Foundation pit finite element model parameterized rapid modeling method, system, device and medium

Abstract

The application relates to the field of civil engineering, and discloses a foundation pit finite element model parameterization rapid modeling method: a finite element model is constructed through foundation pit geometric information and soil characteristics, soil response is simulated by adopting a nonlinear constitutive relation and a random process, foundation pit stability optimization and dynamic adjustment are realized, and the application further provides a foundation pit finite element model parameterization rapid modeling system: an input module, a modeling module, a constitutive model module, a random process module, a simulation module, an optimization module, a grid refinement module and a real-time monitoring module. The application realizes simplified modeling process, improved calculation precision, accurate simulation of soil nonlinear behavior and dynamic response, and significantly improves the stability and safety in the foundation pit design and construction process by automatically generating a foundation pit finite element model, introducing a Kachanov-Delale nonlinear soil constitutive model, generating a soil elastic modulus random field based on a Gaussian process, and dynamically adjusting model parameters in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of civil engineering, in particular to a foundation pit finite element model parameterization rapid modeling method, system, device and medium. BACKGROUND

[0002] In the current foundation pit design and construction process, the establishment of finite element model usually relies on manual input and traditional modeling methods; many times, the designer needs to manually input soil properties and foundation pit geometric information according to geological exploration data, and carry out mesh division; this method not only takes time and effort, but also is easily affected by human factors, especially in the case of complex soil layers and irregular foundation pit shapes, which is easy to cause model deviation, and then affects the accuracy of the analysis result; the existing artificial modeling method cannot efficiently cope with complex design requirements, and is also easy to cause omissions in engineering design.

[0003] In addition, the existing technology relies on linear models in the selection of soil constitutive models, ignoring the nonlinear characteristics of soil under different stress states; the elastic modulus, shear modulus and other parameters of soil are affected by depth and stress during the construction process of the foundation pit, and the linear model cannot effectively describe this complex change; for example, when the depth of the foundation pit increases or encounters uneven soil layers, the nonlinear behavior of the soil is particularly obvious, and the existing technology is difficult to accurately simulate this phenomenon; this leads to a great risk in the stability and safety of the design in the actual construction process, especially when facing uncertain soil conditions, the reliability of the design scheme is greatly discounted.

[0004] Finally, the existing foundation pit model mostly relies on static data and static analysis, ignoring the dynamic changes of soil and foundation pit conditions during construction; during the construction process of the foundation pit, the physical properties of the soil, the state of the supporting structure and the construction steps may change, and the traditional finite element model often cannot reflect these changes in real time, leading to potential problems that occur during the construction process cannot be identified and solved in time; such a static model cannot adapt to the dynamic process of construction, increasing the risk and unpredictability of the project, and it is also difficult to provide real-time optimized design schemes for construction personnel; therefore, there is an urgent need for a technical solution that can dynamically update model parameters and adapt to the actual changes on the construction site; therefore, the present application proposes a foundation pit finite element model parameterization rapid modeling method, system, device and medium to solve the deficiencies of the prior art. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a foundation pit finite element model parameterization rapid modeling method, system, device and medium, which solves the problems of tedious manual modeling, insufficient simulation of soil nonlinear characteristics and inability to dynamically adjust model parameters in real time.

[0006] To achieve the above object, the application is implemented by the following technical solutions: a foundation pit finite element model parameterization rapid modeling method, comprising the following steps:

[0007] S1, input the geometric information of the foundation pit and the soil material properties, the geometric information including the shape, size, depth, slope, wall thickness, excavation sequence of the foundation pit, and the soil material properties including the elastic modulus, density, cohesion;

[0008] S2, based on the geometric information and soil material properties, construct a foundation pit finite element model and perform mesh division;

[0009] S3, adopt a nonlinear soil constitutive relation for modeling, and describe the nonlinear elastic behavior of the soil material based on the Kachanov-Delale model;

[0010] S4, introduce a random process theory, generate a random field of soil elastic modulus through a Gaussian process, and then obtain multiple random samples for simulating the foundation pit response under different soil conditions;

[0011] S5, simulate the multiple random samples, solve the foundation pit stress and displacement response parameters under different soil conditions, and perform statistical analysis to obtain the stress and displacement simulation results of the foundation pit;

[0012] S6, based on the stress and displacement simulation results of the foundation pit, define a foundation pit stability optimization objective function, the optimization objectives including minimizing the stress and displacement parameters, improve the foundation pit stability through a multi-scale optimization method, minimize the deformation and stress concentration, and obtain the optimized stress distribution and displacement field optimization results;

[0013] S7, based on the optimized stress distribution and displacement field optimization results, determine the high stress area and perform mesh refinement to increase the density of mesh division;

[0014] S8, according to the real-time monitoring data in the foundation pit construction process, the monitoring data being obtained through sensors, perform model parameter dynamic adjustment to optimize the finite element model of the foundation pit.

[0015] The application also provides a foundation pit finite element model parameterization rapid modeling system, comprising:

[0016] An input module for inputting the geometric information of the foundation pit and the soil material properties;

[0017] A modeling module for automatically generating a finite element model and performing mesh division according to the input information;

[0018] A constitutive model module for adopting the Kachanov-Delale model for nonlinear soil constitutive modeling;

[0019] A random process module generates a soil elastic modulus random field based on a Gaussian process and performs simulation;

[0020] A simulation module calculates the responses of the foundation pit under different soil conditions and performs statistical analysis;

[0021] An optimization module minimizes stress and displacement through a multi-scale optimization method;

[0022] A mesh refinement module performs mesh refinement on high stress areas based on the optimization results;

[0023] A real-time monitoring module acquires construction data in real time through sensors and dynamically adjusts model parameters.

[0024] The application also provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method as described above when executing the computer program.

[0025] The application also provides a storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the method as described above.

[0026] The application provides a foundation pit finite element model parameterization fast modeling method, system, device and medium.

