripting to automatically adjust the mesh density according to the gradient of stress and strain, using finer meshes near the support structure and in stress concentration areas, and coarser meshes away from these areas; S45. Intelligent boundary conditions: Based on geological reports and field monitoring data, the boundary conditions are automatically adjusted using algorithms to better reflect the complex geological conditions and changes in external loads. When a rise in groundwater levels is detected, the software can automatically update the groundwater pressure, taking into account the impact of climate change on groundwater levels and soil properties, and dynamically adjust boundary conditions; S46. Construction process simulation: According to the actual construction sequence, excavation and support structure are gradually installed, multiple analysis steps are set to simulate the entire construction process, and positions corresponding to on-site monitoring points are set in the model to compare the simulation results with the measured data; S47. Analysis and calculation: Run the model calculation, the software will automatically solve the stress, strain and displacement in each analysis step, view and analyze the calculation results, and use deformation cloud diagrams to represent the calculation results; S48. Result verification and adjustment: Compare the calculated results with the field monitoring data, evaluate the accuracy of the model, and adjust the model parameters according to the verification results until a satisfactory prediction effect is obtained.

[0005] Furthermore, an optimization design method for foundation pit support is provided. In step S5, monitoring equipment is installed during the construction process: inclinometer, stress meter, real-time monitoring of the displacement and stress changes of the foundation pit, and monitoring data is collected. A foundation pit prediction model is established based on the corresponding data using a machine learning algorithm. The specific steps of adjusting the design plan based on the monitoring data are as follows; S51: Data collection: During the foundation pit construction process, inclinometers and stress gauge monitoring equipment are installed to collect displacement and stress change data of the foundation pit in real time; S52: Data preprocessing: Clean and organize the collected raw data, remove outliers and erroneous data, and prepare for subsequent analysis. According to the characteristics of the foundation pit project, extract relevant characteristic variables from the collected data: time, location, depth, soil type, and support structure; S53: Model construction: Use support vector machine to establish foundation pit prediction model, integrate the formula ,in, is the prediction result of foundation pit quality. represents the cumulative sedimentation, represents the final water level value, F is the external force, k is the comprehensive stiffness value obtained through experiments and numerical simulations, which includes the influence of soil properties and supporting structure factors, and the calculation results are compared and analyzed with the theoretical results according to the formula; S54: Model training: Use existing historical data to train the model, continuously adjust the model parameters so that it can better fit the existing data, use a portion of the retained data to verify the model and check its prediction ability. When new monitoring data arrives, input it into the model to predict the quality changes of the foundation pit; S55: Result analysis and adjustment: Compare and analyze the model prediction results with the theoretical calculation results to check the differences between the two. When there is a large deviation between the prediction results and the theoretical calculation, it means that there are factors that have not been fully considered. At this time, the design plan needs to be adjusted: change the support structure type, adjust the construction sequence; S56: Update training and verification: The updated design will generate new data, which will be added to the model, and the model will be retrained and verified. The cycle will be repeated until the model prediction results are close to the theoretical calculation results and the error is within an acceptable range. S57: Monitoring and maintenance: Continuously monitor the status of the foundation pit during the construction process, update the model based on the monitoring data, and ensure the safety and stability of the foundation pit project.

[0006] An optimization design system for foundation pit support, the optimization design system for foundation pit support is used to implement an optimization design method for foundation pit support; the optimization design system for foundation pit support includes: a project data acquisition and processing module, a design decision support module, a numerical simulation and optimization module, a monitoring and feedback control module, a project construction management module, and a post-evaluation and feedback module; The project data acquisition and processing module is responsible for collecting basic information such as the site's topography, soil physical and mechanical parameters (such as cohesion and internal friction angle), geological structure, soil properties, and groundwater level, assessing geological disaster risks, and analyzing potential risks of landslides, collapses, and groundwater level changes; Design decision support module: select the appropriate support structure type (such as steel sheet piles, underground continuous walls, soil nail walls, anchor cables) according to geological conditions and risk assessment results, and preliminarily determine the size and material of the support structure based on empirical formulas; Numerical simulation and optimization module: Use finite element analysis software to build a three-dimensional model of the foundation pit and its surrounding environment, set boundary conditions, simulate the performance of different support schemes at different construction stages, compare safety, economy and efficiency, adjust model parameters based on on-site monitoring data, and ensure that the design meets the constraints (such as safety factor, displacement limit); Monitoring and feedback control module: Install monitoring equipment such as inclinometers and stress gauges to monitor the displacement and stress changes of the foundation pit in real time, collect monitoring data, and use machine learning algorithms to establish a foundation pit prediction model, adjust the design plan or take necessary reinforcement measures based on the monitoring data; Project construction management module: formulate a reasonable construction process to reduce the impact on the surrounding environment and residents, install monitoring equipment, regularly check the performance of the support structure and changes in the surrounding environment, and ensure that the construction process meets the design requirements and safety standards; Post-evaluation and feedback module: Evaluate the final performance of the support system, record any abnormal conditions, update the design process and construction methods based on project feedback, and improve the overall project quality and efficiency.

