Uncertain numerical analysis method and device considering spatial variation of soil parameters

Abstract

The invention discloses an uncertainty numerical analysis method and device considering soil parameter spatial variation, and the method comprises the following steps: 1) building a numerical model according to engineering survey data, and obtaining random parameters needed by uncertainty numerical analysis; the parameters are soil elastic modulus random field statistical parameters; 2) assigning the generated random parameter to a finite element unit in the numerical model, and establishing a multilayer soil body random field numerical model; and 3) inputting the multilayer soil body random field numerical model considering the soil body spatial variation into Abaqus for cyclic calculation, and performing data analysis on a calculation result to obtain deformation prediction of a geotechnical engineering structure. According to the method, a random field theory and a numerical simulation method are combined, various uncertainties caused by spatial variation of natural rock-soil body parameters can be well considered, and a model calculation result better conforms to the actual situation.

Description

Technical Field

[0001] The present invention relates to finite element simulation technology, and in particular, to a numerical analysis method and device for considering the uncertainty of spatial variability of soil parameters. Background Art

[0002] Currently, the deterministic design method is still used in the design of various geotechnical engineering projects, and the spatial variability of geotechnical materials cannot be considered. The uncertainty caused by the spatial variability of geotechnical materials makes it difficult to study and predict the deformation laws of various geotechnical engineering structures. Numerical simulation has the advantages of low cost and repeatability and can be used as a powerful tool for studying deformation laws.

[0003] Finite element simulation is an analysis method based on numerical calculation and discretization technology. By decomposing a complex object or system into multiple small elements (finite elements) and calculating the response of each element under specific conditions, the physical behavior of the entire system can be simulated. When studying and predicting the deformation laws of various geotechnical engineering structures, combining the random field theory with the numerical simulation method can well consider various uncertainties caused by the spatial variability of natural geotechnical parameters, making the calculation results more in line with the actual situation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a numerical analysis method for considering the uncertainty of spatial variability of soil parameters in view of the defects in the prior art.

[0005] The technical solution adopted by the present invention to solve its technical problems is: a numerical analysis method for considering the uncertainty of spatial variability of soil parameters, including:

[0006] 1) Establish a numerical model according to engineering survey data to obtain the random parameters required for uncertainty numerical analysis; the parameters are the statistical parameters of the soil elastic modulus random field;

[0007] 2) Assign the generated random parameters to the finite element cells in the numerical model to establish a multi-layer soil random field numerical model; specifically as follows:

[0008] In Abaqus, assign the material parameters in the random field file to each cell respectively, and batch generate inp files to establish a multi-layer soil random field numerical model;

[0009] 3) Input the multi-layer soil random field numerical model considering soil spatial variability into Abaqus for iterative calculation, and perform data analysis on the calculation results to obtain the deformation prediction of the geotechnical engineering structure;

[0010] 3.1) Input the inp files batch generated in step 2.1) into the finite element analysis software Abaqus for iterative calculation for uncertainty numerical analysis;

[0011] 3.2) Analyze the calculation results of step 3.1) through data analysis, count the variation of characteristic parameters with the number of simulations, obtain the influence of the spatial variability of elastic modulus on the calculation results, as well as the failure probability of the corresponding engineering structure, and obtain the deformation prediction of the geotechnical engineering structure.

[0012] According to the above scheme, in step 1), the random parameters required for uncertainty numerical analysis are obtained as follows:

[0013] 1.1) Establish a numerical model based on engineering survey data, including geometric model, finite element mesh generation, and boundary condition setting;

[0014] 1.2) Export the finite element analysis format file (inp file) of the Abaqus numerical model, and extract the element numbers and node numbers in the inp file through Python programming to generate the element center coordinate file;

[0015] 1.3) Generate the random parameters required for uncertainty analysis based on the generated element center coordinate file to obtain the random field file;

[0016] 1.4) Verify the correctness of the elastic modulus random field; through statistical analysis of the soil elastic modulus parameters at a certain position in the soil layer, determine whether they are consistent with the statistical parameters of the corresponding soil material structure.

