Finite element simulation method for deformation of subway foundation pit in soft soil area

Abstract

The invention relates to the technical field of computer aided design, in particular to a metro foundation pit deformation finite element simulation method in a soft soil area, which comprises the following steps: performing finite element simulation on a metro foundation pit model under different grid parameters, and determining the deformation size and calculation duration of each position; according to the deformation size, determining a plurality of deformation areas and the area influence degree of each deformation area; determining the optimization feasibility of each position in each deformation area according to the deformation size and the calculation duration of each position in each simulation; determining a grid optimization proportion of each deformation area according to the area influence degree and the optimization feasibility degree of each position; determining a target reference grid parameter according to the grid parameter, the hardware configuration and the calculation duration of each simulation; and determining a target grid parameter of each deformation area according to the target reference grid parameter and the grid optimization proportion of each deformation area. By adopting the method, the accuracy of finite element simulation of subway foundation pit deformation can be improved, and the consumption of computing resources is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer-aided design, in particular to a subway foundation pit deformation finite element simulation method in soft soil area. BACKGROUND

[0002] With the development of cities, the subway gradually becomes the mainstream of public transportation, however, more and more subway construction, it is inevitable that the subway line is getting deeper, in this case, when the subway is constructed, due to the deepening of the foundation pit and the softness of the local geology, it leads to the deformation of the foundation pit during the construction of the subway foundation pit. In order to have better protection measures during construction to avoid abnormal subway foundation pit, it is often necessary to perform finite element simulation on the deformation of the foundation pit before the construction of the subway foundation pit. The core idea of finite element simulation is to discretize the soft soil area of the subway foundation pit into a limited number of simple unit bodies, such as tetrahedrons or hexahedrons, and then simulate the mechanical and thermal conduction behaviors in the real physical system through mathematical approximation, so as to predict the deformation of the subway foundation pit. That is, according to the stress of each region of the foundation pit, the stress and deformation of the corresponding local position in the simulation foundation pit are captured, in the finite element simulation, the more fine the grid division of each region is, the higher the precision of the simulation result is, and the more the consumption of computing resources is.

[0003] When two subway tunnels meet in the foundation pit, directional blasting is needed to blast through the small channel, which requires accurate simulation of the explosion part, but the accuracy of the area far away from the explosion wave is relatively low. Currently, multi-scale grid adaptive technology is commonly used, that is, local grid encryption or adaptive refinement strategy is adopted to encrypt the grid of key areas such as explosion area, but it does not consider that the explosion involves a relatively large area, and the airflow, particles and other effects caused by the explosion will also affect other soft soil areas to cause deformation, resulting in low simulation accuracy. Moreover, this way of uniformly adjusting the grid density or adjusting the attention area according to the model shape is a waste of computing resources. During the explosion process, most of the foundation pit positions do not produce deformation and do not need to consume a large amount of computing resources. SUMMARY

[0004] In order to solve the technical problems of inaccurate subway foundation pit deformation finite element simulation and high consumption of computing resources, the purpose of the present application is to provide a subway foundation pit deformation finite element simulation method in soft soil area, and the technical scheme adopted is as follows: The present application provides a subway foundation pit deformation finite element simulation method in soft soil area, the method comprises: Performing finite element simulation on the subway foundation pit model under different grid parameters to determine the deformation size and calculation time of each position; determine a plurality of deformation regions according to the deformation sizes of the positions in each simulation, and determine a region influence degree of each of the deformation regions; determine an optimization feasibility of each position in each of the deformation regions according to the deformation sizes of the positions in each simulation and the calculation time length; determine a grid optimization ratio of each of the deformation regions according to the region influence degrees of the deformation regions and the optimization feasibilities of the positions; determine a target reference grid parameter according to the grid parameters, hardware configurations and calculation time lengths of the simulations; determine a target grid parameter of each of the deformation regions according to the target reference grid parameter and the grid optimization ratios of the deformation regions.

[0005] According to the soft soil area subway foundation deformation finite element simulation method provided by the application, the deformation sizes of the positions in each simulation are determined, and the deformation regions are determined. determine a deformation degree of each position according to the deformation sizes of the positions in each simulation; take the positions with the deformation degrees greater than or equal to a preset deformation degree threshold as seed points; start region growing according to the deformation degrees of the surrounding positions from each of the seed points to obtain a plurality of deformation regions.

[0006] According to the soft soil area subway foundation deformation finite element simulation method provided by the application, the deformation sizes of the positions in each simulation are determined, and the deformation regions are determined. start region growing according to the deformation degrees of the surrounding positions from each of the seed points to obtain a plurality of deformation regions. determine a merging degree of the position reached according to the difference between the deformation degree of the position reached and the average of the deformation degrees of the positions in the neighborhood of the position reached; if the merging degree is greater than or equal to a preset merging degree threshold, merge the position reached into the deformation region corresponding to the seed point; stop the iteration until the merging degree of the position reached is less than the preset merging degree threshold, and obtain the deformation region corresponding to the seed point.

[0007] According to the soft soil area subway foundation deformation finite element simulation method provided by the application, the deformation sizes of the positions in each simulation are determined, and the deformation regions are determined. determine the average distance from the seed point in the deformation region to each position at the edge of the deformation region for each of the deformation regions; According to the maximum value in the deformation degree of each position in the deformation region and the distance mean value, a region deformation degree of the deformation region is determined; According to the size of the deformation region and the region deformation degree, a region influence degree of the deformation region is determined.

