Anti-floating anchor rod reinforcing construction method based on finite element analysis
Through finite element analysis and simulation construction technology, the problems of low construction safety and efficiency in floating anchor construction are solved, accurate model construction and real-time monitoring are realized, construction quality and safety are improved, and costs and deviations are reduced.
Patent Information
- Application Number
- CN202411315684.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-20
- Publication Date
- 2024-12-31
AI Technical Summary
The lack of effective comparison between the finite element model and actual construction in the existing anti-floating anchor construction technology leads to low construction safety and efficiency, and the failure to effectively use construction parameter data for accurate model construction, resulting in reduced construction accuracy and efficiency.
Using the anti-floating anchor reinforcement construction method based on finite element analysis, through data preprocessing and model construction, an accurate finite element learning model is generated, the construction process is simulated, and the construction process is monitored in real time, and parameter comparison is performed to evaluate the construction decision data. Pass rate, optimize resource allocation and construction steps.
It improves construction quality and safety, reduces construction costs, and improves construction efficiency and accuracy through simulation and prediction of potential problems and resource allocation, ensuring the accuracy of structural deformation and stress distribution.
Smart Images
Figure CN119227194A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of anti-floating anchor rod construction, in particular to an anti-floating anchor rod reinforcement construction method based on finite element analysis. Background Art
[0002] Anti-floating anchor construction is a reinforcement measure widely used in underground projects, basements, reservoirs and other occasions.
[0003] The Chinese patent with publication number CN105089081A discloses a method for anti-floating anchor construction, which mainly carries out anti-floating anchor construction in the following order: 1. site leveling; 2. equipment assembly and commissioning; 3. measurement and laying out and drilling rig positioning; 4. drilling holes; 5. high-pressure rotary jet hole expansion; 6. fabrication of assembled multi-corrosion-resistant expanded anchor and anchor placement; 7. cement slurry injection into the bag; 8. grouting into the anchor hole; 9. foundation bottom plate construction; 10. anti-floating anchor locking. Although the above patent solves the problem of anti-floating anchor construction, the following problems still exist in actual operation: 1. There is no effective comparison between the simulation animation of the finite element model and the actual construction, which leads to the inability to understand the application of the finite element model in a timely manner; 2. Failure to formulate specific construction steps based on the best construction plan, and failure to conduct simulated construction according to the construction steps, which reduced the safety of actual construction; 3. A more intuitive and accurate model was not constructed based on the specific construction parameter data of the anti-floating anchor construction, which resulted in reduced efficiency and accuracy in the anti-floating anchor construction. Summary of the invention
[0004] The purpose of the present invention is to provide an anti-floating anchor reinforcement construction method based on finite element analysis. Through accurate model construction and simulation analysis, the material consumption and cost situation in the construction process can be predicted, thereby optimizing resource allocation and reducing construction costs. The stiffness matrix of each grid is confirmed according to the displacement function and elastic mechanics, and assembled into an overall stiffness matrix. This process ensures the accuracy of the model in describing structural deformation and stress distribution. There are also strict requirements on the positioning accuracy of anchor holes in the measurement and layout stage, which helps to reduce construction deviations and improve construction quality. Through simulated construction, problems and difficulties that may arise in actual construction can be predicted, so that resource allocation and optimization can be carried out in advance, which can solve problems in the prior art.
[0005] To achieve the above object, the present invention provides the following technical solutions: The anti-floating anchor reinforcement construction method based on finite element analysis includes the following steps: S1: Construction data confirmation: retrieve the construction parameter data of the anti-floating anchor from the database, perform data preprocessing on the construction parameter data, perform unique coding and labeling on the data preprocessing, and use the construction parameter data with unique coding and labeling as the target construction data; S2: Data finite element model construction: retrieve the data parameters in the target construction data, import the retrieved data parameters into the finite element learning model for model construction, and obtain the target learning model after the construction is completed; S3: Confirmation of reinforcement construction plan: retrieve the target learning model, confirm the characteristic parameters in the target learning model, formulate a construction process plan based on the characteristic parameters in the target learning model, simulate the formulated construction process plan, and use the simulation construction results as construction decision data; S4: Construction plan monitoring and evaluation: Carry out actual construction operations in the construction area based on the construction decision data, and monitor the actual construction operation process in real time. Compare data parameters between the construction data of the actual construction operation and the construction decision data, and determine the qualification rate of the construction decision data based on the parameter comparison results.