[0017] According to the above scheme, in step 1.3), based on the generated element center coordinate file, use Python programming to adopt the random parameter generation method of the Cholesky decomposition midpoint method to generate the random parameters required for uncertainty analysis.

[0018] According to the above scheme, the statistical parameters of the soil elastic modulus random field include the soil material structure, the corresponding mean value of elastic modulus, coefficient of variation, horizontal correlation distance, and vertical correlation distance.

[0019] According to the above scheme, in step 3), it is as follows:

[0020] 3.1) Input the inp files batch-generated in step 2.1) into the finite element analysis software Abaqus, and perform uncertainty numerical analysis through loop calculation;

[0021] 3.2) Analyze the calculation results of step 3.1) through data analysis, count the variation of characteristic parameters with the number of simulations, obtain the influence of the spatial variability of elastic modulus on the corresponding engineering structure, as well as the failure probability of the corresponding engineering structure.

[0022] An uncertainty numerical analysis device considering the spatial variation of soil parameters includes:

[0023] A random parameter generation module, configured to establish a numerical model according to engineering survey data and obtain the random parameters required for uncertainty numerical analysis; the parameters are the statistical parameters of the soil elastic modulus random field.

[0024] A multi-layer soil random field numerical model establishment module, configured to assign the generated random parameters to the finite element cells in the numerical model to establish a multi-layer soil random field numerical model; specifically as follows:

[0025] In Abaqus, assign the material parameters in the random field file to each cell respectively, and batch generate inp files to establish a multi-layer soil random field numerical model.

[0026] An analysis and calculation module, configured to input the multi-layer soil random field numerical model considering soil spatial variability into a finite element software for loop calculation, and perform data analysis on the calculation results to obtain the deformation prediction of the geotechnical engineering structure.

[0027] According to the above solution, in the random parameter generation module, to obtain the random parameters required for uncertainty numerical analysis, the method is as follows:

[0028] 1) Establish a numerical model according to engineering survey data, including geometric model, finite element mesh division, and boundary condition setting;

[0029] 2) Export the finite element analysis format file (inp file) of the Abaqus numerical model, and extract the element numbers and node numbers in the inp file through Python programming to generate an element center coordinate file;

[0030] 3) Generate the random parameters required for uncertainty analysis based on the generated element center coordinate file to obtain a random field file;

[0031] 4) Verify the correctness of the elastic modulus random field; by selecting the soil elastic modulus parameters at a certain position in the soil layer for statistical analysis, determine whether they are consistent with the statistical parameters of the corresponding soil material structure.

[0032] According to the above solution, in the random parameter generation module, based on the generated element center coordinate file, use Python programming to adopt the random parameter generation method of the Cholesky decomposition midpoint method to generate the random parameters required for uncertainty analysis.

[0033] According to the above solution, the statistical parameters of the soil elastic modulus random field include the soil material structure, the corresponding elastic modulus mean value, coefficient of variation, horizontal correlation distance, and vertical correlation distance.

[0034] The present invention also provides an electronic device, including:

[0035] One or more processors;

[0036] and

[0037] a storage device for storing one or more programs,

[0038] wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of the above solutions.

[0039] The present invention also provides a computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method according to any one of the above solutions.

[0040] The beneficial effects produced by the present invention are:

[0041] 1. The numerical analysis method considering the uncertainty of soil parameters' spatial variability in the present invention generates a random field of multi-layer soil with any shape for uncertainty analysis by using a finite element analysis software to assign random parameters.

[0042] 2. The present invention proposes a numerical analysis method considering the uncertainty of soil parameters' spatial variability, which combines the random field theory with the numerical simulation method, can well consider various uncertainties caused by the spatial variability of natural geotechnical parameters, and makes the model calculation results more in line with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0044] Figure 1 is the flowchart of the method of the embodiment of the present invention.

