Method and system for determining design parameters of pile-supported reinforced embankment

Abstract

The invention provides a method and a system for determining design parameters of a pile-supported reinforced embankment. The method comprises the following steps: acquiring a plurality of embankment design parameter combinations; each embankment design parameter combination comprises a technical parameter combination and an economic parameter combination; respectively creating a corresponding python modeling script based on each technical parameter combination; calling the python modeling script of each combination through an API (Application Program Interface) of finite element software so as to automatically construct an embankment three-dimensional finite element model corresponding to each combination; applying a simulation load to the embankment three-dimensional finite element model corresponding to each combination to obtain an embankment settlement prediction result corresponding to each combination; calculating the embankment design cost corresponding to each embankment three-dimensional finite element model based on each economic parameter combination; and determining the embankment design parameters of the optimal combination based on the embankment settlement prediction result and the embankment design cost corresponding to each combination. Through interaction of finite element software and python, automatic modeling is realized, the modeling difficulty is reduced, and the design efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of engineering technology, and in particular to a method and system for determining design parameters of a pile-supported reinforced embankment. Background Art

[0002] At present, in order to obtain a better embankment design scheme, it is generally determined by comparing parameters such as settlement results and stress of reinforcement materials. The settlement results, stress of reinforcement materials and other data are obtained by modeling and simulating the pile-supported reinforced embankment through 3D modeling software.

[0003] However, due to the complexity of 3D modeling software, designers need to have high professional skills, which limits its wide application in actual engineering. In addition, manual modeling leads to low efficiency of embankment design. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a method and system for determining design parameters of a pile-supported reinforced embankment, so as to reduce the difficulty of modeling and improve the efficiency of embankment modeling, thereby improving the efficiency of determining embankment design parameters.

[0005] In a first aspect, a method for determining design parameters of a pile-supported reinforced embankment is provided, comprising:

[0006] Acquire multiple embankment design parameter combinations; each embankment design parameter combination includes a technical parameter combination and an economic parameter combination;

[0007] Create corresponding python modeling scripts based on each combination of technical parameters;

[0008] The Python modeling scripts of each combination are called through the API interface of the finite element software to automatically construct the 3D finite element model of the embankment corresponding to each combination;

[0009] Apply simulated loads to the three-dimensional finite element model of the embankment corresponding to each combination, and obtain the predicted results of the embankment settlement corresponding to each combination;

[0010] Based on various economic parameter combinations, the embankment design cost corresponding to each 3D finite element model of the embankment is calculated;

[0011] The optimal combination of embankment design parameters is determined based on the embankment settlement prediction results and embankment design costs corresponding to each combination.

[0012] Optionally, obtaining multiple embankment design parameter combinations includes:

[0013] Obtain embankment design parameters from pre-built embankment design tables based on Python preset instructions;

[0014] The embankment design parameters are divided into variable parameters and immutable parameters. Variable parameters refer to the parameters that can be adjusted during the design process, including at least pile parameters, pile cap parameters and reinforcement material parameters; immutable parameters include at least soil parameters, embankment parameters and economic parameters.

[0015] According to the value range and step size of the variable parameters, the variable parameters and the immutable parameters are combined to generate multiple embankment design parameter combinations.

[0016] Optionally, the method further comprises:

[0017] The soil parameters are automatically converted into soft soil creep parameters through the soft soil creep conversion formula built into the embankment design table;

[0018] Compare the calculated soft soil creep parameters with the value range of local soft soil;

[0019] If it is within the value range of local soft soil, the calculated soft soil creep parameter is determined as the final soft soil creep parameter;

[0020] If it exceeds the value range of the local soft soil, the average value of the value range of the local soft soil is determined as the final soft soil creep parameter.

[0021] Optionally, after creating a corresponding Python modeling script based on the technical parameters of each combination, the method further includes:

[0022] Generate a modeling task based on each combined Python modeling script;

[0023] Each modeling task is packaged and sent to a pre-built parallel task processing system, so that the task scheduling node in the parallel task processing system can distribute each modeling task to multiple task execution nodes.

[0024] Optionally, calling the python modeling script of each combination through the API interface of the finite element software to automatically construct the 3D finite element model of the embankment corresponding to each combination includes:

[0025] The Python modeling script corresponding to the received modeling task is called through the API interface of the finite element software of each task execution node;

[0026] Generate multiple virtual modeling instances according to the python modeling script;

[0027] Encapsulate each virtual modeling instance in a separate container;

[0028] The three-dimensional finite element model of the embankment corresponding to each combination is automatically constructed based on the Python modeling script corresponding to each virtual modeling instance.