[0006] Preferably, the construction parameter data is preprocessed in S1, and uniquely coded after the data preprocessing, including: First, the construction parameter data is obtained, wherein the construction parameter data includes groundwater level data, soil layer unit weight data, anchor working length data, cement and steel bar quality data, slurry test block test data and anchor pullout resistance test data; The acquired construction parameter data is subjected to data preprocessing, wherein the data preprocessing includes sequentially performing data cleaning, data integration, data transformation, data reduction and data standardization on the construction parameter data; The construction parameter data after data preprocessing is uniquely coded and labeled, wherein the unique coding label is a unique ID assigned by the self-incrementing ID of the database and then a timestamp is added; The target construction data is obtained after the unique coding number.
[0007] Preferably, in S2, the retrieved data parameters are imported into the finite element learning model to construct the model, including: Retrieving the finite element learning model from the model database; Among them, the finite element learning model includes anchor data, construction soil data and construction structure data; At the same time, the interaction rules between the anchor and the soil are determined based on the anchor data and the construction soil data, and the interaction rules are obtained from the database; Importing each data parameter in the target construction data into the finite element learning model, and making the data parameter of the target construction data correspond to the corresponding part in the finite element learning model; The corresponding parts are data parts with the same attributes; After the data parameters of the target construction data are matched with the corresponding parts of the finite element learning model, the load and boundary conditions of the corresponding finite element learning model are adjusted; Among them, the loads and boundary conditions include the deadweight of the soil, the buoyancy data caused by the change of groundwater level, and the external load data.
[0008] Preferably, the method of importing the retrieved data parameters into the finite element learning model for model building in S2 further includes: Meshing the finite element learning model after adjusting the load and boundary conditions; Among them, the finite element learning model is meshed using the transformation and extension method; The finite element learning model is meshed according to the conversion and extension method, and the generated mesh data is set according to the construction plan; Performing quality inspection on the generated mesh, wherein the quality inspection includes the shape, uniformity and distortion degree of the mesh; When the mesh quality is not within the standard range, the mesh is automatically adjusted; when the mesh quality is within the standard range, a finite element learning model with mesh division completed is obtained; Solve the parameters of the finite element learning model after meshing; Before solving the parameters, the type of parameters to be solved must be confirmed. The parameter types to be solved include construction material properties, load size, and boundary conditions. After the parameter type to be solved is confirmed, the gradient descent method is used to iteratively solve the parameter until the parameter to be solved meets the preset maximum number of iterations; After the parameters of the finite element learning model are solved, the displacement function is confirmed for the grids divided in the finite element learning model, where each grid corresponds to the displacement function; The stiffness matrix of each grid is confirmed according to the displacement function and elastic mechanics, and the stiffness matrix of each grid is assembled according to the positional relationship in the finite element learning model to form an overall stiffness matrix; The load and boundary conditions of the finite element learning model are imported into the overall stiffness matrix, and finally a complete finite element learning model constructed with target construction data is obtained; And mark the completed finite element learning model as the target learning model.
[0009] Preferably, the generated mesh is subjected to quality inspection, including: Obtain the aspect ratio of each grid unit according to the length of the long side and the wide side of each grid unit; A first grid quality evaluation coefficient is obtained according to a change in the aspect ratio of each grid unit; wherein the first grid quality evaluation coefficient is obtained by the following formula:
[0010] Among them, X 01 represents the first grid quality evaluation coefficient; n represents the number of grids; B i represents the aspect ratio corresponding to the i-th grid; B0 represents the preset aspect ratio threshold; B i-1 Indicates the aspect ratio of the i-1th grid; B c Indicates the preset aspect ratio difference threshold between two adjacent grids; Obtaining a second grid quality evaluation coefficient using the area corresponding to each grid unit; The first grid quality evaluation coefficient and the second grid quality evaluation coefficient are used to obtain a grid quality evaluation coefficient, wherein the grid quality evaluation coefficient is obtained by the following formula:
[0011] Where X represents the grid quality evaluation coefficient; X 01 represents the first grid quality evaluation coefficient; X 02 represents the second grid quality evaluation coefficient; Comparing the grid quality evaluation coefficient with a preset quality evaluation coefficient threshold; When the mesh quality evaluation coefficient exceeds a preset quality evaluation coefficient threshold, the mesh quality is determined to be abnormal, and a mesh quality abnormality alarm is issued; When the mesh quality evaluation coefficient does not exceed the preset quality evaluation coefficient threshold, the curvature of each side length of the mesh is calculated to obtain the curvature and value of the four side lengths corresponding to each mesh; When the curvature and value corresponding to each grid are non-zero, it is determined that the current grid is distorted; When the number of distorted meshes exceeds a preset mesh distortion threshold, the mesh quality is judged to be abnormal and a mesh quality abnormality alarm is issued.