[0008] According to the soft soil area subway foundation pit deformation finite element simulation method provided by the application, the optimization feasibility of each position in each deformation region is determined according to the deformation size and the calculation time length of each position in each simulation, which comprises: For each position in each deformation region, the deformation size difference between the deformation size of the position in each simulation and the deformation size of the position in the reference simulation is determined; The calculation time length difference between the calculation time length of the position in each simulation and the calculation time length of the position in the reference simulation is determined; According to the deformation size difference and the calculation time length difference of the position in each simulation, the optimization feasibility of the position in the deformation region is determined.

[0009] According to the soft soil area subway foundation pit deformation finite element simulation method provided by the application, the target reference grid parameter is determined according to the grid parameter, hardware configuration and calculation time length of each simulation, which comprises: According to the grid parameter, hardware configuration and calculation time length of each simulation, the relationship between the grid parameter and the calculation time length is determined; The grid parameter is sequentially sampled, and the calculation time length corresponding to each sampled grid parameter is determined according to each sampled grid parameter and the relationship between the grid parameter and the calculation time length; wherein the direction of sampling the grid parameter is the direction of increasing grid density, reducing grid size or increasing grid number; According to the growth of the calculation time length obtained by each sampling, the target reference grid parameter is determined.

[0010] According to the soft soil area subway foundation pit deformation finite element simulation method provided by the application, the relationship between the grid parameter and the calculation time length is determined according to the grid parameter, hardware configuration and calculation time length of each simulation, which comprises: The grid parameter and hardware configuration of each simulation are taken as input, and the calculation time length of each simulation is taken as output, and the relationship between the grid parameter and the calculation time length is obtained by random forest training.

[0011] According to the soft soil area subway foundation pit deformation finite element simulation method provided by the application, the target reference grid parameter is determined according to the growth of the calculation time length obtained by each sampling, which comprises: For each sampling, determine the length increase of the calculation length obtained by the current sampling relative to the calculation length obtained by the last sampling; According to the length increase of the previous adjacent preset number of samplings, determine the length increase threshold of the current sampling; If the length increase of the current sampling is less than the length increase threshold of the current sampling, continue sampling; If the length increase of the current sampling is greater than or equal to the length increase threshold of the current sampling, the sampling grid parameter of the current sampling is determined as the target reference grid parameter, and the sampling is stopped.

[0012] According to the target reference grid parameter and the grid optimization ratio of each deformation area, the target grid parameter of each deformation area is determined, which comprises: Respectively for each deformation area, on the basis of the target reference grid parameter, the target grid parameter of the deformation area is obtained by increasing according to the grid optimization ratio of the deformation area.

[0013] According to the soft soil area subway foundation pit deformation finite element simulation method provided by the application, the method further comprises: The target reference grid parameter is used as the target grid parameter of the remaining areas except the deformation areas.

[0014] The application has the following beneficial effects: the finite element simulation of the subway foundation pit model is carried out under different grid parameters, the deformation size and the calculation length of each position are determined, then a plurality of deformation areas are determined according to the deformation size of each position in each simulation, the area influence degree of each deformation area is determined, the optimization feasibility of each position in each deformation area is determined according to the deformation size and the calculation length of each position in each simulation, the grid optimization ratio of each deformation area is determined according to the area influence degree of each deformation area and the optimization feasibility of each position, then the grid optimization ratio of each deformation area is changed on the basis of the target reference grid parameter, so that different target grid parameters can be accurately set for different areas, the accuracy of the finite element simulation of the deformation of the subway foundation pit is improved, and the consumption of calculation resources in the area with small deformation is avoided, and the consumption of calculation data is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0016] Figure 1 A flowchart of a subway foundation pit deformation finite element simulation method in soft soil area provided by an embodiment of the present application; Figure 2 A flowchart of determining a deformation region provided by an embodiment of the present application; Figure 3 A flowchart of determining a deformation region based on a seed point through region generation provided by an embodiment of the present application; Figure 4 A flowchart of determining a target reference mesh parameter provided by an embodiment of the present application; Figure 5 A flowchart of determining a target reference mesh parameter through time length growth provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purposes, the following will combine the drawings and preferred embodiments to specifically describe a subway foundation pit deformation finite element simulation method according to the present application, its specific implementation, structure, characteristics and effects in detail. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0019] The following will specifically describe the specific scheme of the subway foundation pit deformation finite element simulation method in soft soil area provided by the present application in combination with the drawings.

[0020] Please refer to Figure 1 which shows a flowchart of a subway foundation pit deformation finite element simulation method in soft soil area provided by an embodiment of the present application, including the following steps: Step 101, performing finite element simulation on the subway foundation pit model under different mesh parameters to determine the deformation size of each position and the calculation time length.