[0027] Advantages:

[0028] 1. The application adopts a technical scheme of automatically generating a foundation pit finite element model, achieving the technical effects of greatly simplifying the modeling process and improving the calculation efficiency; unlike the manual input and complex mesh division method in the prior art, the application generates a model through an automatic process, significantly shortens the modeling time, avoids the mistakes of manual operation, reduces the possibility of errors, and greatly improves the work efficiency and model accuracy.

[0029] 2. The application introduces a Kachanov-Delale nonlinear soil constitutive model to more accurately simulate the nonlinear behavior of soil under complex stress environment; compared with the linear soil model commonly used in the prior art, the application can more truly reflect the elastic-plastic deformation characteristics of soil, especially in high stress areas, and avoids the shortcomings of traditional methods that ignore the nonlinear characteristics of soil, thereby improving the reliability of foundation pit analysis and design.

[0030] 3. The application generates a random field of soil elastic modulus based on a Gaussian process, achieving the technical effect of fully considering soil spatial variability and uncertainty; in the prior art, soil properties are often assumed to be uniformly distributed, but soil characteristics are actually highly heterogeneous, and the application accurately simulates the spatial distribution of soil elastic modulus through random field technology, which can more comprehensively reflect the soil behavior under actual working conditions and enhance the prediction accuracy of the response of the foundation pit.

[0031] 4. The application achieves the effect of adaptive adjustment of the model during construction by dynamically adjusting the parameters of the finite element model in real time; compared with the scheme of a static and unchanged foundation pit model in the prior art, the application can obtain monitoring data in real time during construction and automatically adjust the key parameters in the finite element model to respond to the dynamic influence of soil and environmental changes; this technical scheme ensures the stability of the foundation pit during the entire construction period and can quickly respond to any abnormal conditions that occur during construction, improving safety and construction flexibility. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a method flowchart of the application;

[0033] Figure 2 is a system architecture diagram of the application;

[0034] Figure 3 is a computer device structure schematic diagram of the application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the application will be described below with reference to the drawings in the specification of the application. Obviously, the described embodiments are only some of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the application.

[0036] Please refer to Figure 1 The embodiments of the application provide a foundation pit finite element model parameterized rapid modeling method, system, device and medium, which comprises the following steps:

[0037] S1, input the geometric information and soil material properties of the foundation pit, the geometric information including the shape, size, depth, slope, wall thickness and excavation sequence of the foundation pit, and the soil material properties including the elastic modulus, density and cohesion;

[0038] In this embodiment, when inputting the geometric information of the foundation pit, it is first necessary to determine the geometric shape of the foundation pit; specifically, the shape of the foundation pit can be rectangular, circular, elliptical or other common geometric shapes, and the specific shape is determined by design requirements and actual site conditions; the size of the foundation pit includes the length, width and depth of the foundation pit, all of which will determine the scale of the foundation pit and its carrying capacity; for example, for a rectangular foundation pit, the length and width of the foundation pit need to be input; for a deep foundation pit, the depth information also needs to be determined.

[0039] In some embodiments, the slope of the foundation pit is also an important parameter that needs to be considered; the slope of the foundation pit determines the angle between the foundation pit wall and the horizontal plane, and is usually closely related to the stability of the foundation pit and the construction difficulty; for a foundation pit with a large slope, the risk of soil sliding is greater, so the accurate input of the slope is crucial for risk assessment.

[0040] Specifically, in step S1, the wall thickness of the foundation pit also needs to be input; the wall thickness is usually related to the design of the supporting structure of the foundation pit, and for a finite element model, the wall thickness directly affects the stiffness of the supporting structure and its resistance to deformation of the foundation pit; after inputting the wall thickness information, the model will calculate the boundary conditions of the foundation pit according to the data and reflect the influence of the supporting structure.

[0041] In addition, the excavation sequence of the foundation pit is also very important; the excavation sequence will affect the stress state of the soil in the foundation pit, so it is necessary to clearly input the excavation depth of each stage and the corresponding change of soil mechanical properties; in actual construction, the optimization of the excavation sequence has a significant effect on the stability of the foundation pit, and a reasonable excavation sequence can effectively reduce the risk of deformation and inclination of the foundation pit during construction.

[0042] For the soil material properties part, in this embodiment, the elastic modulus is a key parameter that describes the deformation ability of soil material within the elastic range; the size of the elastic modulus directly affects the deformation behavior of the soil under stress; in the model, the elastic modulus is represented by the symbol E, and the unit is usually MPa or GPa; the elastic modulus varies with the type and water content of the soil, so the corresponding value needs to be input according to the different types of soil.

[0043] As an option, the density of the soil is also one of the input parameters; generally, the density of the soil p will affect the self-weight of the foundation pit and the stress generated thereby; different types of soil (such as sand and clay) have different density ranges; in general, the density of the soil is obtained through experimental testing or engineering experience; the common soil density value is approximately between 1.5-2.0 g / cm 3 .

[0044] In addition, the cohesion c of the soil is the strength of the interaction between soil particles, which plays an important role in landslide and soil stability analysis; the value of the cohesion is usually obtained from actual test data of the soil or through empirical formula.

[0045] In one possible implementation, the elastic modulus E, the density p and the cohesion c of the soil are used as the basic parameters of the material constitutive relation in the finite element model; by inputting these parameters, the system can automatically combine these material properties with the foundation pit geometric information to generate a preliminary finite element model.

[0046] Specifically, the material constitutive relation is based on the Kachanov-Delale model, which describes the nonlinear elastic behavior of soil material through the following formula:

[0047] σ(ε)=E(ε)·ε+α·H(ε)·ε n ;

[0048] wherein σ(ε) is the stress, ε is the strain, E(ε) is the elastic modulus, α is the hardening parameter, H(ε) is the hardening function, and n is the hardening index; this formula reflects the nonlinear response of the soil under the action of strain and stress; by introducing this model, the soil response of the foundation pit under different loads can be more accurately simulated, thereby providing more accurate data support for subsequent stress analysis, displacement calculation, etc.