[0029] Optionally, the virtual modeling instance includes at least a boundary instance, a soil layer instance, a pile instance, an embankment instance and a reinforcement material instance, and the embankment three-dimensional finite element model corresponding to each combination is automatically constructed based on the Python modeling script corresponding to each virtual modeling instance, including:

[0030] Based on the boundary conditions in the boundary instance, the boundary of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script;

[0031] Based on the soft soil creep parameters in the soil layer instance, the four-layer soft soil foundation of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script;

[0032] Based on the pile parameters, pile cap parameters and elastoplastic model in the pile instance, the pile body of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script;

[0033] Based on the embankment parameters and Mohr-Coulomb model in the embankment example, the embankment of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script. The embankment includes the pavement structure, embankment filling material and sand cushion layer.

[0034] Based on the reinforcement material parameters and elastic-plastic model in the reinforcement material example, the positive and negative interfaces of the reinforcement material of the 3D finite element model of the embankment are automatically created through the corresponding Python modeling script.

[0035] Optionally, applying a simulated load to the three-dimensional finite element model of the embankment corresponding to each combination, and obtaining the embankment settlement prediction results corresponding to each combination include:

[0036] The mesh method is used to mesh the three-dimensional finite element model of the embankment corresponding to each combination;

[0037] The corresponding simulation loads are applied to the meshed three-dimensional finite element model of the embankment according to the construction stage, and the embankment settlement prediction results corresponding to each combination are obtained.

[0038] Optionally, after obtaining the settlement prediction results corresponding to each combination, the method further includes:

[0039] Extract the settlement prediction results into Python through the API interface of the finite element software;

[0040] Convert the settlement prediction results into a standardized data format through a Python visual script;

[0041] Preprocessing the settlement prediction results after standardization, which at least includes data screening, unit conversion and average value calculation for a specific area;

[0042] The preprocessed settlement prediction results are used to generate charts or animations through the Python visualization library.

[0043] Optionally, the embankment design parameters of the optimal combination are determined based on the embankment settlement prediction results and embankment design costs corresponding to each combination, including:

[0044] Calculate the expected value of embankment settlement prediction results and embankment design cost for each combination respectively;

[0045] The total expected value of each combination is calculated based on the expected value of embankment settlement prediction results and embankment design cost;

[0046] The embankment design parameters corresponding to the combination whose total expected value is closest to 1 are determined as the embankment design parameters of the optimal combination.

[0047] In a second aspect, a system for determining a design scheme of a pile-supported reinforced embankment is provided, comprising:

[0048] An acquisition unit is used to acquire multiple embankment design parameter combinations; each embankment design parameter combination includes a technical parameter combination and an economic parameter combination;

[0049] A creation unit is used to create corresponding Python modeling scripts based on various technical parameter combinations;

[0050] A calling unit, used for calling the python modeling script of each combination through the API interface of the finite element software so as to automatically construct the three-dimensional finite element model of the embankment corresponding to each combination;

[0051] A load simulation unit is used to apply a simulated load to the three-dimensional finite element model of the embankment corresponding to each combination to obtain the predicted result of the embankment settlement corresponding to each combination;

[0052] A calculation unit, used for respectively calculating the embankment design cost corresponding to each embankment three-dimensional finite element model based on each economic parameter combination;

[0053] The determination unit is used to determine the embankment design parameters of the optimal combination based on the embankment settlement prediction results and embankment design costs corresponding to each combination.

[0054] The embodiment of the present invention provides a method and system for determining design parameters of a pile-supported reinforced embankment, which obtains multiple combinations of embankment design parameters; creates corresponding python modeling scripts based on the technical parameters of each combination; calls the python modeling scripts of each combination through the API interface of the finite element software to automatically construct the three-dimensional finite element model of the embankment corresponding to each combination; applies simulated loads to the three-dimensional finite element model of the embankment corresponding to each combination to obtain the embankment settlement prediction results corresponding to each combination; calculates the embankment design cost corresponding to each three-dimensional finite element model of the embankment based on each economic parameter combination; and determines the embankment design parameters of the optimal combination based on the embankment settlement prediction results and embankment design costs corresponding to each combination. The embodiment of the present invention realizes automatic modeling through the interaction of finite element software and python, without the need to directly operate complex finite element software, reduces the difficulty of modeling, and can quickly obtain accurate calculation results, improves design efficiency, and provides powerful technical support and tools for engineering designers.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 A flow chart showing a method for determining design parameters of a pile-supported reinforced embankment provided by an embodiment of the present invention is shown;

[0058] Figure 2 An overall model diagram of a three-dimensional finite element model of an embankment provided by an embodiment of the present invention is shown;

[0059] Figure 3 A perspective view of a three-dimensional finite element model of an embankment provided by an embodiment of the present invention is shown;

[0060] Figure 4 A structural schematic diagram of a system for determining design parameters of a pile-supported reinforced embankment provided by an embodiment of the present invention is shown.