[0012] Preferably, obtaining the second grid quality evaluation coefficient using the area corresponding to each grid unit includes: Extract the area corresponding to each grid cell; Obtaining a second grid quality evaluation coefficient using the area corresponding to each grid unit; Dividing the grid units into a plurality of grid groups according to the principle that every two adjacent grid units form a group; Obtaining the area ratio corresponding to each two adjacent grid cells according to the area of each two adjacent grid cells; The second grid quality evaluation coefficient is obtained by using the area ratio corresponding to each two adjacent grid units; wherein the second grid quality evaluation coefficient is obtained by the following formula:
[0013] Among them, X 02 represents the second grid quality evaluation coefficient; n represents the number of grids; B i Indicates the aspect ratio of the i-th grid; B i-1 represents the aspect ratio of the i-1th grid; S i represents the area corresponding to the i-th grid; S i-1 Represents the area corresponding to the i-1th grid.
[0014] Preferably, a construction process plan is formulated according to the characteristic parameters in the target learning model in S3, and the formulated construction process plan is simulated for construction, including: First, the characteristic parameters in the target learning model are confirmed, wherein the characteristic parameters include anchor parameters, rock and soil material parameters, anchor and rock and soil interface material parameters, and geometric size parameters; Formulate construction process plan based on characteristic parameters in the target learning model; The construction process plan includes the following steps: Step 1: Pad construction: The base of the underground garage is manually picked up and the pad construction is carried out after the trench inspection is passed; Step 2: Measurement and setting out: The anchor rods are positioned by setting out and the anchor rod holes are marked. The positioning deviation of the anchor rod holes is no more than 20mm, and the hole distance in the horizontal and vertical directions is no more than 100mm. Step 3: Drilling rig in place: Check the position of the anchor hole. If there is any deviation, reposition it. If there is no deviation, install the anchor hole drilling rig and level, adjust and stabilize it. Step 4: Drilling operation: The depth, wind pressure and speed of the anchor hole drilling rig are set according to the characteristic parameters in the target learning model. At the same time, the deflection of the anchor hole is not greater than 2%; the drilling depth exceeds 0.5m of the predetermined anchor rod design length; In addition, when the anchor hole drilling rig is drilling, the rock debris in the hole should be discharged in time. If the slag discharge is not smooth, the feeding should be stopped and the slag discharge channel should be cleared by the back and forth movement method. Step 5: Install the anchor rod: Use the locator to fix the position of the anchor rod, place the anchor rod at the center of the drill hole, and insert it to the preset depth; Step 6: Grouting: Use M30 cement mortar for grouting, and the cement grade must not be lower than P42.5; use a grouting pipe to grout from the bottom of the hole upward, control the grouting pressure to be no less than 0.5MPa, and gradually pull the grouting pipe upward and out of the hole as the mortar is poured in.
[0015] Preferably, in S3, a construction process plan is formulated according to the characteristic parameters in the target learning model, and the formulated construction process plan is simulated for construction, and further includes: Carry out simulated construction according to the construction process plan; Automatically sort the data parameters and construction processes in the construction process plan; Use 3D modeling and AR animation to create virtual construction animations of sorted data parameters and construction processes; Determine the completeness of the completed virtual construction animation. If the completeness is not within a preset range, the completed virtual construction animation is abnormal construction data, and the construction process plan is re-formulated; If the degree of completeness is within the preset range, the completed virtual construction animation is qualified construction data; And mark the qualified construction data as construction decision data.