[0049] Through these series of input parameters, the model will be able to comprehensively and accurately reflect the structural form of the foundation pit and the soil properties, laying a solid foundation for subsequent meshing, simulation calculation and optimization design.

[0050] S2, based on the geometric information and soil material properties, constructing a foundation pit finite element model and performing meshing;

[0051] In this embodiment, first, based on the foundation pit geometric information input in step S1, the system will generate a three-dimensional finite element model consistent with the actual foundation pit form; the construction process of this model involves mapping the geometric shape of the foundation pit into the computer, so that it becomes a mathematical model that can be used in finite element analysis software; specifically, the model will form a polyhedral geometric structure in three-dimensional space according to the length, width, depth and other parameters of the foundation pit, wherein the slope and wall thickness and other characteristics of the foundation pit will be reflected through node coordinates and element connection mode.

[0052] Specifically, when constructing the finite element model, special consideration needs to be given to the soil layer distribution of the foundation pit; for example, the thickness, type, and physical and mechanical properties (such as elastic modulus, cohesion, etc.) of the soil layer will be divided into multiple different elements; in some embodiments, the system will automatically divide the soil layer elements according to the excavation sequence of the foundation pit, establishing separate finite element grids in the soil of each layer to reflect the different physical properties of the soil at different depths; the hierarchical division of the soil not only affects the stress distribution of the foundation pit, but is also closely related to the changes in soil response at different stages of the construction process, so this process needs to be accurately divided.

[0053] In some embodiments, the construction of the foundation pit finite element model will fully consider the role of supporting structures (such as pile foundations, support walls, etc.) and accurately reproduce the stiffness and strength of the supporting structures in the model; these supporting structures will interact with the soil layer to transfer the stress state of the foundation pit; the stiffness and strength of the supporting structures can be defined by inputting the geometric parameters of the structures (such as the diameter, length, position, etc. of the pile foundation) and modeling the supporting structures according to actual construction requirements and design parameters.

[0054] As an option, special constraint conditions can be introduced during the construction of the finite element model to simulate the actual constraints in the construction of the foundation pit; for example, the sidewall during construction may be subjected to extrusion by the soil, so the deformation and lateral constraint conditions of the foundation pit wall need to be considered; in addition, the excavation sequence will also be reflected in the model by setting different boundary conditions, ensuring that the mechanical response of each excavation stage accurately reflects the deformation and stability of the foundation pit.

[0055] In one possible implementation, after the foundation pit finite element model is completed, the next step is to enter the mesh division stage; mesh division is an important step in finite element analysis and can affect the accuracy and computational efficiency of subsequent analysis; during mesh division, the system will automatically generate an adaptive mesh division scheme based on the input foundation pit geometry, soil material properties, and supporting structures; the density of the mesh is usually more dense in areas with high stress concentration (such as the bottom of the foundation pit or the contact area of the supporting structures), while the mesh is sparser in areas with relatively flat stress changes.

[0056] Specifically, the fineness of the mesh division will be adaptively adjusted according to the needs of different areas; in high-stress areas, the size of the mesh will be refined to ensure accurate calculation of stress changes; while in low-stress areas, coarser meshes can reduce computational load and improve efficiency; during this process, the system automatically selects appropriate mesh sizes based on the soil properties of the foundation pit (such as elastic modulus, density, etc.) and external load conditions.

[0057] Generally, the meshing adopts three-dimensional hexahedral or tetrahedral elements, which are usually suitable for complex geometries and irregular boundary conditions; in some embodiments, for a foundation pit with relatively simple geometry, it is more efficient to use a three-dimensional rectangular mesh; for complex excavation sequences and soil layer structures, it may be necessary to combine three-dimensional tetrahedral elements and hexahedral elements for mixed use to ensure the balance between calculation accuracy and efficiency.

[0058] After meshing, the system will generate a finite element model containing node, element, boundary condition, material property, etc. information; the material properties of each mesh element will be based on the aforementioned input soil material properties, such as elastic modulus, density and cohesion, etc. Through this process, the mechanical behavior of the foundation pit is accurately discretized into a numerical calculation problem, ready for subsequent stress, displacement, etc. simulation analysis.

[0059] As an option, meshing can also take into account the transition area between different soil layers, i.e. where the soil type changes significantly, usually where stress concentration is more pronounced; by refining the mesh in these transition areas, the impact of soil type changes can be more accurately reflected, thereby improving the accuracy of the calculation results.

[0060] S3, adopt nonlinear soil constitutive relation for modeling, and describe the nonlinear elastic behavior of soil material based on Kachanov-Delale model;

[0061] In this embodiment, the Kachanov-Delale model provides a method for describing the elastic behavior of soil material by introducing a nonlinear relationship between stress and strain; the Kachanov-Delale model takes into account the change of soil elastic modulus E(ε) with strain ε, and introduces hardening parameter σ and hardening function E(ε) when considering nonlinear strain hardening behavior; the basic formula of the model is:

[0062] σ(ε) = E(ε) · ε + α · H(ε) · ε n ;

[0063] Where σ(ε) is the stress, ε is the strain, E(ε) is the elastic modulus, α is the hardening parameter, H(ε) is the hardening function, and n is the hardening index; the elastic modulus E(ε) changes with the increase of strain, and the nonlinear characteristics of soil are fully described by the hardening function H(ε).

[0064] Specifically, when applying the Kachanov-Delale model, the selection of the elastic modulus E(ε) and the hardening parameter α is crucial for the performance of the soil material; the elastic modulus reflects the stiffness of the soil within the elastic range, and the hardening parameter α describes the degree of hardening of the soil material when it undergoes plastic deformation; the hardening function H(ε) is used to simulate the hardening behavior of the soil, i.e., the change in the stress-strain relationship of the soil when it is subjected to stress.