[0021] It can be understood that, in the finite element simulation of the whole subway foundation pit directional blasting process, the more fine the mesh of each region is, the more computing resources are consumed in the simulation, but the more accurate the deformation data of the foundation pit obtained by the simulation is; on the contrary, the coarser the mesh is, the less resource it consumes, the faster the calculation is, but the relative accuracy of the deformation data of the foundation pit obtained by the simulation will decrease. Therefore, the simulation can be roughly carried out several times under different mesh parameters first, and then according to the deformation data (deformation size) of the foundation pit obtained by the simulation, it is compared to obtain which region has low calculation efficiency, that is, it consumes more resources but does not improve the accuracy too much. At the same time, according to the deformation of the many regions in the foundation pit, the region with the largest deformation or the region that is more easily deformed and is most affected by deformation is found, so as to obtain the importance of different regions of the foundation pit, and then the mesh parameters of each region are adaptively adjusted according to the importance.

[0022] In one embodiment, the mesh parameters can include mesh number, mesh density and mesh type, etc. For example, the mesh type can include hexahedron and tetrahedron, etc.

[0023] In one embodiment, the subway foundation pit model is constructed based on the actual measurement data and construction drawings of the subway foundation pit, the finite element simulation of the subway foundation pit model is carried out under different mesh parameters, and the deformation size of each position is obtained by comparing the simulation results with the original foundation pit model structure. The simulation results can include stress, strain and displacement field distribution data of each position of the subway foundation pit at different time points.

[0024] In one embodiment, the calculation time length at the position can be the time length experienced from the beginning of the simulation to the change rate of the stress, deformation size, etc. data at the position being less than or equal to the preset change rate threshold. For example, the preset change rate threshold can be 5%.

[0025] In one embodiment, the process of constructing the subway foundation pit model is as follows: first, place a three-dimensional laser scanner in the foundation pit, and call up the construction drawings of the subway foundation pit to obtain the depth, length and width dimensions of the foundation pit, as well as the supporting structure data of the underground continuous wall, etc. Based on this, the basic geometric modeling of the subway foundation pit is completed. Then, geological data is obtained through geological exploration means, and the geological exploration data before construction is called up. The geological exploration means include drilling sampling and laboratory determination, etc. In addition, the soil properties in the foundation pit are measured through triaxial experiments and numerous detection systems, such as compression modulus, cohesion, and internal friction angle, etc. Then, multiple environmental sensors are placed in the subway foundation pit to collect environmental data in the foundation pit, including temperature sensors, humidity sensors, etc., and environmental data including temperature, atmospheric pressure, groundwater level, and soil moisture, etc. Finally, the detected environmental data is input into the finite element software, and at the same time, the subway foundation pit model is constructed according to these data and the corresponding model. The mechanical system in the subway foundation pit model includes the elastic-plastic model of the soil body, the supporting structure beam element, and the seepage field model, etc. The setting parameters include boundary conditions and force boundary conditions, etc. At this point, the information of several kinds of parameters is collected to form an information library.

[0026] In one embodiment, the process of comparing the simulation results with the original foundation pit model to obtain the deformation size at each position under different mesh parameters is as follows: after completing the parameterized modeling of the subway foundation pit, nodes are gradually generated from the boundary to the interior according to the push wave method, and high-quality triangular meshes are generated by combining the Delaunay triangulation algorithm, i.e. the initialization mesh. Then, based on the initialization mesh, the subway foundation pit model and the boundary conditions, a suitable solver is selected, such as the Newmark method (a method for simulating the dynamic response of structures under transient loads) or the central difference method, etc. The time step is adjusted according to the directional blasting dynamic process, and the simulation solution is performed. After obtaining the simulation results, the stress, strain and displacement field distribution data at each position in the subway foundation pit at different times are extracted, and the original foundation pit model structure is compared to obtain the deformation size at each position. In the above push wave method and Delaunay triangulation algorithm, a certain mesh size (limited by the front edge length) and mesh density (limited by the unit growth rate and density function, etc.) of the unit mesh are generated by limiting the front edge length, unit growth rate and density function, etc., and finite element simulation is performed to obtain the deformation size at each position under the corresponding mesh parameters. Similarly, by adjusting the mesh parameters such as the front edge length, unit growth rate and density function, meshes of different mesh sizes and mesh densities can be further generated, and through finite element simulation, the deformation size at each position under different mesh parameters can also be obtained.

[0027] In step 102, a plurality of deformation regions are determined according to the deformation sizes of the positions in each simulation, and a region influence degree of each deformation region is determined.

[0028] The region influence degree is used to represent the influence degree of the deformation region affected by the explosion.

[0029] In an embodiment, the deformation degrees of the positions are determined according to the deformation sizes of the positions in each simulation, and then the plurality of deformation regions are determined according to the deformation degrees of the positions.

[0030] In an embodiment, the positions with the deformation degrees meeting a first preset condition can be taken as seed points, and the plurality of deformation regions can be obtained by region growing according to the deformation degrees of the surrounding positions starting from the seed points. The first preset condition can be that the deformation degree is greater than or equal to a preset deformation degree threshold, or the first preset condition can be that the deformation degree is ranked in a front preset number or a front preset proportion in a descending order, or can be other conditions, which are not limited.

[0031] In an embodiment, the region influence degree of each deformation region is determined according to the size of the deformation region and the region deformation degree of the deformation region. In an embodiment, the size of the deformation region can be determined according to the total number of positions in the deformation region. In an embodiment, the region deformation degree of the deformation region can be determined according to the maximum value in the deformation degrees of the positions in the deformation region and the size of the deformation region.