[0016] Preferably, the actual construction operation process is monitored in real time in S4, data parameters are compared between the construction data of the actual construction operation and the construction decision data, and the qualified rate of the construction decision data is determined according to the parameter comparison result, including: Apply the data parameters in the construction decision data to the actual construction operation, and monitor the steps of the actual construction operation according to the steps in the construction decision data; If the difference between the data parameters in the actual construction operation step and the data parameters in the construction decision data step is not within the preset range, the construction is terminated; If the difference between the data parameters in the actual construction operation step and the data parameters in the construction decision data step is within a preset range, the data parameters in the actual construction operation and the construction decision data are compared.
[0017] Preferably, in S4, the actual construction operation process is monitored in real time, data parameters are compared between the construction data of the actual construction operation and the construction decision data, and the qualified rate of the construction decision data is determined according to the parameter comparison result, which further includes: Determine the pass rate of construction decision data based on parameter comparison results; And according to the judgment results, the construction decision data is divided into primary construction, secondary construction and tertiary construction; Among them, the higher the qualification rate of construction decision data, the higher the safety rate of actual construction operation.
[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The anti-floating anchor reinforcement construction method based on finite element analysis provided by the present invention can predict the material consumption and cost during the construction process through accurate model construction and simulation analysis, thereby optimizing resource allocation and reducing construction costs, confirming the stiffness matrix of each grid according to the displacement function and elastic mechanics, and assembling it into an overall stiffness matrix. This process ensures the accuracy of the model in describing structural deformation and stress distribution.
[0019] 2. The anti-floating anchor reinforcement construction method based on finite element analysis provided by the present invention also has strict requirements on the positioning accuracy of anchor holes in the measurement and layout stage, which helps to reduce construction deviations and improve construction quality. Through simulated construction, problems and difficulties that may arise in actual construction can be predicted, so that resource allocation and optimization can be carried out in advance. The production of virtual construction animations can help construction personnel better understand the construction process and details, and discover potential safety hazards and risk points in advance.
[0020] 3. The anti-floating anchor reinforcement construction method based on finite element analysis provided by the present invention can evaluate the qualification rate of construction decision data by comparing the actual construction operation with the construction decision data, and divide the construction decision data into primary, secondary and tertiary construction according to the parameter comparison results, which is helpful for differentiated management and risk control of construction at different levels. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 It is a schematic diagram of the anti-floating anchor reinforcement construction module of the present invention; Figure 2 It is a schematic diagram of the process of the anti-floating anchor reinforcement construction method of the present invention; Figure 3 It is a schematic diagram of the construction process steps of the present invention. DETAILED DESCRIPTION
[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0023] In order to solve the problem that the existing technology does not build a more intuitive and accurate model based on the specific construction parameter data of the anti-floating anchor construction, which leads to the reduction of efficiency and accuracy in the construction of the anti-floating anchor, please refer to Figure 1-Figure 3 , this embodiment provides the following technical solutions: The anti-floating anchor reinforcement construction method based on finite element analysis includes the following steps: S1: Construction data confirmation: retrieve the construction parameter data of the anti-floating anchor from the database, perform data preprocessing on the construction parameter data, perform unique coding and labeling on the data preprocessing, and use the construction parameter data with unique coding and labeling as the target construction data; Among them, the unique ID is allocated through the database's self-increment ID, which can ensure the uniqueness and continuity of the ID; S2: Data finite element model construction: retrieve the data parameters in the target construction data, import the retrieved data parameters into the finite element learning model for model construction, and obtain the target learning model after the construction is completed; Among them, by introducing multiple information such as anchor data, construction soil data and construction structure data, the finite element learning model can more comprehensively reflect the actual construction situation; S3: Confirmation of reinforcement construction plan: retrieve the target learning model, confirm the characteristic parameters in the target learning model, formulate a construction process plan based on the characteristic parameters in the target learning model, simulate the formulated construction process plan, and use the simulation construction results as construction decision data; Among them, through simulated construction, problems and difficulties that may arise in actual construction can be predicted, so that resource allocation and optimization can be carried out in advance; S4: Construction plan monitoring and evaluation: Carry out actual construction operations in the construction area according to the construction decision data, monitor the actual construction operation process in real time, compare data parameters based on the construction data of the actual construction operation with the construction decision data, and judge the qualification rate of the construction decision data based on the parameter comparison results; Among them, by comparing the actual construction operations with the construction decision data, the qualification rate of the construction decision data can be evaluated.