[0061] Explanation of main component symbols: 1. Pavement structure; 2. Embankment filling; 3. Sand cushion layer; 4. First layer of soft soil foundation; 5. Second layer of soft soil foundation; 6. Third layer of soft soil foundation; 7. Fourth layer of soft soil foundation; 8. Traffic load; 9. Pile cap; 10. Pile body; 11. Reinforcement material. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0063] Considering that the operation of 3D modeling software is complicated, designers need to have high professional skills, which limits its wide application in actual engineering. In addition, manual modeling leads to low efficiency of embankment design. Based on this, the embodiment of the present invention provides a method and system for determining design parameters of a pile-supported reinforced embankment, which is described below through embodiments.

[0064] The embodiment of the present invention provides a method for determining design parameters of a pile-supported reinforced embankment. Figure 1 As shown, including:

[0065] Step S101: Acquire multiple embankment design parameter combinations; each embankment design parameter combination includes a technical parameter combination and an economic parameter combination.

[0066] In a feasible implementation, a web front-end interface for embankment modeling can be created for the user. In the front-end interface, a parameter configuration unit is configured, and the embankment design parameters can be manually configured through the parameter configuration unit, or the back-end can automatically obtain multiple pre-stored embankment design parameter combinations by clicking the automatic button.

[0067] Step S102: Create corresponding Python modeling scripts based on each combination of technical parameters.

[0068] When there are multiple technical parameter combinations, Python modeling scripts can be created in batches at the same time to improve efficiency.

[0069] Step S103: calling the python modeling script of each combination through the API interface of the finite element software to automatically construct the 3D finite element model of the embankment corresponding to each combination.

[0070] In this step, the finite element software is, for example, PLAXIS 3D, Abaqus, Flac3D, etc. An interactive channel between the finite element software and Python is established in advance. Specifically, select "Configure remote script server" in PLAXIS 3D, set the port and password, and interact with the PLAXIS server through the plxscripting module in Python.

[0071] In another embodiment, the constructed three-dimensional finite element model of the embankment may be presented in the web front-end interface exemplified in step S101.

[0072] Step S104: applying a simulated load to the three-dimensional finite element model of the embankment corresponding to each combination to obtain the embankment settlement prediction result corresponding to each combination.

[0073] In the embodiment of the present application, the simulated load is, for example, a traffic load. In the PLAXIS 3D software, Surfaceload can be used to apply the traffic load 8 to the pile-supported reinforced embankment model.

[0074] Step S105: Calculate the embankment design cost corresponding to each embankment three-dimensional finite element model based on each economic parameter combination.

[0075] In this step, economic parameters such as the unit price of piles, the price of reinforcement materials, etc.

[0076] In step S104 and step S105, the pre-constructed linear regression equation can be used to calculate the embankment settlement prediction result and the embankment design cost. The linear regression equation is as follows:

[0077]

[0078] Where Y is the objective function, i.e. settlement or cost; X i is a variable parameter, including at least pile parameters, pile cap parameters and reinforcement material parameters; β0 is the intercept term; β i is the linear coefficient of the factor; β ii is the quadratic coefficient of the factor; β ij is the coefficient of the interaction between the factors.

[0079] In a specific example, the data of various factors, settlement and cost are as follows in Table 1:

[0080]

[0081]

[0082] Step S106: Determine the embankment design parameters of the optimal combination based on the embankment settlement prediction results and embankment design costs corresponding to each combination.

[0083] In another embodiment, a certain reduction coefficient may be assigned to the optimal combination of embankment design parameters.

[0084] The optimal combination of embankment design parameters is determined by comprehensively comparing the embankment settlement prediction results and embankment design costs.

[0085] The embodiment of the present invention realizes automatic modeling through the interaction of finite element software and Python, without the need to directly operate complex finite element software, thus reducing the difficulty of modeling, and can quickly obtain accurate calculation results, thereby improving design efficiency and providing powerful technical support and tools for engineering designers.

[0086] Based on the above embodiment, obtaining multiple combinations of embankment design parameters includes:

[0087] Step S1011: Obtain embankment design parameters in a pre-constructed embankment design table based on Python preset instructions.

[0088] In this step, the pre-constructed embankment design table is, for example, an Excel table. In one example, these embankment design parameters can be obtained from geological survey reports, design materials or on-site collection, and input into the Excel table by relevant personnel.

[0089] Among them, the embankment design forms include embankment design sub-table, soil layer sub-table and material sub-table.

[0090] Step S1012: Divide the embankment design parameters into variable parameters and immutable parameters. Variable parameters refer to parameters that can be adjusted during the design process, including at least pile parameters, pile cap parameters and reinforcement material parameters; immutable parameters include at least soil parameters, embankment parameters and economic parameters.