[0032] In step 103, the optimization feasibility of each position in each deformation region is determined according to the deformation sizes of the positions in each simulation and the calculation time length.

[0033] The optimization feasibility is used to represent the feasibility of inputting more computing resources to the position in the deformation region to obtain greater accuracy.

[0034] In an embodiment, the optimization feasibility of each position in each deformation region is determined according to the change of the deformation size and the calculation time length of the position in the process of changing the grid parameters of each simulation towards a target direction. The target direction is a direction in which the grid density increases, the grid size decreases, or the grid number increases.

[0035] In step 104, the grid optimization proportion of each deformation region is determined according to the region influence degree of each deformation region and the optimization feasibility of each position.

[0036] The grid optimization proportion is used to represent the increase degree of the grid density of the deformation region in order to more accurately and efficiently complete the simulation.

[0037] It can be understood that for the finite element simulation of subway foundation pit deformation, not all areas worth investing in computing resources need to be focused on, more attention should be paid to the impact of the explosion on the soft soil area, which leads to the need to mainly adjust the grid parameters for the area with greater explosion impact and greater accuracy through investment in computing resources. That is, the greater the explosion impact and the higher the optimization feasibility of the area, the more detailed the grid should be, and the grid density of each deformation area is constructed accordingly, so the grid optimization ratio of the deformation area and the area impact degree of the deformation area and the optimization feasibility of each position exist the following relationship: The grid optimization ratio of the deformation area is positively correlated with the area impact degree of the deformation area and the optimization feasibility of each position in the deformation area.

[0038] In one embodiment, the product of the area impact degree of the deformation area and the optimization feasibility of the position in the deformation area is taken as the reciprocal, and then the reciprocal is inversely proportional to the normalization, and then the average value of the inverse proportional normalization results corresponding to each position is obtained. The grid optimization ratio of the deformation area. The formula is as follows: ; Wherein, represents the grid optimization ratio of the deformation area . represents the area impact degree of the deformation area . represents the optimization feasibility of the th position in the deformation area . represents the total number of positions in the deformation area . represents the exponential function with e as the base. represents the absolute value. is used to realize inverse proportional normalization.

[0039] The greater the grid optimization ratio, the greater the density of the deformation area should be increased when the grid is encrypted in order to more accurately and efficiently complete the finite element simulation during simulation.

[0040] Step 105, according to the grid parameters of each simulation, hardware configuration and calculation time, determine the target reference grid parameter.

[0041] The target reference grid parameter is a grid parameter required by the subway foundation pit to ensure accuracy and save computing resources during finite element simulation. That is, under the target reference grid parameter, if the grid parameter is further adjusted to increase the grid density, reduce the grid size, or increase the number of grids, more computing time will be increased overall, and the investment in computing resources will be doubled.

[0042] In step 106, the target grid parameters of the deformation regions are determined according to the target reference grid parameter and the grid optimization ratios of the deformation regions.

[0043] In one embodiment, the target reference grid parameter can be used as the target grid parameter of the remaining regions except the deformation regions. The target grid parameters of the deformation regions are determined according to the target reference grid parameter and the grid optimization ratios of the deformation regions.

[0044] In one embodiment, after obtaining the target grid parameters of each position in the subway foundation pit, a push wave front method is used to generate a hexahedral dominant hybrid grid corresponding to each region in the foundation pit, and then the deformation simulation of the subway foundation pit is completed according to the set boundary conditions and the corresponding solver.

[0045] The subway foundation pit deformation finite element simulation method in the soft soil area determines the deformation size and computing time of each position under different grid parameters, then determines a plurality of deformation regions according to the deformation size of each position in each simulation, determines the region influence degree of each deformation region, determines the optimization feasibility of each position in each deformation region according to the deformation size and computing time of each position in each simulation, determines the grid optimization ratio of each deformation region according to the region influence degree of each deformation region and the optimization feasibility of each position, and then changes the grid optimization ratio of each deformation region based on the target reference grid parameter, so that different target grid parameters can be accurately set for different regions, the accuracy of the subway foundation pit deformation finite element simulation is improved, and the consumption of computing resources in the deformation region is avoided, and the consumption of computing data is reduced.

[0046] In one embodiment, referring to Figure 2 A plurality of deformation regions are determined according to the deformation size of each position in each simulation, including the following steps: In step 201, the deformation degree of each position is determined according to the deformation size of each position in each simulation.

[0047] It can be understood that when a subway foundation pit is constructed, there is often a fixed process, and directional blasting is needed to open a new air duct for it in order not to affect the ventilation of other adjacent lines, so directional blasting is performed when the foundation pit is relatively stable, that is, before the explosion, the subway foundation pit is basically relatively stable, but when the explosion in the foundation pit occurs, the airflow and splashed particles of the explosion will cause deformations of different sizes at various positions in the foundation pit, and the position with greater deformation indicates that it is more affected by the explosion, and the simulation should be more accurate. Therefore, the deformation degree of each position can be determined according to the deformation size of each position in each simulation, and the deformation region can be determined according to the deformation degree of each position.

[0048] In one embodiment, for each position, the maximum value of the deformation size in each direction obtained in each simulation at the position is determined, and the deformation degree of the position is determined according to the maximum value corresponding to each simulation at the position.