[0045] Figure 2 is the schematic diagram of the numerical model and mesh division of foundation pit dewatering excavation in the embodiment of the present invention;

[0046] Figure 3 is the schematic diagram of the realization of the random field of elastic modulus of multi-layer soil in the foundation pit in the embodiment of the present invention;

[0047] Figure 4 is the schematic diagram of the verification of the random field of elastic modulus in the embodiment of the present invention;

[0048] Figure 5 is the settlement curve graph of the ground surface measuring points under different construction steps in the embodiment of the present invention;

[0049] Figure 6 is the lateral displacement curve graph of the retaining wall under different construction steps in the embodiment of the present invention;

[0050] Figure 7 is the statistical analysis graph of the calculation results of the ground surface settlement in the embodiment of the present invention;

[0051] Figure 8 It is a statistical analysis diagram of the calculation results of the retaining wall in the embodiment of the present invention;

[0052] Figure 9 It is a diagram showing the change of the statistical characteristic parameters in the embodiment of the present invention with the number of simulation times;

[0053] Figure 10 It is the excessive standard probability distribution diagram in the embodiment of the present invention. Detailed implementation manners

[0054] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] As Figure 1 shown, a numerical analysis method considering the uncertainty of soil parameter spatial variability includes:

[0056] 1) Establish a numerical model according to engineering survey data to obtain the random parameters required for uncertainty numerical analysis; the parameters are the statistical parameters of the soil elastic modulus random field;

[0057] 1.1) Establish a numerical model according to engineering survey data, including geometric model, finite element mesh division and boundary condition setting;

[0058] 1.2) Export the finite element analysis format file (inp file) of the Abaqus numerical model, and extract the element numbers and node numbers in the inp file through Python programming to generate the element center coordinate file;

[0059] 1.3) Based on the generated element center coordinate file, use Python programming to generate the random parameters required for uncertainty analysis by using the random parameter generation method of the Cholesky decomposition midpoint method to obtain the random field file;

[0060] 1.4) Verify the correctness of the elastic modulus random field. By selecting the soil elastic modulus parameters at a certain position of the soil layer for statistical analysis, judge whether it is consistent with the statistical parameters of the corresponding soil material structure.

[0061] 2) Assign the generated random parameters to the finite element cells in the numerical model to establish a multi-layer soil random field numerical model; specifically as follows:

[0062] According to the random field file generated in step 1.3 and the inp file exported in step 1.1, assign the material parameters in the random field file to each cell in Abaqus, and batch generate the inp file to establish a multi-layer soil random field numerical model;

[0063] 3) Input the numerical model of multi-layer soil body random field considering the spatial variability of soil body into Abaqus for cyclic calculation, and conduct data analysis on the calculation results.

[0064] 3.1) Input the inp files batch-generated in step 2.1) into the finite element analysis software Abaqus for cyclic calculation to conduct uncertainty numerical analysis;

[0065] 3.2) Conduct data analysis on the calculation results in step 3.1), count the variation of characteristic parameters with the number of simulations, obtain the influence of the spatial variability of elastic modulus on the calculation results, as well as the failure probability of the corresponding engineering structure, and obtain the deformation prediction of the geotechnical engineering structure.

[0066] According to the above method, the present invention also provides an uncertainty numerical analysis device considering the spatial variability of soil parameters, including:

[0067] A random parameter generation module, configured to establish a numerical model based on engineering survey data to obtain the random parameters required for uncertainty numerical analysis; the parameters are the statistical parameters of the soil body elastic modulus random field;

[0068] A multi-layer soil body random field numerical model establishment module, configured to assign the generated random parameters to the finite element cells in the numerical model to establish a multi-layer soil body random field numerical model; specifically as follows:

[0069] In Abaqus, assign the material parameters in the random field file to each cell respectively, and batch generate inp files to establish a multi-layer soil body random field numerical model;

[0070] An analysis and calculation module, configured to input the numerical model of multi-layer soil body random field considering the spatial variability of soil body into the finite element software for cyclic calculation, and conduct data analysis on the calculation results to obtain the deformation prediction of the geotechnical engineering structure.