[0065] In some embodiments, the hardening index n typically takes a value between 1 and 2, and the specific value is determined according to the soil type and experimental data; for cohesive soil, its hardening behavior may be more pronounced, while for loose sandy soil, it may exhibit weaker hardening behavior; therefore, the selection of the hardening parameter will affect the performance of the model under different soil conditions.

[0066] In general, when the Kachanov-Delale model is applied to the soil model of a foundation pit, the characteristics of each layer of soil can be considered; for example, at different depths of the foundation pit, due to differences in soil type and compaction degree, the performance of the elastic modulus and hardening behavior will vary; therefore, the elastic modulus E(ε) and the hardening parameter α of the soil will be defined according to the physical properties of different soil layers.

[0067] As an option, to improve the accuracy of the model, more complex nonlinear constitutive models can also be considered to simulate the mechanical behavior of specific soil types; for example, the Mohr-Coulomb criterion can be used to describe the failure criterion of the soil, or more material constants can be introduced to adapt to different soil responses.

[0068] In one possible implementation, by applying the Kachanov-Delale model in the finite element model, the nonlinear behavior of the soil under loading can be simulated; this enables the foundation pit analysis not only to reflect linear elastic behavior, but also to consider the nonlinear elastic response of the soil under large deformation conditions; this is of great significance for the stability analysis of the foundation pit under deep excavation or complex construction conditions.

[0069] When calculating stress, the Kachanov-Delale model can provide more accurate constitutive behavior of soil material, especially under large deformation and high stress conditions; when analyzing the foundation pit, by inputting the corresponding soil elastic modulus, hardening parameter, hardening function, and hardening index, the model can accurately simulate the soil response at different construction stages, ensuring the reliability of the calculation results.

[0070] Specifically, the nonlinear elastic behavior of soil is particularly important in the process of foundation pit construction; for example, during the excavation of the foundation pit, the deformation of the soil will change significantly with the change of depth and load; through the Kachanov-Delale model, the stress-strain relationship of the soil under gradual loading can be more accurately described, providing a more realistic physical basis for the stability evaluation of the foundation pit.

[0071] S4, introduce the random process theory, generate the random field of soil elastic modulus through Gaussian process, and obtain multiple random samples for simulating the response of foundation pit under different soil conditions;

[0072] In this embodiment, the random field of soil elastic modulus is generated through Gaussian process; Gaussian process is a commonly used random process, which can describe the correlation of soil elastic modulus values between different positions through covariance function; specifically, the soil elastic modulus of the foundation pit area is regarded as a spatial random variable, and Gaussian process can generate multiple samples to simulate different soil conditions.

[0073] Specifically, the key to generating the random field of soil elastic modulus is to define a suitable covariance function; generally, the covariance function has the following form:

[0074]

[0075] where E(x) and E(y) are the soil elastic modulus values at positions x and y, respectively, σ 2 is the variance of soil elastic modulus, indicating the variability of elastic modulus; is the correlation length, indicating the variability of soil properties in space, the larger indicates that the soil property changes slowly, and the smaller indicates that the change is more dramatic, and |x-y| is the spatial distance between positions x and y.

[0076] As an option, by adjusting the variance σ 2 and the correlation length of the covariance function, the spatial variability of soil elastic modulus can be controlled; for example, when is large, it means that the soil elastic modulus changes slowly in space; while a smaller value indicates that the soil elastic modulus changes more dramatically in space; this method allows the spatial heterogeneity of soil to be fully considered in the model.

[0077] Generally, the soil elastic modulus random field generated by the Gaussian process will be used to reflect the actual physical state of the soil of the foundation pit, especially the soil elastic modulus values of different soil layers; in the model, these different soil conditions are simulated by multiple random samples, so that each sample represents a possible soil property distribution, thereby comprehensively simulating the response of the foundation pit.

[0078] In a possible implementation, after generating multiple random samples, the finite element analysis of the foundation pit will be calculated based on these samples; each sample will be input into the finite element model and corresponding stress and displacement calculations will be performed; by simulating multiple samples, the foundation pit response data under different soil conditions can be obtained and statistical analysis can be performed; these statistical data (such as mean, standard deviation, etc.) can be used to evaluate the stability of the foundation pit under different soil conditions and provide data support for subsequent design optimization.

[0079] Specifically, each random sample will represent a different soil elastic modulus distribution; during the finite element simulation process, these samples will correspond to different soil conditions respectively, and thus different foundation pit stress and displacement responses will be obtained; by simulating multiple times, a set of foundation pit response results under different soil conditions can be obtained, and statistical analysis can be performed.

[0080] As an option, these different soil elastic modulus random field samples can be used for further foundation pit stability analysis; for example, the maximum stress, maximum displacement and other key performance indicators of the foundation pit under different soil conditions can be calculated, so as to identify potential risk areas; in addition, these simulation results can be compared to help determine the optimal foundation pit design scheme.

[0081] S5, simulating the multiple random samples, solving the foundation pit stress and displacement response parameters under different soil conditions, and performing statistical analysis to obtain the stress and displacement simulation results of the foundation pit;

[0082] In this embodiment, first, each random sample is simulated and calculated by finite element analysis; each sample represents a different soil elastic modulus distribution, and these distributions are based on the generation rules of the aforementioned Gaussian process, so each sample reflects the mechanical response of the foundation pit under specific soil conditions; during the simulation process, important response parameters such as stress and displacement of the foundation pit are solved, and these parameters are crucial for evaluating the stability of the foundation pit.

[0083] When performing the simulation, the soil elastic modulus in the foundation pit model will change with each random sample; stress calculation is based on the constitutive relationship of the material, especially the stress-strain relationship in the Kachanov-Delale model; through simulation calculation under different soil conditions, the system will be able to obtain the stress and displacement response of the foundation pit under different sample conditions; specifically, the stress and displacement distribution of each sample will be solved by the finite element method, so as to obtain important parameters such as the maximum stress, maximum displacement, displacement field, etc. of the foundation pit under each soil condition.