[0049] In one embodiment, the maximum value corresponding to each simulation at the position can be proportionally normalized, and the proportionally normalized result of the maximum value corresponding to each simulation at the position is averaged to obtain the deformation degree of the position. The formula is as follows: ; Wherein, represents the deformation degree of the position . represents the deformation size in the first direction obtained by the first simulation at the position . represents the maximum value of the deformation size in each direction obtained by the first simulation at the position . represents the total number of simulations. represents proportional normalization.

[0050] Step 202, taking the position with a deformation degree greater than or equal to a preset deformation degree threshold as a seed point.

[0051] For example, the preset deformation degree threshold can be set to 0.6.

[0052] It can be understood that for the position with a deformation degree greater than or equal to the preset deformation degree threshold, it can be considered that the deformation at the position is large, and the deformation condition of the position needs to be focused on to estimate the blasting effect. Since the deformation of the foundation pit caused by the explosion is often caused by the extrusion of the shock airflow, etc., which will cause the deformation in the foundation pit to be continuous, therefore, according to the positions with a deformation degree greater than or equal to the preset deformation degree threshold, multiple deformation regions are divided.

[0053] Step 203, starting from each seed point, region growing is performed according to the deformation degree of each position in the surrounding area, and a plurality of deformation regions are obtained.

[0054] In an embodiment, starting from each seed point respectively, step by step, the positions in the surrounding area are iterated outward, for each iterated position, the merging degree of the iterated position is determined according to the deformation degree of the iterated position and the deformation degrees of positions in the neighborhood of the iterated position, and region growing is performed according to the merging degree, and a deformation region corresponding to the seed point is obtained.

[0055] In an embodiment, if the merging degree is greater than or equal to a preset merging degree threshold, the iterated position is merged into the deformation region corresponding to the seed point, and the iteration is stopped until the merging degree of the iterated position does not meet the second preset condition, and then a deformation region corresponding to the seed point is obtained.

[0056] In the above embodiment, the deformation degree of each position is determined according to the deformation size of each position in each simulation, and the positions with a deformation degree greater than or equal to a preset deformation degree threshold are taken as seed points, so that the seed position with a larger deformation degree can be accurately determined, and then region growing is performed according to the deformation degree of each position in the surrounding area starting from each seed point, so that a plurality of deformation regions can be accurately obtained.

[0057] In an embodiment, referring to Figure 3 Step 203, starting from each seed point, region growing is performed according to the deformation degree of each position in the surrounding area, and a plurality of deformation regions are obtained, including the following steps: Step 2031, starting from each seed point respectively, step by step, the positions in the surrounding area are iterated outward.

[0058] Step 2032, for each iterated position, the merging degree of the iterated position is determined according to the difference between the deformation degree of the iterated position and the average of the deformation degrees of positions in the neighborhood of the iterated position.

[0059] For example, the neighborhood of the iterated position can be a range with a diameter of 1 centimeter with the iterated position as the center.

[0060] In an embodiment, the merging degree of the iterated position is negatively correlated with the difference between the deformation degree of the iterated position and the average of the deformation degrees of positions in the neighborhood of the iterated position.

[0061] In one embodiment, the difference between the deformation degree of the position reached and the average deformation degree of positions in the neighborhood of the position reached can be calculated, and the difference between the deformation degree of the position reached and the average deformation degree of positions in the neighborhood of the position reached is determined according to the ratio between the difference and the average deformation degree of positions in the neighborhood of the position reached. The difference between the deformation degree of the position reached and the average deformation degree of positions in the neighborhood of the position reached is inversely proportional normalized to obtain the merging degree of the position reached. The formula is as follows: ; wherein, denotes the merging degree of the first position reached around the seed point. denotes the deformation degree of the first position reached around the seed point. denotes the average deformation degree of positions in the neighborhood of the first position reached around the seed point. denotes the average deformation degree of positions in the neighborhood of the first position reached around the seed point. denotes the average deformation degree of positions in the neighborhood of the first position reached around the seed point. denotes the exponential function with base e. denotes the absolute value. for inversely proportional normalization.

[0062] Step 2033, if the merging degree is greater than or equal to the preset merging degree threshold, the position reached is merged into the deformation region corresponding to the seed point.

[0063] For example, the preset merging degree threshold can be set to 0.6.

[0064] Step 2034, until the merging degree of the position reached is less than the preset merging degree threshold, the traversal is stopped, and the deformation region corresponding to the seed point is obtained.

[0065] In the above embodiment, each seed point is started from each seed point, and the surrounding positions are gradually traversed outward. For each position reached, the merging degree of the position reached is determined according to the difference between the deformation degree of the position reached and the average deformation degree of positions in the neighborhood of the position reached. The region growing is performed according to the comparison result of the merging degree and the preset merging degree threshold, thereby realizing the accurate division of multiple deformation regions based on multiple positions with large deformation degrees.

[0066] ​​​​In one embodiment, the area influence degree of each deformation region is determined by: determining, for each deformation region respectively, a distance average of a seed point in the deformation region to each position of an edge of the deformation region; determining an area deformation degree of the deformation region according to a maximum value in deformation degrees of each position in the deformation region and the distance average; and determining the area influence degree of the deformation region according to a size of the deformation region and the area deformation degree.