[0091] In the embodiments of the present application, variable parameters are parameters that can be adjusted during the design process, such as pile length, pile spacing, whether to add reinforcement, etc. Immutable parameters are related to the project itself, such as foundation soil parameters, geological conditions, etc., and are usually not adjusted during the optimization process. These parameters remain unchanged during the dynamic programming process.

[0092] Step S1013: combining the variable parameters and the immutable parameters according to the value range and step size of the variable parameters to generate a plurality of embankment design parameter combinations.

[0093] For example, the range of pile length is 10-15 meters, with a step length of 0.5 meters; the range of pile spacing is 2-3 meters, with a step length of 0.2 meters. The pile length increases by 0.5 meters from 10 meters to 15 meters; the pile spacing increases by 0.2 meters from 2 meters to 3 meters. Through these parameter combinations, the system can automatically generate a large number of combined design parameters. For example, if there are 10 and 5 values ​​for pile length and pile spacing respectively, there are a total of 50 combinations.

[0094] At present, domestic standards and British standard BS8006 only consider the influence of upper embankment load, pile spacing and pile cap on the tension of reinforced materials when calculating the tension of reinforced materials, while ignoring the effect of soil between piles. However, the settlement change caused by soft soil creep will significantly affect the stress of reinforced materials.

[0095] Therefore, based on the above embodiment, the method further includes:

[0096] Step S1014: The soil parameters are automatically converted into soft soil creep parameters by using the soft soil creep conversion formula built into the embankment design table.

[0097] In this step, soil parameters are stored in the soil layer table, including, for example, gravity γ, initial void ratio e0, compression modulus Es1-2, internal friction angle Permeability coefficient k, etc.

[0098] The creep parameters of soft soil include the modified rebound coefficient κ * , Modified compression index λ * , Modified creep index μ * .

[0099] The soft soil creep conversion formula is as follows:

[0100]

[0101] Where, p = 100 kPa; C c is the compression ratio.

[0102] This embodiment takes into account the creep characteristics of soft soil, thereby improving the accuracy of long-term settlement prediction after construction and the calculation accuracy of the tension of reinforcement materials, and is suitable for the design and analysis of pile-supported high-strength reinforced embankments in deep soft foundations.

[0103] The study on the sensitivity of SSC model parameters shows that the modified compression index λ * and modified creep index μ * The value of will greatly affect the settlement of soft soil foundation, among which the modified compression index λ * Determines the deformation in the initial development stage of foundation settlement, and the modified creep exponent μ *It mainly affects the long-term settlement of the foundation. Therefore, if only the parameters estimated by the above formula are used in the modeling process, the estimation of long-term settlement will be relatively inaccurate. Therefore, in order to improve the accuracy of numerical calculation, the following steps are adopted.

[0104] Step S1015: Compare the calculated soft soil creep parameter with the value range of the local soft soil.

[0105] Step S1016: If it is within the value range of the local soft soil, the calculated soft soil creep parameter is determined as the final soft soil creep parameter.

[0106] Step S1017: If it exceeds the value range of the local soft soil, the average value of the value range of the local soft soil is determined as the final soft soil creep parameter.

[0107] In an example, as shown in Table 2 below, the local value range of soft soil creep parameters in some areas of Zhejiang Province is given:

[0108]

[0109] In another feasible implementation, if there are test conditions, soil can be directly taken from the project site for relevant tests, and the accurate λ can be calculated according to formulas (5) to (7). * , κ * , μ * Value. Can greatly improve accuracy.

[0110] Specifically, a one-dimensional consolidation creep test can be carried out to obtain an accurate modified compression index λ based on the relationship between soil stress, strain and time. * , Modified Rebound Index κ * and modified creep index μ * .

[0111] Modified compression index λ * Through ε H -lnσ curve is obtained:

[0112]

[0113] Where: ε H is the logarithmic strain; σ is the stress applied to the soil; V is the volume of the soil sample after loading; V0 is the original volume of the soil sample.

[0114] Modified Rebound Index κ * It can be obtained through the e-lnp curve, that is, the average slope of the soil rebound curve and the recompression curve.

[0115] Modified creep index μ * Through ε V -lnt curve acquisition:

[0116]

[0117] Where: ε V is the creep rate of soil; ε is the soil strain; h0 is the height of soil sample; t is the time point selected when each level of load is applied to the soil.

[0118] In traditional batch modeling of Python and PLAXIS interaction, serial operation is usually used, that is, after one model is calculated, the calculation of the next model will start. This method is inefficient, especially when processing a large number of models. Users need to wait for each model to be completed in turn, and the overall calculation time will become very long.

[0119] Therefore, based on the above embodiment, after creating the corresponding Python modeling script based on the technical parameters of each combination, the method further includes:

[0120] Step S107: Generate a modeling task according to the Python modeling script of each combination.