[0071] A specific application embodiment:

[0072] Step 1: Generate the random parameters required for uncertainty numerical analysis based on engineering survey data;

[0073] 1.1) Establish a numerical model, use Abaqus software to model with half of the foundation pit size, and its size is 100m×100m. As Figure 2 (a) shows, the excavation depth of the foundation pit H = 47.72m, then the half excavation width is B = 25.15m. Referring to the relevant technical standards of foundation pit engineering, simplify the construction machinery and construction personnel around the foundation pit into a uniform load of 30kPa. As Figure 2As shown in (b), the grid size is taken as 1 m, and the model is divided into 3,871 finite - element units. Among them, the soil and the retaining wall are simulated by solid elements, while the internal supports are simulated by beam elements. The soil parameters and the retaining - wall parameters are consistent with those of the foundation - pit vertical - shaft dewatering model. After the foundation - pit excavation, reinforced - concrete supports are set as temporary internal supports. The sizes of the first and second - layer internal supports are 1.3 m×1.3 m, and the rest are 1.5 m×1.5 m. The elastic modulus of the concrete internal support is 33 GPa, and the Poisson's ratio is 0.3. The construction method of layered dewatering is adopted, that is, before the excavation of each layer of soil, the groundwater level in the pit is lowered to the position of or below the foundation - pit excavation surface;

[0074] 2) Export the inp file of the Abaqus foundation - pit dewatering and excavation numerical model, and extract the element numbers and node numbers in the inp file through Python programming to generate the element - center coordinate file;

[0075] 3) Based on the generated element - center coordinate file, use Python programming to adopt the Cholesky - decomposition mid - point method for generating random parameters required for uncertainty analysis. The statistical parameters are shown in Table 1. Figure 3 It represents the result of a single random - field simulation.

[0076] Table 1 Statistical parameters of the elastic - modulus random field of the foundation - pit soil

[0077]

[0078] 4) Verify the correctness of the elastic - modulus random field by conducting statistical analysis on the elastic - modulus parameters of the soil at a certain position in the soil layer. Among them, the number of simulations of the random field is 1,000 times. Figure 4 It is the curve of the mean value and coefficient of variation of the elastic modulus at a certain position in the three - layer soil varying with the number of random simulations. As the number of random simulations increases, the mean values and coefficients of variation of the elastic moduli of silty clay, silty sand, and mudstone gradually stabilize at the given statistical values of the random - field parameters in Table 1, which verifies the correctness of the established random - field model.

[0079] Step 2) Use the Python secondary - development interface of Abaqus to assign the generated random parameters to the finite - element units in the numerical model to establish a multi - layer soil random - field numerical model;

[0080] In Abaqus, assign the material parameters in the random - field file to each unit respectively, and batch - generate inp files and submit them to Abaqus for cyclic calculation.

[0081] Step 3) Input the multi - layer soil random - field numerical model considering soil spatial variability into Abaqus for cyclic calculation, and conduct data analysis on the calculation results to obtain the statistical distribution of the calculation results.

[0082] 3.1) Ground surface settlement under different construction steps is as Figure 5 shown. As the excavation progresses, the spatial variability of the elastic modulus will gradually intensify the impact on the ground surface settlement, and the impact is more obvious near the location where the maximum settlement occurs. However, the spatial variability of the elastic modulus has no impact on the location where the maximum ground surface settlement occurs. At the same time, it can be seen that in the first 4 construction steps, the impact of the spatial variability of the elastic modulus on the maximum value of the ground surface settlement gradually increases, and the fluctuation ranges are 1.8 mm, 2.9 mm, 5 mm, and 6.2 mm respectively. In the latter 4 construction steps, the impact of the spatial variability of the elastic modulus on the maximum value of the ground surface settlement almost remains the same, and the fluctuation range is about 7 mm.