[0084] As an option, in these simulation calculations, the influence of external loads on the foundation pit may also be considered, such as construction loads, changes in surrounding soil pressure, etc., which will affect the response of the foundation pit; therefore, these external load factors can be considered in the calculation to improve the authenticity of the simulation results.

[0085] Generally, the simulation results of each random sample will include complete stress and displacement distribution graphs; these graphs show the mechanical response of the foundation pit under different soil conditions, providing a data basis for subsequent optimization analysis and safety evaluation; the simulation results not only provide the response under a single soil condition, but also help designers and engineers evaluate the overall stability of the foundation pit by comparing the performance under different soil samples.

[0086] In one possible implementation, after simulating multiple random samples, the system will perform statistical analysis on the simulation results; this process includes calculating the mean, standard deviation, maximum value, minimum value, etc. of the stress, displacement, etc. response of each sample under different soil conditions; through these statistics, the deformation range and stress concentration area of the foundation pit under different soil conditions can be analyzed in depth, so as to further judge the reliability of the foundation pit under various soil conditions.

[0087] Specifically, through statistical analysis, we can obtain the probability distribution of the stress and displacement of the foundation pit, which can help evaluate the performance of the foundation pit under different soil conditions; for example, it may be found that the stress concentration is larger and the displacement is larger under certain soil conditions, while the performance is more stable under other soil conditions; in this way, engineers can clearly identify the potential risks of the foundation pit under different soil conditions and take appropriate design improvement measures.

[0088] As an option, in the analysis process, statistical methods such as Monte Carlo simulation may also be used to further evaluate the response uncertainty of the foundation pit under different soil conditions; through simulation of a large number of random samples, Monte Carlo simulation can provide more comprehensive performance evaluation of the foundation pit and help develop safer design schemes.

[0089] S6, based on the stress and displacement simulation results of the foundation pit, define a stability optimization objective function of the foundation pit, the optimization objective including minimizing stress and displacement parameters, improve the stability of the foundation pit through a multi-scale optimization method, minimize deformation and stress concentration, and obtain optimized stress distribution and displacement field optimization results;

[0090] In this embodiment, first, based on the stress and displacement response data obtained in the aforementioned step S5, we define a stability optimization objective function of the foundation pit; the objective function mainly consists of two parts: one is stress minimization, and the other is displacement minimization; the optimization objective of the stability of the foundation pit is not only to reduce the absolute values of stress and displacement, but also to avoid stress concentration and large-scale displacement in space to ensure the overall safety and long-term stability of the foundation pit structure.

[0091] Specifically, the optimization objective function can be expressed as:

[0092] F = λ1·∫σ 2 (x)dV + λ2·∫ε 2 (x)dV;

[0093] Where σ(x) is the stress of a point x in the foundation pit, ε(x) is the displacement of the point, λ1 and λ2 are weight coefficients representing the relative importance of stress and displacement; dV is the volume element, and the integral calculation is performed on all regions of the foundation pit model; by minimizing the objective function F, we can optimize the stress and displacement simultaneously to ensure the stability of the foundation pit under different soil conditions.

[0094] Generally, in the multi-scale optimization method, the optimization of the foundation pit will be divided into two levels: local optimization and global optimization; local optimization mainly targets local areas of the foundation pit, such as stress concentration areas and areas with large deformation, etc., which usually need to be refined; while global optimization considers the stress and displacement distribution of the whole foundation pit, optimizing the design of the entire foundation pit to make it perform better in different construction and load conditions.

[0095] As an option, the focus of local optimization is usually on high-stress areas and deformation-concentrated areas; in these areas, the system will reduce local stress and displacement by refining the grid, enhancing material stiffness, or optimizing support structure design; for example, if the stress in a certain area is too large, the stress concentration can be reduced by increasing the support strength of that area or changing the material distribution.

[0096] In one possible implementation, through the multi-scale optimization method, the global stress and displacement of the foundation pit are first optimized, and on this basis, the local stress-concentrated areas are refined and optimized; this approach can effectively balance the calculation accuracy and efficiency, avoiding excessive waste of computing resources.

[0097] In particular, during the optimization process, we consider the overall stress distribution and displacement field of the foundation pit; for each optimization step, the system will calculate based on the stress and displacement simulation results, gradually adjust the design parameters of the foundation pit (such as the stiffness of the supporting structure, grid density, soil elastic modulus, etc.), so that the stress and displacement response of the foundation pit is improved in the global range; after each optimization iteration, the stress and displacement are recalculated and compared with the previous calculation results to ensure the convergence of the optimization target.

[0098] Generally, in the optimization process, not only the stress and displacement should be reduced, but also the extreme value of stress and excessive value of displacement should be avoided; especially for those stress concentration areas that may lead to the destruction of the foundation pit, through optimization design, we can effectively reduce the stress level of these high-risk areas, and thus improve the safety of the foundation pit.

[0099] As an option, the optimization results not only include the stress and displacement response of the foundation pit, but also the optimized material distribution, supporting structure design, etc.; these results provide the basis for subsequent construction and monitoring, and can be adjusted and implemented according to the optimization design.

[0100] S7, based on the optimized stress distribution and displacement field optimization results, determine the high stress area, and perform grid refinement, increase the grid density in these areas, so as to improve the calculation accuracy;

[0101] In this embodiment, first, the stress distribution and displacement field optimized in step S6 will be used as input data; these optimization results reflect the stress and displacement state of the foundation pit under different soil conditions; in particular, the stress distribution map and displacement field in the optimization results can clearly identify the areas with high stress (i.e. high stress areas) and areas where large deformation may occur in the foundation pit.