[0024] The construction parameter data in S1 is preprocessed and uniquely coded, including: First, the construction parameter data is obtained, wherein the construction parameter data includes groundwater level data, soil layer unit weight data, anchor working length data, cement and steel bar quality data, slurry test block test data and anchor pullout resistance test data; The acquired construction parameter data is subjected to data preprocessing, wherein the data preprocessing includes sequentially performing data cleaning, data integration, data transformation, data reduction and data standardization on the construction parameter data; The construction parameter data after data preprocessing is uniquely coded and labeled, wherein the unique coding label is a unique ID assigned by the self-incrementing ID of the database and then a timestamp is added; The target construction data is obtained after the unique coding number.
[0025] Specifically, through data cleaning, noise, duplication, errors or irrelevant information in construction parameter data can be effectively removed, thereby improving the accuracy and reliability of data. Data integration can integrate data from different sources to form a unified data view, which is convenient for subsequent analysis and processing. Data transformation can convert data into a format or structure that is more suitable for analysis, such as converting continuous variables into discrete variables, or normalizing indicators of different dimensions. Data reduction can reduce the size of the data set while maintaining the important characteristics of the data, thereby improving the efficiency of data processing and analysis. Through data standardization, data of different indicators can be converted to the same dimension, so that different indicators can be directly compared and calculated. The construction parameter data is marked by a unique coding number to ensure that each data item has a unique identifier, which is convenient for data tracking, query and management. The unique ID is assigned through the database's self-incrementing ID to ensure the uniqueness and continuity of the ID; and the timestamp tag can record the time when the data is generated or processed, which is helpful for data version control and time series analysis.
[0026] Import the retrieved data parameters in S2 into the finite element learning model for model construction, including: Retrieving the finite element learning model from the model database; Among them, the finite element learning model includes anchor data, construction soil data and construction structure data; At the same time, the interaction rules between the anchor and the soil are determined based on the anchor data and the construction soil data, and the interaction rules are obtained from the database; Importing each data parameter in the target construction data into the finite element learning model, and making the data parameter of the target construction data correspond to the corresponding part in the finite element learning model; The corresponding parts are data parts with the same attributes; After the data parameters of the target construction data are matched with the corresponding parts of the finite element learning model, the load and boundary conditions of the corresponding finite element learning model are adjusted; Among them, the loads and boundary conditions include the deadweight of the soil, the buoyancy data caused by the change of groundwater level, and the external load data.
[0027] Meshing the finite element learning model after adjusting the load and boundary conditions; Among them, the finite element learning model is meshed using the transformation and extension method; The finite element learning model is meshed according to the conversion and extension method, and the generated mesh data is set according to the construction plan; Performing quality inspection on the generated mesh, wherein the quality inspection includes the shape, uniformity and distortion degree of the mesh; When the mesh quality is not within the standard range, the mesh is automatically adjusted; when the mesh quality is within the standard range, a finite element learning model with mesh division completed is obtained; Solve the parameters of the finite element learning model after meshing; Before solving the parameters, the type of parameters to be solved must be confirmed. The parameter types to be solved include construction material properties, load size, and boundary conditions. After the parameter type to be solved is confirmed, the gradient descent method is used to iteratively solve the parameter until the parameter to be solved meets the preset maximum number of iterations; After the parameters of the finite element learning model are solved, the displacement function is confirmed for the grids divided in the finite element learning model, where each grid corresponds to the displacement function; The stiffness matrix of each grid is confirmed according to the displacement function and elastic mechanics, and the stiffness matrix of each grid is assembled according to the positional relationship in the finite element learning model to form an overall stiffness matrix; The load and boundary conditions of the finite element learning model are imported into the overall stiffness matrix, and finally a complete finite element learning model constructed with target construction data is obtained; And mark the completed finite element learning model as the target learning model.