[0083] 3.2) The lateral displacement of the retaining wall under different construction steps is as Figure 6 shown. As the excavation progresses, the spatial variability of the elastic modulus will gradually intensify the impact on the lateral displacement of the retaining wall, and the impact is more obvious near the location where the maximum lateral displacement occurs. The spatial variability of the elastic modulus has little impact on the location where the maximum lateral displacement of the retaining wall occurs. In the first 4 construction steps, the impact of the spatial variability of the elastic modulus on the maximum value of the lateral displacement of the retaining wall gradually increases, and the fluctuation ranges are 4 mm, 6 mm, 8 mm, and 9 mm respectively. In the latter 4 construction steps, the fluctuation range of the maximum value of the lateral displacement of the retaining wall almost remains the same, all about 10 mm, and the fluctuation range of the lateral displacement at the lower part of the retaining wall becomes larger.

[0084] 3.3) To further study the impact of the spatial variability of the elastic modulus on the ground surface settlement and the lateral displacement of the retaining wall, statistical analysis was carried out on the maximum ground surface settlement and the maximum lateral displacement of the retaining wall after the excavation. As Figure 7 (a) shows, the spatial variability of the elastic modulus will have a significant impact on the maximum value of the ground surface settlement, and the frequency distribution is similar to the normal distribution, with 80% located in the interval [12, 15] mm. From Figure 7 (b), it can be seen that the spatial variability of the elastic modulus has little impact on the distribution of the maximum ground surface settlement location, which is concentrated at about 13 m away from the retaining wall.

[0085] 3.4) From Figure 8 (a), it can be seen that the spatial variability of the elastic modulus will have a significant impact on the maximum lateral displacement of the retaining wall, and the distribution is similar to the normal distribution, with 80% located in the interval [37, 42] mm. From Figure 8 (a), it can be seen that the distribution of the maximum lateral displacement location of the retaining wall is little affected by the spatial variability of the elastic modulus, which is concentrated at about 28 m in depth.

[0086] 3.5) From Figure 9It can be seen that the mean value of the maximum deformation and the coefficient of variation tend to be stable when the number of random simulations reaches 500 times and 200 times respectively. The mean value of the maximum ground settlement is about 13.7 mm, and the coefficient of variation is 0.08. The mean value of the maximum lateral displacement of the retaining wall is about 39.5 mm, and the coefficient of variation is 0.035. The mean value of the maximum ground settlement and the mean value of the maximum lateral displacement of the retaining wall are basically the same as the results of the deterministic analysis.

[0087] 3.6) Figure 10 The cumulative probability distribution curves of the maximum ground settlement and the maximum lateral displacement of the retaining wall exceeding the warning values are given respectively. For this foundation pit project, the horizontal deformation control value of the retaining structure needs to be less than 0.15%H (foundation pit depth) and not greater than 40 mm, and the maximum ground settlement control value needs to be less than 0.15%H (foundation pit depth) and not greater than 30 mm. It can be seen that the spatial variability of the elastic modulus has a greater impact on the discreteness of the calculation results. The maximum ground settlement hardly exceeds the warning value, while there is a certain probability that the maximum lateral displacement of the retaining wall exceeds the warning value, which needs attention.

[0088] It should be understood that those of ordinary skill in the art can make improvements or transformations according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. An uncertainty numerical analysis method considering the spatial variability of soil parameters, characterized in that Including the following steps: 1) Establish a numerical model based on engineering survey data to obtain the random parameters required for uncertainty numerical analysis; the parameters are the statistical parameters of the soil elastic modulus random field; 2) Assign the generated random parameters to the finite element elements in the numerical model to establish a multi-layer soil random field numerical model; specifically as follows: In Abaqus, assign the material parameters in the random field file to each element respectively, and batch generate inp files to establish a multi-layer soil random field numerical model; 3) Input the multi-layer soil random field numerical model considering soil spatial variability into Abaqus for cyclic calculation, and perform data analysis on the calculation results to obtain the deformation prediction of the geotechnical engineering structure.