[0121] Step S108: Packaging each modeling task and sending it to a pre-built parallel task processing system, so that the task scheduling node in the parallel task processing system can distribute each modeling task to multiple task execution nodes.

[0122] Among them, the parallel task processing system is built by building a Kubernetes cluster, which consists of task scheduling nodes and task execution nodes. The task scheduling nodes can automatically schedule and balance tasks, dynamically allocate computing resources, and ensure the smooth execution of parallel computing tasks.

[0123] Since the traditional serial calculation method is inefficient and cannot meet the requirements of completing multiple design tasks in a short time, the parallel calculation method of the present invention can run multiple design schemes at the same time and quickly obtain calculation results, thereby greatly shortening the design cycle and meeting the requirements of engineering progress.

[0124] Based on the above embodiment, calling the python modeling script of each combination through the API interface of the finite element software to automatically construct the 3D finite element model of the embankment corresponding to each combination includes:

[0125] Step S1031: calling the Python modeling script corresponding to the received modeling task through the API interface of the finite element software of each task execution node.

[0126] In this step, the Python modeling script corresponding to the received modeling task can be called through the API interface of the PLAXIS 3D software.

[0127] Step S1032: Generate multiple virtual modeling instances according to the python modeling script.

[0128] In this step, a virtual PLAXIS instance is specifically generated. A virtual PLAXIS instance refers to a virtual engineering project created using the PLAXIS software.

[0129] Step S1033: Encapsulate each virtual modeling instance in an independent container.

[0130] Encapsulating each PLAXIS instance in an independent container ensures that each PLAXIS instance runs in an independent environment, avoiding software dependency conflicts and improving model portability and repeatability.

[0131] Step S1034: Automatically construct the three-dimensional finite element model of the embankment corresponding to each combination based on the Python modeling script corresponding to each virtual modeling instance.

[0132] Since most design units are not equipped with large servers or computing clusters, they usually only have personal computers or a small number of workstations to perform model calculations. Faced with complex design tasks or large batch modeling requirements, the computing power of personal computers is very limited. Through the virtualization technology of the present invention, a large number of virtual PLAXIS instances can be run through a small number of physical devices to achieve parallel computing and make full use of existing hardware resources. Without the need for large-scale investment in purchasing servers, complex computing tasks can be completed efficiently, thereby reducing hardware investment costs.

[0133] Based on the above embodiment, the virtual modeling instance includes at least a boundary instance, a soil layer instance, a pile instance, an embankment instance and a reinforcement material instance; based on the Python modeling script corresponding to each virtual modeling instance, automatically constructing the embankment three-dimensional finite element model corresponding to each combination includes the following steps: the constructed embankment three-dimensional finite element model is as follows: Figure 2 and Figure 3 As shown:

[0134] Step S1034A: Based on the boundary conditions in the boundary instance, the boundary of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script.

[0135] In this step, the boundary conditions are fixed boundary conditions or free boundary conditions.

[0136] Step S1034B: Based on the soft soil creep parameters in the soil layer instance, the four-layer soft soil foundation of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script. From top to bottom, they are the first layer of soft soil foundation 4, the second layer of soft soil foundation 5, the third layer of soft soil foundation 6, and the fourth layer of soft soil foundation 7.

[0137] In the PLAXIS 3D software, the four layers of soft soil foundation are named silty clay, silt, clay and silt.

[0138] In PLAXIS 3D, soil layers can be automatically created using the drilling tool.

[0139] Step S1034C: Based on the pile parameters, pile cap 9 parameters and elastoplastic model in the pile instance, the pile body 10 of the 3D finite element model of the embankment is automatically created through the corresponding python modeling script.

[0140] In PLAXIS 3D software, the pile is created using embedded beam row units, and the pile cap 9, pile body 10, and pile-soil interface are characterized using an elastoplastic model.

[0141] Step S1034D: Based on the embankment parameters and the Mohr-Coulomb model in the embankment example, a three-dimensional finite element model of the embankment is automatically created through the corresponding python modeling script, and the embankment includes a pavement structure 1, an embankment filling 2, and a sand cushion layer 3.

[0142] Step S1034E: Based on the parameters of the reinforced material 11 and the elastic-plastic model in the reinforced material 11 instance, the positive and negative interfaces of the reinforced material 11 of the three-dimensional finite element model of the embankment are automatically created through the corresponding python modeling script.

[0143] In PLAXIS 3D software, Geogrid units are used to create high-strength reinforced materials. The reinforced materials adopt an elastoplastic model, and positive and negative interfaces of the reinforced materials are created so that they can move with the soil and pile cap when subjected to force, thereby more accurately reflecting the lifting effect of the reinforced materials in pile-supported embankments.