[0102] In particular, by analyzing the optimized stress distribution, the system can identify the high stress areas in the foundation pit; these areas are usually the most critical part of the foundation pit design, because stress concentration may lead to structural damage or soil instability; in the finite element model of the foundation pit, high stress areas are often caused by soil or structural defects, material non-uniformity or uneven loading, etc.

[0103] As an option, when determining the high stress area, a stress threshold can be set, when the stress of a certain area exceeds this threshold, it is considered that the area is a high stress area; the selection of this threshold can be based on engineering experience or according to the specific design requirements; for example, a reference stress value can be set, any stress area exceeding this value will be considered as a high stress area and enter the next step of grid refinement process.

[0104] Generally, for these high-stress areas, the system will perform a mesh refinement operation; this means that the original mesh division will be densified in these high-stress areas, increasing the node density and element number of the mesh; this refinement allows the finite element model to more accurately describe the stress and displacement response of the soil and structure in these areas, avoiding calculation errors due to coarse mesh.

[0105] In one possible implementation, the mesh refinement operation not only increases the mesh density in high-stress areas, but also further optimizes the calculation accuracy by adjusting the shape and size of the mesh; for example, in some complex areas, it may be necessary to divide the mesh into more tetrahedral or hexahedral elements to ensure that parts with large stress changes are adequately calculated.

[0106] Specifically, the refined mesh areas will more accurately simulate stress concentration phenomena, making the calculation results closer to reality; in these refined areas, smaller mesh elements will be used to capture higher-precision stress gradients; this method can effectively avoid calculation errors caused by coarse mesh, ensuring the accuracy of the calculation results.

[0107] As an option, local optimization design can also be combined during the mesh refinement process to further improve accuracy; for example, after mesh refinement in high-stress areas, the strength of the support structure may need to be locally enhanced to better interact with the soil; these local optimizations can be achieved by increasing soil stiffness or improving support design, thereby further optimizing the stability of the foundation pit.

[0108] S8. Based on real-time monitoring data during the construction of the foundation pit, the monitoring data is obtained through sensors, and the finite element model of the foundation pit is dynamically adjusted and optimized;

[0109] In this embodiment, sensors are placed at key locations of the foundation pit, including support structures, retaining piles, foundation pit bottoms, and surrounding soil layers; data is collected in real time through a sensor network, and these data are transmitted to a data processing center through wireless or wired means; the following is a detailed introduction of the sensors and data collection targets:

[0110] Displacement sensor: used to measure the horizontal and vertical displacement of the foundation pit soil or structure; common types of displacement sensors include laser displacement sensors, LVDT (Linear Variable Differential Transformer), and fiber optic displacement sensors; measured data includes the horizontal displacement of the support structure, settlement, and lateral displacement of the soil.

[0111] Stress sensor: used to monitor the stress state of the soil or structure in the foundation pit in real time, especially the axial force, bending moment, shear force, etc. of the support structure; common sensors include strain gauge sensors, fiber optic strain sensors, etc.

[0112] Temperature Sensor: Used to monitor the temperature changes of the foundation soil, helping to analyze the influence of groundwater level changes and seasonal temperature fluctuations on soil properties; common sensors include thermocouples, RTD (Resistance Temperature Detector), etc.

[0113] Pore Water Pressure Sensor: Used to monitor the changes in pore water pressure of the foundation soil; common sensor types include capacitive, piezoelectric, and optical fiber pore water pressure sensors.

[0114] Vibration Sensor: Used to detect vibrations around the foundation pit, monitor possible vibrations during construction, mechanical equipment-induced impacts, external load changes, etc.; common vibration sensors include MEMS accelerometers, piezoelectric sensors, etc.

[0115] After data collection, the sensor data will be filtered, denoised, and abnormal data will be removed by the signal processing unit to ensure that the data transmitted to the processing system is accurate and reliable.

[0116] Through real-time monitoring data, the parameters of the finite element model can be dynamically adjusted; common dynamic adjustment methods include least squares optimization, Kalman filtering, and machine learning methods, etc.

[0117] Least Squares Optimization Method: The core goal of minimizing errors is to dynamically adjust model parameters; in the finite element model, the calculated stress and displacement usually have certain errors compared to the actual data collected by the sensor; the least squares optimization method adjusts the model parameters by minimizing the error between the monitoring data and the model calculation results.

[0118] The specific optimization objective function is as follows:

[0119]

[0120] Where E is the error function, representing the difference between the actual observation data and the finite element calculation results, and the goal of optimization is to minimize this error function; N is the total number of monitoring data points, i.e. the number of monitoring data collected by the sensor; w i is the weight coefficient, usually used to weight the error contribution of different measuring points; different measuring points may have different importance or measurement accuracy, so weights are used to adjust the priority in error calculation; is the observation data (such as actual measurement values of displacement, stress, etc.) collected by the i-th measuring point (sensor); is the predicted data of the i-th measuring point calculated by the finite element model, which depends on the model parameters p These parameters are continuously adjusted during the optimization process to make the finite element model match the actual monitoring data; p is the model parameter vector, which contains the soil mechanical parameters that need to be optimized in the finite element model, such as elastic modulus E, Poisson's ratio v, internal friction angle φ, cohesion c, etc.

[0121] The method optimizes the finite element model by iteratively adjusting the parameter p to minimize the error between the calculated results and the actual monitoring data.

[0122] Kalman Filter: To improve the adaptive ability of the model, the Kalman filter method is applied to dynamically update the model parameters; Kalman filter combines sensor measurement data and finite element prediction data, effectively eliminates noise, and real-time corrects the parameters in the model, especially suitable for processing sensor data with noise.

[0123] The basic formula of Kalman filter is as follows:

[0124]

[0125] P k =(I-K k H k )P k-1 ;

[0126] Where, is the state estimation at the current time k; is the state estimation at the previous time k-1; K k is the Kalman gain, a weighting coefficient used to balance the prediction error and observation error; y k is the observation value at the current time k; H k is the observation matrix, describing how to associate the current state with the observation data; P k is the covariance matrix at the current time k, indicating the size of the estimation error; P k-1 is the covariance matrix at the previous time k-1, indicating the error covariance of the previous time estimation; I is the identity matrix, ensuring the consistency of the dimension in the state update process.