2. The uncertainty numerical analysis method considering the spatial variability of soil parameters according to claim 1, characterized in that, In step 1), to obtain the random parameters required for uncertainty numerical analysis, specifically as follows: 1.1) Establish a numerical model based on engineering survey data, including geometric model, finite element mesh division, and boundary condition setting; 1.2) Export the finite element analysis format file (inp file) of the Abaqus numerical model, and extract the element numbers and node numbers in the inp file through Python programming to generate an element center coordinate file; 1.3) Generate the random parameters required for uncertainty analysis based on the generated element center coordinate file to obtain a random field file; 1.4) Verify the correctness of the elastic modulus random field; By selecting the soil elastic modulus parameters at a certain position in the soil layer for statistical analysis, judge whether they are consistent with the statistical parameters of the corresponding soil material structure.

3. The uncertainty numerical analysis method considering the spatial variability of soil parameters according to claim 2, characterized in that, In step 1.3), based on the generated element center coordinate file, use Python programming to adopt the random parameter generation method of the Cholesky decomposition midpoint method to generate the random parameters required for uncertainty analysis.

4. The uncertainty numerical analysis method considering the spatial variability of soil parameters according to claim 1, characterized in that The statistical parameters of the soil elastic modulus random field include the soil material structure, the corresponding mean elastic modulus, coefficient of variation, horizontal correlation distance, and vertical correlation distance.

5. The uncertainty numerical analysis method considering the spatial variability of soil parameters according to claim 1, characterized in that In step 3), specifically as follows: 3.1) Input the inp files batch-generated in step 2.1) into the finite element analysis software Abaqus for cyclic calculation to perform uncertainty numerical analysis; 3.2) Perform data analysis on the calculation results of step 3.1), count the changes of statistical characteristic parameters with the number of simulation times, obtain the influence of the spatial variability of the elastic modulus on the corresponding engineering structure, and the failure probability of the corresponding engineering structure.

6. An uncertainty numerical analysis device considering the spatial variability of soil parameters, characterized in that, Including: A random parameter generation module, used to establish a numerical model based on engineering survey data to obtain the random parameters required for uncertainty numerical analysis; the parameters are the statistical parameters of the soil elastic modulus random field; A multi-layer soil random field numerical model establishment module, used to assign the generated random parameters to the finite element elements in the numerical model to establish a multi-layer soil random field numerical model; specifically as follows: In Abaqus, assign the material parameters in the random field file to each element respectively, and batch generate inp files to establish a multi-layer soil random field numerical model; An analysis and calculation module is configured to input a numerical model of a multi-layer soil random field considering the spatial variability of soil into a finite element software for iterative calculations, and perform data analysis on the calculation results to obtain deformation predictions for geotechnical engineering structures.

7. The uncertainty numerical analysis device considering the spatial variability of soil parameters according to claim 6, characterized in that In the random parameter generation module, the random parameters required for uncertainty numerical analysis are obtained as follows: 1) Establish a numerical model based on engineering survey data, including a geometric model, finite element mesh generation, and boundary condition setting; 2) Export the finite element analysis format file (inp file) of the Abaqus numerical model, and extract the element numbers and node numbers in the inp file through Python programming to generate an element center coordinate file; 3) Generate the random parameters required for uncertainty analysis based on the generated element center coordinate file to obtain a random field file; 4) Verify the correctness of the elastic modulus random field; By selecting the soil elastic modulus parameter at a certain position in the soil layer for statistical analysis, determine whether it is consistent with the statistical parameters of the corresponding soil material structure.

8. The uncertainty numerical analysis device considering the spatial variability of soil parameters according to claim 7, wherein in the random parameter generation module, the random parameters required for uncertainty analysis are generated by using the Cholesky decomposition midpoint method random parameter generation method based on the generated element center coordinate file through Python programming.

9. An electronic device, comprising: One or more processors; And A storage device for storing one or more programs, Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1 to 5.

Images