[0144] Most existing calculation methods cannot consider soil arching and scooping at the same time. Usually, the pile-soil stress ratio under soil arching is calculated first, and then the load borne by the soil between piles is borne by the reinforcement material. However, in actual engineering, soil arching and scooping exist at the same time and affect each other. PLAXIS 3D modeling can effectively reflect this interactive relationship.

[0145] Based on the above embodiment, applying simulated loads to the three-dimensional finite element model of the embankment corresponding to each combination to obtain the embankment settlement prediction results corresponding to each combination includes the following steps:

[0146] Step S1041: meshing the three-dimensional finite element model of the embankment corresponding to each combination using the mesh method.

[0147] In the embodiment of the present invention, mesh=0.6, which belongs to medium-precision division.

[0148] Step S1042: applying corresponding simulation loads to the meshed three-dimensional finite element model of the embankment according to the construction stage to obtain the embankment settlement prediction results corresponding to each combination.

[0149] There are 5 construction stages in the embodiment of the present invention:

[0150] 1) Initial stage: According to existing studies, when the soft soil creep model is used, the surface settlement caused by the soil self-weight stress cannot be ignored. Therefore, the calculation type is set to gravity load and pre-consolidation is 10 years (gravity load calculation);

[0151] 2) Pile foundation and sand cushion: Activate the pile unit, pile cap unit and sand cushion, the construction time is 30 days (consolidation calculation);

[0152] 3) Filling: Activate the embankment fill and pavement structure, the construction time is 90 days (consolidation calculation);

[0153] 4) Equal load preloading: activation traffic load 20kPa, preloading time 180 days (consolidation calculation);

[0154] 5) Long-term operation: Activate all structures and loads, and operate for 15 years (consolidation calculation).

[0155] Through multi-stage construction simulation, the settlement prediction results such as embankment settlement under different working conditions, maximum lateral and longitudinal tension of reinforcement materials, maximum bending moment of pile body and pile cap pressure are analyzed and extracted.

[0156] In one example, the settlement prediction results are shown in Table 3 below:

[0157]

[0158] Based on the above embodiment, after obtaining the settlement prediction results corresponding to each combination, the method further includes:

[0159] The settlement prediction results are extracted into Python through the API interface of the finite element software.

[0160] The embodiment of the present invention can interact with the PLAXIS server through the plxscripting module in Python to extract the settlement prediction results, wherein the nodes of embankment settlement are the center point of the bottom of the embankment, the center point of the top of the road surface and the position of the shoulder; the settlement data can be obtained by reading the data of each point. In addition, the pile cap pressure can be read, and the node of the pile cap pressure is the top of the pile cap on the center line of the embankment; the internal force of the reinforcement material is read through the Geogrid unit, and the longitudinal maximum tension N1 and N2 of the reinforcement material are automatically given; the internal force of the pile is read through the Embedded beam row unit.

[0161] The settlement prediction results are converted into a standardized data format through a Python visual script.

[0162] After PLAXIS 3D has completed the calculation, the results are usually output in the form of files, including data such as settlement, stress, deformation, etc. Convert them into a standardized data format suitable for further processing.

[0163] The settlement prediction results after standardization are preprocessed, and the preprocessing at least includes data screening, unit conversion and average value calculation for a specific area.

[0164] The purpose of this step is to convert the results of the calculation into a form suitable for visualization. For example, the settlement data may need to be converted into a grid for 2D or 3D plotting.

[0165] The preprocessed settlement prediction results are used to generate charts or animations through the Python visualization library.

[0166] Depending on different needs, you can generate a two-dimensional settlement distribution map or a three-dimensional model map. For example, use the Matplotlib library to draw a two-dimensional settlement distribution map. If three-dimensional visualization is required, you can use the plot_surface() function to generate a three-dimensional settlement model.

[0167] In addition, to better display the time-related calculation results or the comparison of different design parameters, interactive charts or animations can be generated. For example, use Plotly to generate a scalable and rotatable three-dimensional graph, or use Matplotlib's FuncAnimation() to generate a frame-by-frame animation to show the changes in each stage of the calculation process.

[0168] In another embodiment, the visualization results can also be embedded in the report or interface. Specifically, it is exported as a static image (such as PNG, JPEG) or a dynamic video (such as MP4, GIF) to be embedded in the design report. This method not only reduces the time of manual drawing, but also ensures the consistency of the calculation results and the chart. In addition, if an interactive chart (such as Plotly's HTML output) is used, the results can be directly displayed on the web front-end interface or the PLAXIS 3D interface to achieve dynamic result display.

[0169] By visualizing the settlement prediction results, the following effects can be achieved:

[0170] (1) Intuitive visualization: The results are presented in charts or three-dimensional graphics, making complex geotechnical calculation results clear at a glance and greatly simplifying the interpretation of data.

[0171] (2) Strong interactivity: Users can adjust input parameters (such as pile length, pile spacing, etc.) to view model changes and corresponding calculation results in real time, which can optimize the design more quickly.