[0007] Beneficial effects of the present invention: Through comprehensive investigation and risk assessment of geology and environment, potential risk points can be identified more accurately, and targeted preventive measures can be taken to significantly improve the safety level of foundation pit support. The application of numerical analysis and optimization modules enables the design scheme to simulate actual working conditions more accurately, ensuring that the safety factor and displacement limit constraints of the design scheme are met. Through real-time monitoring and feedback, problems in the construction process can be discovered and solved in a timely manner, delays caused by emergencies can be avoided, and the controllability of the construction progress can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of an optimization design method for foundation pit support; Figure 2 It is a module diagram of an optimization design system for foundation pit support; Figure 3 is a bar chart of feature importance; DETAILED DESCRIPTION

[0009] An optimization design method for foundation pit support, the specific process is as follows: Figure 1 As shown, the following steps are included; S1. Geological and environmental survey: Collect basic data on the site's topography, soil cohesion, friction angle, geological structure, soil properties, and groundwater level, and consider the impact of foundation pit excavation on surrounding buildings, roads, and pipeline infrastructure; S2. Risk assessment: Analyze potential risks of landslide, collapse, and groundwater level changes, evaluate technical difficulties and safety issues encountered during construction, and formulate prevention and response measures for various possible risks; S3. Design concept and preliminary plan: Support system selection: According to geological conditions and risk assessment results, determine which support structure to use (such as steel sheet piles, underground continuous walls, soil nail walls, anchor cables, etc.), make preliminary size estimates, and preliminarily determine the size and material of the support structure based on empirical formulas; S4. Numerical analysis and optimization: Use finite element analysis software to establish a three-dimensional model of the foundation pit and surrounding environment, boundary condition setting: define the stratum properties, support structure parameters, construction process, multiple scheme comparison: simulate the performance of different support schemes at different construction stages, and compare safety, economy and efficiency; S5. Real-time monitoring and feedback: Install monitoring equipment during the construction process: inclinometers and stress gauges to monitor the displacement and stress changes of the foundation pit in real time, collect monitoring data, and use machine learning algorithms to establish a foundation pit prediction model based on the corresponding data, and integrate the formula ,in, is the prediction result of foundation pit quality. represents the cumulative sedimentation, represents the water level value, F is the external force, k is the comprehensive stiffness value obtained through experiments and numerical simulations, which includes the influence of soil properties and supporting structure factors, and compares and analyzes the calculation results with the theoretical results according to the formula, adjusts the design plan according to the monitoring data, and takes reinforcement measures when necessary; S6. Construction planning and management: construction sequence planning, formulating a reasonable construction process, reducing the impact on the surrounding environment and residents, installing monitoring equipment, regularly checking the performance of the support structure and changes in the surrounding environment, and monitoring the construction process to ensure that it meets the design requirements and safety standards; S7. Post-evaluation and feedback: Evaluate the final performance of the support system, record any abnormalities, and update the design process and construction methods based on project feedback to improve overall project quality and efficiency.