[0067] It can be understood that, at the time of explosion, the foundation pit shakes and air flow and particles generated by the explosion strike everywhere in the foundation pit, resulting in different degrees of deformation at these positions. The greater the deformation or the greater the deformation range, the greater the influence of the explosion. Therefore, the size of the influence of the explosion on each deformation region, i.e., the area influence degree, needs to be accurately obtained during simulation according to the deformation range of each deformation region (i.e., the size of the deformation region) and the area deformation degree of the deformation region.

[0068] In one embodiment, the area deformation degree of the deformation region is determined according to a ratio between the maximum value in the deformation degrees of each position in the deformation region and the distance average.

[0069] In one embodiment, the area influence degree of the deformation region is positively correlated with the size of the deformation region and the area deformation degree of the deformation region.

[0070] In one embodiment, the area influence degree of the deformation region is determined according to a product of the size of the deformation region and the area deformation degree. The formula is as follows: ; wherein, represents the area influence degree of the deformation region . represents the size of the deformation region , i.e., the number of pixel points in the deformation region . represents the deformation degree of the i-th position in the deformation region . represents the maximum value in the deformation degrees of each position in the deformation region . represents the distance average of a seed point in the deformation region to each position of an edge of the deformation region . represents the area deformation degree of the deformation region . is used for realizing positive proportional normalization.

[0071] ​The greater the area influence degree, the greater the deformation range and deformation degree of the deformation area, and the greater the influence degree of the deformation area on the explosion. Therefore, the deformation area needs to be paid more attention to in simulation, and the deformation area should be more detailed in simulation.

[0072] In the above embodiment, the area influence degree of the deformation area can be accurately determined according to the size of the deformation area and the deformation degree of the area.

[0073] In one embodiment, the optimization feasibility of each position in each deformation area is determined according to the deformation size and the calculation time of each position in each simulation, including: for each position in each deformation area, the deformation size difference between the deformation size of the position in each simulation and the deformation size of the position in the reference simulation is determined, wherein the reference simulation is the simulation in which the grid density is the smallest, the grid size is the largest, or the grid number is the least; the calculation time difference between the calculation time of the position in each simulation and the calculation time of the position in the reference simulation is determined; and the optimization feasibility of the position in the deformation area is determined according to the deformation size difference and the calculation time difference of the position in each simulation.

[0074] It can be understood that when performing finite element simulation, the more detailed the grid division, the more accurate the simulation result, but it will also lead to a significant increase in the consumption of computing resources during simulation. Moreover, in some areas, the increase in grid density does not significantly improve the accuracy of deformation, that is, with the increase in calculation difficulty, the change in deformation accuracy is basically unchanged, indicating that the grid parameters on the area do not need to be further adjusted. Therefore, the optimization feasibility of each position can be determined according to the consistency between the calculation difficulty (i.e., the calculation time) and the change in deformation accuracy (i.e., the deformation size) when the grid density is increased in each simulation.

[0075] In one embodiment, the first simulation can be used as the reference simulation, and the grid density of the first simulation is the smallest, the grid size is the largest, or the grid number is the least. In subsequent simulations, the grid parameters are gradually adjusted to increase the grid density, decrease the grid size, or increase the grid number.

[0076] In one embodiment, the optimization feasibility of the position in the deformation area is positively correlated with the corresponding deformation size difference of the position in each simulation, and is negatively correlated with the corresponding calculation time difference of the position in each simulation.

[0077] In one embodiment, for each position in each deformation area, the ratio between the corresponding deformation size difference and the calculation time difference of the position in each simulation is calculated, the ratio is proportionally normalized, and then the proportionally normalized results of the ratio of the position in each simulation are averaged to obtain the optimization feasibility of the position. The formula is as follows: ; wherein, represents the optimization feasibility of the i-th position in the deformation region. represents the deformation size of the i-th position in the deformation region in the n-th simulation. represents the deformation size of the i-th position in the deformation region in the reference simulation. represents taking an absolute value. represents the deformation size difference corresponding to the i-th position in the deformation region in the n-th simulation. represents the calculation time length of the i-th position in the deformation region in the n-th simulation. represents the calculation time length of the i-th position in the deformation region in the reference simulation. represents the calculation time length difference corresponding to the i-th position in the deformation region in the n-th simulation. Generally, there is no possibility of being zero, and if there is an extreme case, in order to avoid the possibility of zero in the denominator of the fraction, a non-zero constant such as 0.001 is added at the position of the denominator of the fraction. represents the total number of simulations. Because the first simulation is used as the reference simulation, the average value is calculated in the formula. is used. is used. is used to realize the positive proportional normalization.

[0078] The greater the optimization feasibility, the more accurate the position can be obtained by investing in computing resources.

[0079] In the above embodiment, according to the consistency between the calculation difficulty (i.e., the calculation time length) and the deformation accuracy change (i.e., the deformation size) under the condition of increasing grid density in each simulation, the optimization feasibility of each position can be accurately determined.