[0172] (3) Improve efficiency: Visualization results can quickly help design teams or decision makers identify problems, understand the pros and cons of design solutions, and reduce analysis time.

[0173] (4) Simplify communication: Visualization is easier to understand, and people without professional backgrounds can quickly obtain key information, which promotes communication and decision-making among teams.

[0174] Based on the above embodiment, the embankment design parameters of the optimal combination are determined based on the embankment settlement prediction results and embankment design costs corresponding to each combination, including:

[0175] The predicted results of embankment settlement and the expected value of embankment design cost of each combination are calculated respectively.

[0176] In this step, the expected values ​​of embankment settlement prediction results and embankment design costs can be calculated by the following formulas:

[0177]

[0178] Where i = 1 or 2, d1 represents the expected value of the embankment settlement prediction result, d2 represents the expected value of the embankment design cost; Y1 represents the embankment settlement prediction result; Y2 represents the embankment design cost; Y 1(max) Indicates the preset maximum settlement, Y 1(min) Indicates the preset minimum settlement, which can be set according to local regulations; Y 2(max) represents the preset maximum embankment design cost, Y 2(min) represents the preset minimum embankment design cost; w i is the weight corresponding to each i.

[0179] The total expected value of each combination is calculated based on the expected value of embankment settlement prediction results and embankment design cost;

[0180] In one example, this can be calculated as follows:

[0181]

[0182] Where D is the total expectation.

[0183] The embankment design parameters corresponding to the combination whose total expected value is closest to 1 are determined as the embankment design parameters of the optimal combination.

[0184] In the embodiment of the present invention, the closer the total expected value is to 1, the better the regression model fits, and therefore the more accurate the predicted settlement and embankment design cost is.

[0185] The following is a table 4 of the actual optimization results:

[0186]

[0187] Based on the same inventive concept, a system for determining a design scheme of a pile-supported reinforced embankment is provided. Figure 4 As shown, including:

[0188] An acquisition unit 401 is used to acquire multiple embankment design parameter combinations; each embankment design parameter includes a technical parameter combination and an economic parameter combination;

[0189] A creation unit 402 is used to create corresponding Python modeling scripts based on each combination of technical parameters;

[0190] A calling unit 403 is used to call the python modeling script of each combination through the API interface of the finite element software so as to automatically construct the 3D finite element model of the embankment corresponding to each combination;

[0191] The load simulation unit 404 is used to apply a simulated load to the three-dimensional finite element model of the embankment corresponding to each combination to obtain the embankment settlement prediction result corresponding to each combination;

[0192] A calculation unit 405 is used to calculate the embankment design cost corresponding to each embankment three-dimensional finite element model based on each economic parameter combination;

[0193] The determination unit 406 is used to determine the embankment design parameters of the optimal combination based on the embankment settlement prediction results and embankment design costs corresponding to each combination.

[0194] An embodiment of the present invention also provides a computer program product for a method for determining design parameters of a pile-supported reinforced embankment, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0195] The embodiment of the present invention also provides a device for determining the design parameters of a pile-supported reinforced embankment, which can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0196] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0197] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, each functional unit in the embodiment provided by the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0199] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0200] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0201] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for determining design parameters of pile-supported reinforced embankments, characterized in that: include: Acquire multiple embankment design parameter combinations; each of the embankment design parameter combinations includes a technical parameter combination and an economic parameter combination; Create corresponding python modeling scripts based on each combination of technical parameters; The Python modeling scripts of each combination are called through the API interface of the finite element software to automatically construct the 3D finite element model of the embankment corresponding to each combination; Applying simulated loads to the three-dimensional finite element models of the embankment corresponding to each combination to obtain the predicted results of embankment settlement corresponding to each combination; Based on various economic parameter combinations, the embankment design cost corresponding to each 3D finite element model of the embankment is calculated; The embankment design parameters of the optimal combination are determined based on the embankment settlement prediction results and embankment design costs corresponding to the combinations.

2. The method according to claim 1, characterized in that The obtaining of multiple embankment design parameter combinations includes: Obtain embankment design parameters from pre-built embankment design tables based on Python preset instructions; The embankment design parameters are divided into variable parameters and immutable parameters, wherein the variable parameters refer to parameters that can be adjusted during the design process, including at least pile parameters, pile cap parameters and reinforcement material parameters; and the immutable parameters include at least soil parameters, embankment parameters and economic parameters; According to the value range and step size of the variable parameters, the variable parameters and the immutable parameters are combined to generate a plurality of embankment design parameter combinations.