[0010] Furthermore, an optimization design method for foundation pit support is provided. In step S4, a finite element analysis software is used to establish a three-dimensional model of the foundation pit and the surrounding environment, and boundary conditions are set: defining the stratum properties, support structure parameters, and construction process. The specific steps are as follows: S41. Establish geometric model: obtain the design drawings of the foundation pit, geological survey report, and support structure design information, and draw a three-dimensional model in the software according to the size and shape of the foundation pit, including the excavation range of the foundation pit, support structure, and surrounding soil; S42. Define material properties: Input the physical and mechanical parameters of different soil layers according to the geological report: elastic modulus, Poisson's ratio, internal friction angle, cohesion, input the elastic modulus, density, and Poisson's ratio of concrete or steel; S43. Set boundary conditions: apply appropriate displacement constraints to the bottom and sides of the model. The bottom usually fixes all degrees of freedom, while the sides only restrict horizontal displacement. Load conditions: Ground load: consider surface buildings and traffic loads. Groundwater pressure: determine the groundwater level based on hydrogeological data and apply corresponding water pressure. Support structure load: simulate the installation process of the support structure and gradually apply support reaction force. S44. High-precision meshing: Use the software's built-in adaptive meshing function and scripting to automatically adjust the mesh density according to the gradient of stress and strain, using finer meshes near the support structure and in stress concentration areas, and coarser meshes away from these areas; S45. Intelligent boundary conditions: Based on geological reports and field monitoring data, the boundary conditions are automatically adjusted using algorithms to better reflect the complex geological conditions and changes in external loads. When a rise in groundwater levels is detected, the software can automatically update the groundwater pressure, taking into account the impact of climate change on groundwater levels and soil properties, and dynamically adjust boundary conditions; S46. Construction process simulation: According to the actual construction sequence, excavation and support structure are gradually installed, multiple analysis steps are set to simulate the entire construction process, and positions corresponding to on-site monitoring points are set in the model to compare the simulation results with the measured data; S47. Analysis and calculation: Run the model calculation, the software will automatically solve the stress, strain and displacement in each analysis step, view and analyze the calculation results, and use deformation cloud diagrams to represent the calculation results; S48. Result verification and adjustment: Compare the calculated results with the field monitoring data, evaluate the accuracy of the model, and adjust the model parameters according to the verification results until a satisfactory prediction effect is obtained.

[0011] Furthermore, an optimization design method for foundation pit support is provided. In step S5, monitoring equipment is installed during the construction process: inclinometer, stress meter, real-time monitoring of the displacement and stress changes of the foundation pit, and monitoring data is collected. A foundation pit prediction model is established based on the corresponding data using a machine learning algorithm. The specific steps of adjusting the design plan based on the monitoring data are as follows; S51: Data collection: During the foundation pit construction process, inclinometers and stress gauge monitoring equipment are installed to collect displacement and stress change data of the foundation pit in real time; S52: Data preprocessing: Clean and organize the collected raw data, remove outliers and erroneous data, and prepare for subsequent analysis. According to the characteristics of the foundation pit project, extract relevant characteristic variables from the collected data: time, location, depth, soil type, support structure, such as Figure 3 As shown; S53: Model construction: Use support vector machine to establish foundation pit prediction model, integrate the formula ,in, is the prediction result of foundation pit quality. represents the cumulative sedimentation, represents the final water level value, F is the external force, k is the comprehensive stiffness value obtained through experiments and numerical simulations, which includes the influence of soil properties and supporting structure factors, and the calculation results are compared and analyzed with the theoretical results according to the formula; S54: Model training: Use existing historical data to train the model, continuously adjust the model parameters so that it can better fit the existing data, use a portion of the retained data to verify the model and check its prediction ability. When new monitoring data arrives, input it into the model to predict the quality changes of the foundation pit; S55: Result analysis and adjustment: Compare and analyze the model prediction results with the theoretical calculation results to check the differences between the two. When there is a large deviation between the prediction results and the theoretical calculation, it means that there are factors that have not been fully considered. At this time, the design plan needs to be adjusted: change the support structure type, adjust the construction sequence; S56: Update training and verification: The updated design will generate new data, which will be added to the model, and the model will be retrained and verified. The cycle will be repeated until the model prediction results are close to the theoretical calculation results and the error is within an acceptable range. S57: Monitoring and maintenance: Continuously monitor the status of the foundation pit during the construction process, update the model based on the monitoring data, and ensure the safety and stability of the foundation pit project.

[0012] An optimization design system for foundation pit support, the optimization design system for foundation pit support is used to implement an optimization design method for foundation pit support; the optimization design system for foundation pit support, the specific modules are as follows Figure 2 As shown, it includes: project data acquisition and processing module, design decision support module, numerical simulation and optimization module, monitoring and feedback control module, project construction management module, and post-evaluation and feedback module; The project data acquisition and processing module is responsible for collecting basic information such as the site's topography, soil physical and mechanical parameters (such as cohesion and internal friction angle), geological structure, soil properties, and groundwater level, assessing geological disaster risks, and analyzing potential risks of landslides, collapses, and groundwater level changes; Design decision support module: select the appropriate support structure type (such as steel sheet piles, underground continuous walls, soil nail walls, anchor cables) according to geological conditions and risk assessment results, and preliminarily determine the size and material of the support structure based on empirical formulas; Numerical simulation and optimization module: Use finite element analysis software to build a three-dimensional model of the foundation pit and its surrounding environment, set boundary conditions, simulate the performance of different support schemes at different construction stages, compare safety, economy and efficiency, adjust model parameters based on on-site monitoring data, and ensure that the design meets the constraints (such as safety factor, displacement limit); Monitoring and feedback control module: Install monitoring equipment such as inclinometers and stress gauges to monitor the displacement and stress changes of the foundation pit in real time, collect monitoring data, and use machine learning algorithms to establish a foundation pit prediction model, adjust the design plan or take necessary reinforcement measures based on the monitoring data; Project construction management module: formulate a reasonable construction process to reduce the impact on the surrounding environment and residents, install monitoring equipment, regularly check the performance of the support structure and changes in the surrounding environment, and ensure that the construction process meets the design requirements and safety standards; Post-evaluation and feedback module: Evaluate the final performance of the support system, record any abnormal conditions, update the design process and construction methods based on project feedback, and improve the overall project quality and efficiency.