[0080] In one embodiment, referring to Figure 4 , the target reference grid parameters are determined according to the grid parameters, hardware configuration and calculation time length of each simulation, including the following steps: Step 401, determine the relationship between the grid parameters and the calculation time length according to the grid parameters, hardware configuration and calculation time length of each simulation.​​​​​​​​​​​​​​​​​​

[0081] It can be understood that the calculation difficulty gradually increases as the grid density increases, the grid size decreases, and the number of grids increases during simulation. Therefore, the relationship between the grid parameters and the calculation time is constructed according to the calculation time and the corresponding grid parameters of the multiple simulations of the subway foundation pit.

[0082] In an embodiment, the grid parameters and the hardware configuration of each simulation can be taken as input, and the calculation time of each simulation can be taken as output, and the relationship between the grid parameters and the calculation time can be determined by a machine learning algorithm.

[0083] In step 402, the grid parameters are sequentially sampled, and the calculation time corresponding to the sampled grid parameters is determined according to each sampled grid parameter and the relationship between the grid parameters and the calculation time. The direction of sampling the grid parameters is the direction in which the grid density increases, the grid size decreases, or the number of grids increases.

[0084] For example, if the grid parameters include the grid density, the grid density can be sampled by increasing 0.1 times each time.

[0085] The sampled grid parameters can be substituted into the relationship between the grid parameters and the calculation time to obtain the calculation time corresponding to the sampled grid parameters.

[0086] In step 403, the target reference grid parameters are determined according to the growth of the calculation time obtained by each sampling.

[0087] In the above embodiment, the target reference grid parameters can be accurately determined according to the growth of the calculation time obtained by each sampling of the grid parameters, so that the subway foundation pit can ensure accuracy and save computing resources as a whole when performing finite element simulation under the target reference grid parameters.

[0088] In an embodiment, the relationship between the grid parameters and the calculation time is determined according to the grid parameters, the hardware configuration, and the calculation time of each simulation, including: taking the grid parameters and the hardware configuration of each simulation as input, and taking the calculation time of each simulation as output, and obtaining the relationship between the grid parameters and the calculation time by random forest training.

[0089] In the above embodiment, the relationship between the grid parameters and the calculation time can be accurately obtained by random forest training.

[0090] In an embodiment, referring to Figure 5 In step 403, the target reference grid parameters are determined according to the growth of the calculation time obtained by each sampling, including the following steps: Step 4031, for each sampling, determine the time length growth of the calculation time length obtained by the current sampling relative to the calculation time length obtained by the last sampling.

[0091] Determine the time length growth according to the difference between the calculation time length obtained by the current sampling and the calculation time length obtained by the last sampling.

[0092] Step 4032, determine the time length growth threshold of the current sampling according to the time length growths of the previous adjacent preset number of samplings.

[0093] For example, the preset number of times can be twice.

[0094] In one embodiment, the time length growth threshold of the current sampling can be determined according to the mean of the time length growths of the previous adjacent preset number of samplings.

[0095] In one embodiment, the time length growth threshold of the current sampling can be the value obtained by multiplying the mean of the time length growths of the previous adjacent preset number of samplings by a preset multiple. For example, the preset multiple can be 1.5. That is, the time length growth threshold of the current sampling can be represented as . Wherein, represents the mean of the time length growths of the previous adjacent preset number of samplings.

[0096] Step 4033, if the time length growth of the current sampling is less than the time length growth threshold of the current sampling, continue sampling.

[0097] Step 4034, if the time length growth of the current sampling is greater than or equal to the time length growth threshold of the current sampling, determine the sampling grid parameter of the current sampling as the target reference grid parameter, and stop sampling.

[0098] For example, if the time length growth of the current sampling satisfies the following condition, the sampling grid parameter of the current sampling is determined as the target reference grid parameter: ; Wherein, represents the time length growth of the current sampling. represents the mean of the time length growths of the previous adjacent preset number of samplings. represents the time length growth threshold of the current sampling.

[0099] In the above embodiment, the length growth threshold of the current sampling is determined according to the length growth of the previous adjacent preset number of samplings, if the length growth of the current sampling is greater than or equal to the length growth threshold of the current sampling, it indicates that the length growth of the current sampling does not conform to the previous growth rule, therefore, the sampling grid parameter of the current sampling is determined as the target reference grid parameter, which can ensure the accuracy and save the computing resources of the subway foundation pit during the finite element simulation under the target reference grid parameter.

[0100] In one embodiment, the target grid parameters of the deformation regions are determined according to the target reference grid parameter and the grid optimization ratios of the deformation regions, including: respectively for each deformation region, on the basis of the target reference grid parameter, the target grid parameter of the deformation region is obtained by increasing according to the grid optimization ratio of the deformation region.

[0101] ; wherein, denotes the target grid parameter of the deformation region . denotes the target reference grid parameter. denotes the target grid parameter of the deformation region .

[0102] In the above embodiment, on the basis of the target reference grid parameter, the target grid parameter of the deformation region is obtained by increasing according to the grid optimization ratio of the deformation region, which can accurately obtain the target grid parameter of the deformation region, accurately set different target grid parameters for different regions, improve the accuracy of the deformation finite element simulation of the subway foundation pit, and avoid consuming a large amount of computing resources in the region with small deformation, thereby reducing the consumption of computing resources.

[0103] In one embodiment, the method further includes: taking the target reference grid parameter as the target grid parameter of the remaining regions except the deformation regions.