[0028] Specifically, by importing each data parameter in the target construction data into the finite element learning model and corresponding to the corresponding part in the model, it can be ensured that the model accurately reflects the actual construction situation. The interaction rules between the anchor and the soil are obtained from the database according to the anchor data and the construction soil data, and are taken into account in the model construction, which can more comprehensively simulate the complex interactions in the construction process. This helps to more accurately analyze the stress conditions of the anchor and the deformation characteristics of the soil, and adjust the load and boundary conditions of the finite element learning model according to the target construction data, including the deadweight of the soil, the buoyancy data caused by the change of the groundwater level, and the external load data. This flexibility enables the model to be closer to the actual construction environment and improve the accuracy and reliability of the simulation results. By corresponding the data parameters of the target construction data with the corresponding parts in the finite element learning model, it can be ensured that the model more accurately reflects the actual construction situation. This includes the interaction rules between anchors and soil, the deadweight of soil, buoyancy data caused by groundwater level changes, and external load data, all of which help improve the accuracy and reliability of model construction. Through accurate model construction and simulation analysis, the material consumption and cost situation in the construction process can be predicted, thereby optimizing resource allocation and reducing construction costs. In addition, the model can also be used to evaluate the economic feasibility of different construction plans. At the same time, by introducing multiple information such as anchor data, construction soil data, and construction structure data, the finite element learning model can more comprehensively reflect the actual construction situation. This helps to improve the accuracy of the model and make the simulation results closer to the actual situation. The interaction rules between anchors and soil are automatically determined based on anchor data and construction soil data, and the load and boundary conditions of the finite element learning model are automatically adjusted. The automation of this process reduces human intervention and improves work efficiency, while also reducing the possibility of human errors. The transformation and extension method is used for meshing, and the generated mesh data can be set according to the construction plan, making the meshing more flexible and in line with actual needs. At the same time, the quality of the mesh is checked and automatically adjusted to ensure that the mesh quality is within the standard range, thereby improving the accuracy and reliability of the finite element analysis. By confirming the type of solution parameters and using the gradient descent method for iterative solution, the solution parameters that meet the preset conditions can be efficiently found. Each mesh corresponds to a displacement function, and the stiffness matrix of each mesh is confirmed based on the displacement function and elastic mechanics, and assembled into an overall stiffness matrix. This process ensures the accuracy of the model in describing structural deformation and stress distribution.
[0029] Specifically, the generated mesh is subjected to quality inspection, including: Obtain the aspect ratio of each grid unit according to the length of the long side and the wide side of each grid unit; A first grid quality evaluation coefficient is obtained according to a change in the aspect ratio of each grid unit; wherein the first grid quality evaluation coefficient is obtained by the following formula:
[0030] Among them, X 01 represents the first grid quality evaluation coefficient; n represents the number of grids; B i represents the aspect ratio corresponding to the i-th grid; B0 represents the preset aspect ratio threshold; B i-1 Indicates the aspect ratio of the i-1th grid; B c Indicates the preset aspect ratio difference threshold between two adjacent grids; Obtaining a second grid quality evaluation coefficient using the area corresponding to each grid unit; The first grid quality evaluation coefficient and the second grid quality evaluation coefficient are used to obtain a grid quality evaluation coefficient, wherein the grid quality evaluation coefficient is obtained by the following formula:
[0031] Where X represents the grid quality evaluation coefficient; X 01 represents the first grid quality evaluation coefficient; X 02 represents the second grid quality evaluation coefficient; Comparing the grid quality evaluation coefficient with a preset quality evaluation coefficient threshold; When the mesh quality evaluation coefficient exceeds a preset quality evaluation coefficient threshold, the mesh quality is determined to be abnormal, and a mesh quality abnormality alarm is issued; When the mesh quality evaluation coefficient does not exceed the preset quality evaluation coefficient threshold, the curvature of each side length of the mesh is calculated to obtain the curvature and value of the four side lengths corresponding to each mesh; When the curvature and value corresponding to each grid are non-zero, it is determined that the current grid is distorted; When the number of distorted meshes exceeds a preset mesh distortion threshold, the mesh quality is judged to be abnormal and a mesh quality abnormality alarm is issued.
[0032] The technical effect of the above technical solution is that the solution comprehensively evaluates the mesh quality by calculating the aspect ratio of the mesh, the change in the aspect ratio of adjacent meshes, the mesh area and other dimensions. This multi-dimensional evaluation method can more comprehensively and accurately reflect the geometric characteristics and quality status of the mesh, avoiding the one-sidedness and errors that may be caused by a single-dimensional evaluation.
[0033] The solution realizes the automatic detection of mesh quality and abnormal alarm function. Through the preset threshold, the system can automatically determine whether the mesh quality meets the requirements and issue an alarm in time when an abnormality is detected, thus improving the efficiency and accuracy of mesh processing.