[0127] Machine Learning Method: In addition to traditional optimization methods and filtering methods, machine learning methods can also be used to improve the adjustment accuracy of finite element model parameters; for example, by using neural networks or support vector machine (SVM) methods, the system can train data-driven models based on historical monitoring data and construction cases to predict the trend of soil parameters.

[0128] In this method, first, train the model based on historical data, learn the relationship between monitoring data and soil parameters, predict the changes that may occur in the future construction process, and adjust the model parameters in advance.

[0129] Steps of parameter correction process:

[0130] Data acquisition and preprocessing: Obtain monitoring data from sensors and perform denoising, outlier removal and filtering to ensure data accuracy and reliability.

[0131] Error Analysis: Calculate the error between the monitoring data and the finite element calculation results, identify the key areas with larger errors; through error analysis, determine the parameters that need to be adjusted to improve the model accuracy.

[0132] Parameter Optimization: Adjust the model parameters based on optimization methods such as least squares, Kalman filtering or machine learning; in the optimization process, the system iteratively updates the parameters to minimize the error between the calculation results and the monitoring data.

[0133] Model Update: Input the optimized parameters into the finite element model, recalculate the stress, displacement and other responses of the foundation pit, and verify the model accuracy.

[0134] Multiple Iterations and Accuracy Verification: Generally, the calculation results after model adjustment need to go through multiple iterations to ensure that the error gradually converges; in each round of optimization, the system will adjust the parameters in the model and update the calculation results until the error is reduced to below the preset threshold, indicating that the model has successfully matched the monitoring data.

[0135] Nonlinear Constitutive Relationship and Complex Geological Conditions: In some complex geological conditions, such as soft soil foundation or areas with abundant groundwater, the model needs to combine nonlinear constitutive relationships, such as the Modified Cam-Clay Model, to accurately describe the nonlinear deformation characteristics of cohesive soil; these nonlinear constitutive models can more accurately simulate the stress-strain characteristics of soil, ensuring that the stability analysis of the foundation pit is more reliable.

[0136] The foundation pit finite element model parameterization rapid modeling system described below can be mutually corresponding to the foundation pit finite element model parameterization rapid modeling method described above.

[0137] Please refer to Figure 2 , the foundation pit finite element model parameterization rapid modeling system, comprising:

[0138] Input module, the input module is responsible for obtaining the geometric information and soil material characteristics of the foundation pit from the user; the geometric information includes the size, shape of the foundation pit and the position and size of the supporting structure, etc., while the soil material characteristics cover important physical and mechanical parameters such as the elastic modulus, Poisson's ratio, friction angle and cohesion of the soil; these input information provides basic data for the subsequent automatic generation of the model, and is the starting point of the entire modeling process;

[0139] The modeling module automatically generates a finite element model of the foundation pit based on the input geometric information and soil material properties. This module establishes the initial framework of the model by determining the types of nodes and elements and the boundary conditions. At the same time, the module also performs meshing to divide the entire foundation pit area into multiple small finite elements to ensure the accuracy and efficiency of the calculation. The quality of meshing directly affects the calculation accuracy and stability of the finite element analysis.

[0140] The constitutive model module uses the Kachanov-Delale model for nonlinear soil constitutive modeling. This model can effectively describe the nonlinear behavior of soil under different stress states, especially considering the progressive damage characteristics of soil during plastic deformation. By introducing a damage variable, the Kachanov-Delale model can more accurately simulate the mechanical response of soil, especially in high stress regions and during soil deformation.

[0141] The random process module generates a random field of soil elastic modulus based on the Gaussian process and performs simulation. The key of this module is to describe the spatial variability of soil through statistical methods, especially the random distribution of soil elastic modulus in the foundation pit area. The random field generated through simulation can reflect the influence of different soil conditions on the response of the foundation pit and provide more accurate soil parameters for subsequent finite element analysis.

[0142] The simulation module is used to calculate the response of the foundation pit under different soil conditions and perform statistical analysis. Based on the finite element model, combined with the input soil properties and constitutive model, the module solves the stress, displacement, settlement, and other responses of the foundation pit through numerical calculation. During simulation, the module considers different soil types, groundwater levels, and external loads, etc. influencing factors, and obtains the distribution of foundation pit responses under various environments through statistical analysis to provide a basis for optimization and decision-making.

[0143] The optimization module minimizes the stress and displacement of the foundation pit through multi-scale optimization methods. The goal of this module is to minimize the stress concentration and displacement deformation of the foundation pit by adjusting the soil material properties or supporting structure design to ensure safety and stability during construction. Multi-scale optimization methods can optimize at different scales (such as local regions and overall regions) to balance calculation accuracy and cost while meeting engineering design and construction requirements.

[0144] The mesh refinement module refines the mesh in high stress areas based on the optimization results. After optimization analysis, some areas may show greater stress concentration or stronger displacement response, which usually requires higher calculation accuracy. The mesh refinement module refines the mesh in these areas based on the optimization results to improve the accuracy of finite element analysis and make the analysis results more accurate and reliable.

[0145] A real-time monitoring module, which acquires construction data in real time through sensors and dynamically adjusts model parameters; the module integrates various sensors (such as displacement sensors, stress sensors, pore water pressure sensors, etc.), and acquires data such as stress, displacement, settlement, etc. in the process of foundation pit construction in real time; the monitoring data will be fed back to the model, and the model parameters will be dynamically adjusted in real time to ensure that the model can accurately reflect the actual state in the construction process, and the parameters will be corrected according to the changing conditions to optimize the construction strategy of the foundation pit.