Claims

1. An optimization design method for foundation pit support, characterized in that: The steps include: S1. Geological and environmental survey: Collect basic data on site topography, soil cohesion, friction angle, geological structure, soil properties, and groundwater level, and assess the impact of foundation pit excavation on surrounding buildings, roads, and pipeline infrastructure; S2. Risk assessment: Analyze potential risks of landslide, collapse, and groundwater level changes, evaluate technical difficulties and safety issues encountered during construction, and formulate response measures; S3. Design concept and preliminary plan: Support system selection, based on geological conditions and risk assessment results, determine the type of support structure to be used: steel sheet piles, underground continuous wall, soil nail wall, anchor cable, preliminary size estimation, based on empirical formulas, preliminarily determine the size and material of the support structure; S4. Numerical analysis and optimization: Use finite element analysis software to establish a three-dimensional model of the foundation pit and surrounding environment, set boundary conditions: define stratum properties, support structure parameters, construction process, simulate the performance of different support schemes at different construction stages, compare safety, economy and efficiency, adjust model parameters based on on-site monitoring data, and set design constraints: safety factor, displacement limit; S5. Real-time monitoring and feedback: Install monitoring equipment during the construction process: inclinometers and stress gauges to monitor the displacement and stress changes of the foundation pit in real time, collect monitoring data, and use machine learning algorithms to establish a foundation pit prediction model based on the corresponding data, and integrate the formula ,in, is the prediction result of foundation pit quality. represents the cumulative sedimentation, represents the water level value, F is the external force, k is the comprehensive stiffness value obtained through experiments and numerical simulations, including the influence of soil properties and supporting structure factors, and the calculation results are compared and analyzed with the theoretical results according to the formula, and the design plan is adjusted according to the monitoring data, and reinforcement measures are taken when necessary; S6. Construction planning and management: construction sequence planning, formulating a reasonable construction process, installing monitoring equipment, regularly checking the performance of the support structure and changes in the surrounding environment, and monitoring the construction process to ensure that it meets design requirements and safety standards; S7. Post-evaluation and feedback: Evaluate the final performance of the support system, record any abnormalities, and update the design process and construction methods based on project feedback.

2. The optimization design method for foundation pit support according to claim 1, characterized in that: In step S4, a finite element analysis software is used to establish a three-dimensional model of the foundation pit and the surrounding environment, and boundary conditions are set: defining the stratum properties, support structure parameters, and construction process. The specific steps are as follows: S41. Establish geometric model: obtain the design drawings of the foundation pit, geological survey report, and support structure design information, and draw a three-dimensional model in the software according to the size and shape of the foundation pit, including the excavation range of the foundation pit, support structure, and surrounding soil; S42. Define material properties: Input the physical and mechanical parameters of different soil layers according to the geological report: elastic modulus, Poisson's ratio, internal friction angle, cohesion, input the elastic modulus, density, and Poisson's ratio of concrete and steel; S43. Set boundary conditions: apply displacement constraints to the bottom and sides of the model. The bottom fixes all degrees of freedom, while the sides only restrict horizontal displacement. Load conditions: ground load, groundwater pressure, and support structure load; S44. High-precision meshing: Use the software's built-in adaptive meshing function and scripting to automatically adjust the mesh density according to the gradient of stress and strain, using finer meshes near the support structure and in stress concentration areas, and coarser meshes away from these areas; S45. Intelligent boundary conditions: Based on geological reports and field monitoring data, algorithms are used to automatically adjust boundary conditions, complex geological conditions and changes in external loads. When groundwater level rise is detected, groundwater pressure is automatically updated, and the impact of climate change on groundwater levels and soil properties is dynamically adjusted. S46. Construction process simulation: According to the actual construction sequence, excavate and install the support structure step by step, set up multiple analysis steps, set the positions corresponding to the on-site monitoring points in the model, and compare the simulation results with the measured data; S47. Analysis and calculation: Run the model calculation, the software will automatically solve the stress, strain and displacement in each analysis step, view and analyze the calculation results, and use deformation cloud diagrams to represent the calculation results; S48. Result verification and adjustment: Compare the calculated results with the field monitoring data, evaluate the accuracy of the model, and adjust the model parameters according to the verification results until a satisfactory prediction effect is obtained.