[0104] In the above embodiment, the target reference grid parameter is taken as the target grid parameter of the remaining regions except the deformation regions, which can ensure the accuracy and save the computing resources of the remaining regions under the target reference grid parameter.

[0105] The technical features of the above embodiments can be combined arbitrarily, in order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0106] The above-described embodiments are merely illustrative for the present application and are described in a relatively specific and detailed manner, but should not be construed as a limitation to the scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

[0107] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0108] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A subway foundation pit deformation finite element simulation method in soft soil area, characterized in that, The method comprises: performing finite element simulation on a subway foundation pit model under different grid parameters to determine deformation sizes of positions and calculation time lengths; determining a plurality of deformation regions according to the deformation sizes of the positions in each simulation, and determining region influence degrees of the deformation regions; determining optimization feasibility of the positions in each deformation region according to the deformation sizes of the positions in each simulation and the calculation time lengths; determining grid optimization proportions of the deformation regions according to the region influence degrees of the deformation regions and the optimization feasibility of the positions; determining target reference grid parameters according to the grid parameters, hardware configurations and the calculation time lengths of each simulation; determining target grid parameters of the deformation regions according to the target reference grid parameters and the grid optimization proportions of the deformation regions.

2. The method of claim 1, wherein, The method comprises: determining deformation degrees of the positions according to the deformation sizes of the positions in each simulation; taking positions with deformation degrees greater than or equal to a preset deformation degree threshold as seed points; starting from each seed point, performing region growing according to deformation degrees of surrounding positions to obtain a plurality of deformation regions.

3. The method of claim 2, wherein, The method comprises: starting from each seed point, iteratively traversing surrounding positions; for each traversed position, determining a merging degree of the traversed position according to a difference between the deformation degree of the traversed position and an average of deformation degrees of positions in a neighborhood of the traversed position; if the merging degree is greater than or equal to a preset merging degree threshold, merging the traversed position into a deformation region corresponding to the seed point; stopping the iteration until the merging degree of the traversed position is less than the preset merging degree threshold, and obtaining the deformation region corresponding to the seed point.

4. The method of claim 2, wherein, The method comprises: for each deformation region, determining an average distance from the seed point in the deformation region to each position at an edge of the deformation region; determining a region deformation degree of the deformation region according to a maximum value in the deformation degrees of the positions in the deformation region and the average distance; determining a region influence degree of the deformation region according to a size of the deformation region and the region deformation degree.

5. The soft soil area subway foundation pit deformation finite element simulation method according to claim 1, characterized in that, The method comprises: for each position in each deformation region, determining a deformation size difference between the deformation size of the position in each simulation and the deformation size of the position in a reference simulation; wherein the reference simulation is a simulation with grid parameters that result in minimum grid density, maximum grid size or minimum grid quantity; determining a calculation time length difference between the calculation time length of the position in each simulation and the calculation time length of the position in the reference simulation. According to the deformation size difference and the calculation time difference corresponding to the position in each simulation, an optimization feasibility of the position in the deformation region is determined.

6. The soft soil area subway foundation pit deformation finite element simulation method according to claim 1, characterized in that, The determining of the target reference grid parameter according to the grid parameter, the hardware configuration and the calculation time of each simulation comprises: According to the grid parameter, the hardware configuration and the calculation time of each simulation, a relationship between the grid parameter and the calculation time is determined. The grid parameter is sequentially sampled, and according to each sampled sampling grid parameter and the relationship between the grid parameter and the calculation time, a calculation time corresponding to the sampling grid parameter is determined; wherein, a direction of sampling the grid parameter is a direction of increasing grid density, reducing grid size or increasing grid quantity. According to the growth of the calculation time obtained by each sampling, a target reference grid parameter is determined.

7. The method of claim 6, wherein, The determining of the relationship between the grid parameter and the calculation time according to the grid parameter, the hardware configuration and the calculation time of each simulation comprises: The grid parameter and the hardware configuration of each simulation are taken as input, and the calculation time of each simulation is taken as output, and a relationship between the grid parameter and the calculation time is trained by random forest. 8.The subway foundation pit deformation finite element simulation method of soft soil area according to claim 6, characterized in that, The determining of the target reference grid parameter according to the growth of the calculation time obtained by each sampling comprises: For each sampling, a time length growth amount of the calculation time obtained by the current sampling relative to the calculation time obtained by the last sampling is determined; According to the time length growth amounts of the previous adjacent preset number of samplings, a time length growth amount threshold of the current sampling is determined; If the time length growth amount of the current sampling is less than the time length growth amount threshold of the current sampling, the sampling is continued; If the time length growth amount of the current sampling is greater than or equal to the time length growth amount threshold of the current sampling, the sampling grid parameter of the current sampling is determined as the target reference grid parameter, and the sampling is stopped.

9. The soft soil area subway foundation pit deformation finite element simulation method according to any one of claims 1 to 8, characterized in that, The determining of the target grid parameter of each deformation region according to the target reference grid parameter and the grid optimization proportion of each deformation region comprises: Respectively for each deformation region, the target grid parameter of the deformation region is obtained by increasing the target reference grid parameter according to the grid optimization proportion of the deformation region.

10. The method of claim 1 to 8, wherein, The method further comprises: The target reference grid parameter is taken as a target grid parameter of a remaining region except for each deformation region.

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