[0146] The system of the embodiment can be used to execute the above-mentioned method embodiments, and has similar principles and technical effects, which will not be described here again.

[0147] Please refer to the accompanying Figure 3 The application also provides a computer device, comprising a processor and a memory, the memory storing a computer program executable by the processor, and the computer program is executed by the processor to perform the above method.

[0148] The application also provides a storage medium, which stores a computer program, and the computer program is executed by the processor to perform the above method.

[0149] The storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0150] Although the embodiments of the application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the application, and the scope of the application is defined by the appended claims and their equivalents.

Claims

1. A parametric rapid modeling method for finite element models of foundation pits, characterized in that, Includes the following steps: S1. Input the geometric information and soil material properties of the foundation pit. The geometric information includes the shape, size, depth, slope, wall thickness, and excavation sequence of the foundation pit. The soil material properties include elastic modulus, density, and cohesion. S2. Based on the geometric information and soil material properties, construct a finite element model of the foundation pit and perform mesh generation; S3. The nonlinear soil constitutive relation is used for modeling, and the nonlinear elastic behavior of soil materials is described based on the Kachanov-Delale model. S4. Introduce stochastic process theory, generate a stochastic field of soil elastic modulus through Gaussian process, and then obtain multiple random samples to simulate the foundation pit response under different soil conditions; S5. Simulate multiple random samples to solve the stress and displacement response parameters of the foundation pit under different soil conditions, and perform statistical analysis to obtain the stress and displacement simulation results of the foundation pit. S6. Based on the stress and displacement simulation results of the foundation pit, define the objective function for foundation pit stability optimization. The optimization objectives include minimizing stress and displacement parameters. Improve the stability of the foundation pit through multi-scale optimization methods, minimize deformation and stress concentration, and obtain the optimized stress distribution and displacement field optimization results. S7. Based on the optimized stress distribution and displacement field optimization results, identify high-stress regions and perform mesh refinement to increase the mesh density; S8. Based on real-time monitoring data during the foundation pit construction process, the monitoring data is acquired through sensors, and the model parameters are dynamically adjusted to optimize the finite element model of the foundation pit.

2. The parametric rapid modeling method for foundation pit finite element models according to claim 1, characterized in that, The soil constitutive relationship is based on the Kachanov-Delale model, which is described by the following formula: σ(ε)=E(ε)·ε+α·H(ε)·ε n ; Where σ(ε) is stress, ε is strain, E(ε) is elastic modulus, α is hardening parameter, H(ε) is hardening function, and n is hardening exponent.

3. The parametric rapid modeling method for foundation pit finite element models according to claim 1, characterized in that, The random field of soil elastic modulus generated by the Gaussian process has the following covariance function: Where E(x) and E(y) are the soil elastic modulus values ​​at positions x and y, respectively, and σ 2 is the variance of the soil elastic modulus, representing the degree of variability of the elastic modulus; l is the correlation length, representing the degree of spatial variability of soil properties. A larger l indicates that the soil properties change more gradually, while a smaller l indicates that the changes are more drastic; |xy| is the spatial distance between locations x and y.

4. The parametric rapid modeling method for foundation pit finite element models according to claim 1, characterized in that, The statistical analysis includes calculating the mean, standard deviation, and probability distribution function of the foundation pit stress and displacement to assess the response stability of the foundation pit under different soil conditions.

5. The parametric rapid modeling method for foundation pit finite element models according to claim 1, characterized in that, The multi-scale optimization method includes: Local optimization was carried out on various parts of the foundation pit; The global optimization process takes into account the spatial variability of the soil, the uncertainties during construction, and the dynamic response of the structure.

6. The parametric rapid modeling method for foundation pit finite element models according to claim 1, characterized in that, The mesh refinement is based on the stress gradient in the high-stress region. The stress gradient is obtained by simulation calculation and determined through the following steps: calculating the stress distribution of the finite element model of the foundation pit under multiple random soil conditions to obtain stress field data; Gradient calculations are performed on the stress field data to identify regions with large stress change rates, which are designated as high stress gradient regions. Mesh refinement is performed in regions with high stress gradients to improve computational accuracy, while a larger mesh is maintained in regions with gentle stress changes to reduce computational costs.

7. The parametric rapid modeling method for foundation pit finite element models according to claim 1, characterized in that, The real-time monitoring data includes soil displacement, stress, temperature, and pore water pressure. The data is acquired through real-time sensors and used to adjust the parameters of the finite element model of the foundation pit in real time to reflect the dynamic changes of the foundation pit during construction. The data was acquired through the following real-time sensors: Displacement sensors are used to measure the horizontal and vertical displacement of soil or structures in a foundation pit in real time. Stress sensors are used to measure the stress state of soil or structures in foundation pits; Temperature sensor used to measure soil temperature changes in the foundation pit area; A pore water pressure sensor is used to monitor changes in water pressure or pore water pressure in the soil of an excavation pit. Vibration sensors are used to detect vibrations around the foundation pit.

8. A rapid parametric modeling system for finite element models of foundation pits, applied to the rapid parametric modeling method for finite element models of foundation pits as described in any one of claims 1-7, characterized in that, include: The input module is used to input the geometric information of the foundation pit and the properties of the soil materials; The modeling module automatically generates a finite element model and performs mesh generation based on the input information. The constitutive model module uses the Kachanov-Delale model for nonlinear soil constitutive modeling. The stochastic process module generates a random field of soil elastic modulus based on Gaussian process and performs simulation. The simulation module calculates the foundation pit response under different soil conditions and performs statistical analysis. The optimization module minimizes stress and displacement using a multi-scale optimization method. The mesh refinement module refines the mesh in high-stress areas based on the optimization results. The real-time monitoring module acquires construction data in real time through sensors and dynamically adjusts model parameters.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the parametric rapid modeling method for the finite element model of the foundation pit as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the parametric rapid modeling method for the finite element model of the foundation pit as described in any one of claims 1-7.

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