3. The optimization design method for foundation pit support according to claim 1, characterized in that: In step S5, monitoring equipment is installed during the construction process: inclinometer, stress gauge, real-time monitoring of displacement and stress changes of the foundation pit, monitoring data is collected, and a foundation pit prediction model is established based on the corresponding data using a machine learning algorithm. The specific steps of adjusting the design plan based on the monitoring data are as follows; S51: Data collection: During the foundation pit construction process, inclinometers and stress gauge monitoring equipment are installed to collect displacement and stress change data of the foundation pit in real time; S52: Data preprocessing: Clean and organize the collected raw data, remove outliers and erroneous data for subsequent analysis, and extract relevant characteristic variables from the collected data according to the characteristics of the foundation pit project: time, location, depth, soil type, and support structure; S53: Model construction: Use support vector machine to establish foundation pit prediction model, integrate the formula ,in, is the prediction result of foundation pit quality. represents the cumulative sedimentation, represents the final water level value, F is the external force, k is the comprehensive stiffness value obtained through experiments and numerical simulations, which includes the influence of soil properties and supporting structure factors, and the calculation results are compared and analyzed with the theoretical results according to the formula; S54: Model training: Use existing historical data to train the model, continuously adjust model parameters, use a portion of the retained data to verify the model and check its predictive ability. When new monitoring data arrives, input it into the model to predict changes in foundation pit quality; S55: Result analysis and adjustment: Compare and analyze the model prediction results with the theoretical calculation results to check the differences between the two. When there is a large deviation between the prediction results and the theoretical calculation, it means that there are factors that have not been fully considered. At this time, the design plan needs to be adjusted: change the support structure type, adjust the construction sequence; S56: Update training and validation: Add these data to the model, retrain and validate the model, and repeat the cycle until the model prediction results are close to the theoretical calculation results and the error is within an acceptable range; S57: Monitoring and maintenance: Continuously monitor the status of the foundation pit during the construction process, update the model based on the monitoring data, and ensure the safety and stability of the foundation pit project.

4. An optimization design system for foundation pit support, characterized in that: The optimization design system for foundation pit support is used to implement the optimization design method for foundation pit support as described in any one of claims 1 to 3; The optimization design system for foundation pit support comprises: a project data acquisition and processing module, a design decision support module, a numerical simulation and optimization module, a monitoring and feedback control module, a project construction management module, and a post-evaluation and feedback module; The project data acquisition and processing module is responsible for collecting the on-site topography and soil physical and mechanical parameters: cohesion, internal friction angle, geological structure, soil properties, and groundwater level basic data, assessing geological disaster risks, and analyzing potential landslides, collapses, and groundwater level changes. Design decision support module: According to geological conditions and risk assessment results, select the type of support structure: steel sheet pile, underground continuous wall, soil nail wall, anchor cable, and preliminarily determine the size and material of the support structure based on empirical formulas; Numerical simulation and optimization module: Use finite element analysis software to build a three-dimensional model of the foundation pit and its surrounding environment, set boundary conditions, simulate the performance of different support schemes at different construction stages, compare safety, economy and efficiency, adjust model parameters based on on-site monitoring data, and design to meet constraint conditions: safety factor, displacement limit; Monitoring and feedback control module: Install inclinometers and stress gauge monitoring equipment to monitor the displacement and stress changes of the foundation pit in real time, collect monitoring data, and use machine learning algorithms to establish a foundation pit prediction model, adjust the design plan based on the monitoring data, and take reinforcement measures; Project construction management module: formulate a reasonable construction process to reduce the impact on the surrounding environment and residents, install monitoring equipment, and regularly check the performance of the support structure and changes in the surrounding environment; Post-evaluation and feedback module: Evaluate the final performance of the support system, record any abnormal conditions, update the design process and construction methods based on project feedback, and improve the overall project quality and efficiency.

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