3. The method according to claim 2, characterized in that The method further comprises: Automatically converting the soil parameters into soft soil creep parameters by using a soft soil creep conversion formula built into the embankment design table; Comparing the calculated creep parameter of the soft soil with the value range of the local soft soil; If it is within the value range of the local soft soil, the calculated soft soil creep parameter is determined as the final soft soil creep parameter; If it exceeds the value range of the local soft soil, the average value of the value range of the local soft soil is determined as the final soft soil creep parameter.

4. The method according to claim 1, characterized in that: After creating the corresponding Python modeling script based on the technical parameters of each combination, the method further includes: Generate a modeling task based on each combined Python modeling script; Each of the modeling tasks is packaged and sent to a pre-built parallel task processing system, so that a task scheduling node in the parallel task processing system can allocate each of the modeling tasks to a plurality of task execution nodes.

5. The method according to claim 4, characterized in that The method of calling the python modeling script of each combination through the API interface of the finite element software to automatically construct the 3D finite element model of the embankment corresponding to each combination includes: The Python modeling script corresponding to the received modeling task is called through the API interface of the finite element software of each task execution node; Generate multiple virtual modeling instances according to the python modeling script; Encapsulating each of the virtual modeling instances in an independent container; Based on the Python modeling script corresponding to each virtual modeling instance, the three-dimensional finite element model of the embankment corresponding to each combination is automatically constructed.

6. The method according to claim 5, characterized in that The virtual modeling instances at least include boundary instances, soil layer instances, pile instances, embankment instances and reinforcement material instances; the automatic construction of the embankment three-dimensional finite element model corresponding to each combination based on the Python modeling script corresponding to each virtual modeling instance includes: Based on the boundary conditions in the boundary instance, the boundary of the 3D finite element model of the embankment is automatically created by a corresponding python modeling script; Based on the soft soil creep parameters in the soil layer example, a four-layer soft soil foundation of a three-dimensional finite element model of an embankment is automatically created through a corresponding python modeling script; Based on the pile parameters, pile cap parameters and elastoplastic model in the pile example, a pile body of a three-dimensional finite element model of an embankment is automatically created through a corresponding python modeling script; Based on the embankment parameters and the Mohr-Coulomb model in the embankment example, a 3D finite element model of the embankment is automatically created through a corresponding python modeling script, wherein the embankment includes a pavement structure, an embankment filler, and a sand cushion layer; Based on the reinforcement material parameters and the elastoplastic model in the reinforcement material example, the positive and negative interfaces of the reinforcement material of the 3D finite element model of the embankment are automatically created through the corresponding python modeling script.

7. The method according to claim 1, characterized in that The applying a simulated load to the three-dimensional finite element model of the embankment corresponding to each combination to obtain the embankment settlement prediction result corresponding to each combination includes: The mesh method is used to mesh the three-dimensional finite element model of the embankment corresponding to each combination; The corresponding simulation loads are applied to the meshed three-dimensional finite element model of the embankment according to the construction stage, and the embankment settlement prediction results corresponding to each combination are obtained.

8. The method according to claim 1, characterized in that After applying simulated loads to the three-dimensional finite element models of the embankments corresponding to the combinations to obtain settlement prediction results corresponding to the combinations, the method further includes: Extracting the settlement prediction results into Python through the API interface of the finite element software; The settlement prediction results are converted into a standardized data format through a python visual script; Preprocessing the settlement prediction results after the standardization process, wherein the preprocessing at least includes data screening, unit conversion, and average value calculation for a specific area; The preprocessed settlement prediction results are used to generate charts or animations through the Python visualization library.

9. The method according to claim 1, characterized in that: The embankment design parameters of the optimal combination determined based on the embankment settlement prediction results and embankment design costs corresponding to the combinations include: Calculate the expected value of embankment settlement prediction results and embankment design cost for each combination respectively; The total expected value of each combination is calculated based on the expected value of embankment settlement prediction results and embankment design cost; The embankment design parameters corresponding to the combination whose total expected value is closest to 1 are determined as the embankment design parameters of the optimal combination.

10. A system for determining design parameters of pile-supported reinforced embankments, characterized in that: include: An acquisition unit, used for acquiring a plurality of embankment design parameter combinations; each of the embankment design parameter combinations comprises a technical parameter combination and an economic parameter combination; A creation unit is used to create corresponding Python modeling scripts based on various technical parameter combinations; A calling unit, used for calling the python modeling script of each combination through the API interface of the finite element software so as to automatically construct the three-dimensional finite element model of the embankment corresponding to each combination; A load simulation unit, used for applying a simulated load to the three-dimensional finite element model of the embankment corresponding to each combination, to obtain a predicted result of the embankment settlement corresponding to each combination; A calculation unit, used for respectively calculating the embankment design cost corresponding to each embankment three-dimensional finite element model based on each economic parameter combination; The determination unit is used to determine the embankment design parameters of the optimal combination based on the embankment settlement prediction results and embankment design costs corresponding to the combinations.

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