[0034] By calculating the aspect ratio of each grid cell, the change in aspect ratios of adjacent grids, and grid area, the solution can achieve fine-grained control of grid quality. This helps to promptly discover and correct potential quality problems during grid generation or adjustment, ensuring that the grid quality meets the requirements of subsequent applications.
[0035] In addition to basic mesh quality assessment, the solution also adds mesh distortion detection. By calculating the curvature and value of the mesh edge length, it is possible to identify distorted meshes and further determine whether the number of distorted meshes exceeds a preset threshold. This helps to promptly detect and handle mesh distortion problems and ensure the correctness of the mesh's geometry and topology.
[0036] In the fields of numerical simulation, image processing, geographic information system (GIS), etc., grid quality directly affects the accuracy and efficiency of data processing. This solution helps to improve the accuracy and reliability of data processing through strict grid quality assessment and control, and provides strong support for research and application in related fields.
[0037] In summary, this technical solution achieves refined control of grid quality and abnormal alarm function through a multi-dimensional and automated grid quality assessment and detection mechanism, which helps to improve the accuracy and efficiency of data processing and provides strong support for research and application in related fields.
[0038] Specifically, the second grid quality evaluation coefficient is obtained by using the area corresponding to each grid unit, including: Extract the area corresponding to each grid cell; Obtaining a second grid quality evaluation coefficient using the area corresponding to each grid unit; Dividing the grid units into a plurality of grid groups according to the principle that every two adjacent grid units form a group; Obtaining the area ratio corresponding to each two adjacent grid cells according to the area of each two adjacent grid cells; The second grid quality evaluation coefficient is obtained by using the area ratio corresponding to each two adjacent grid units; wherein the second grid quality evaluation coefficient is obtained by the following formula:
[0039] Among them, X 02 represents the second grid quality evaluation coefficient; n represents the number of grids; B i Indicates the aspect ratio of the i-th grid; B i-1 represents the aspect ratio of the i-1th grid; S i represents the area corresponding to the i-th grid; S i-1 Represents the area corresponding to the i-1th grid.
[0040] The technical effect of the above technical solution is that by calculating the area ratio of every two adjacent grid cells, the solution can evaluate the uniformity of grid area distribution. Ideally, the area of the grid should be evenly distributed, which is helpful for subsequent data processing and analysis. Area uniformity assessment is an important aspect of grid quality assessment, which directly affects the accuracy and reliability of data processing.
[0041] Meshes with large area deviations may introduce data deviations and affect the results of the overall data analysis. By calculating the area ratio and obtaining the second mesh quality evaluation coefficient based on it, the scheme can identify meshes with large area deviations and process or optimize them accordingly to reduce the impact of data deviations on the overall results.
[0042] Enhance the comprehensiveness of mesh quality assessment: In mesh quality assessment, in addition to the shape of the mesh (such as aspect ratio), the area distribution of the mesh also needs to be considered. This solution enhances the comprehensiveness and accuracy of mesh quality assessment by introducing area ratio as one of the evaluation indicators.
[0043] According to the second grid quality evaluation coefficient, the grid division can be optimized. For example, if it is found that the grid area deviation in some areas is large, you can consider adjusting the grid division strategy to make the grid area distribution more uniform. This helps to improve the efficiency and accuracy of subsequent data processing and analysis.
[0044] In order to solve the problem in the prior art that no specific construction steps are formulated according to the best construction plan, and no simulation construction is performed according to the construction steps, which leads to reduced safety of actual construction, please refer to Figure 1-Figure 3 , this embodiment provides the following technical solutions: According to the characteristic parameters in the target learning model in S3, a construction process plan is formulated, and the formulated construction process plan is simulated for construction, including: First, the characteristic parameters in the target learning model are confirmed, wherein the characteristic parameters include anchor parameters, rock and soil material parameters, anchor and rock and soil interface material parameters, and geometric size parameters; Formulate construction process plan based on characteristic parameters in the target learning model; The construction process plan includes the following steps: Step 1: Pad construction: The base of the underground garage is manually picked up and the pad construction is carried out after the trench inspection is passed; Step 2: Measurement and setting out: The anchor rods are positioned by setting out and the anchor rod holes are marked. The positioning deviation of the anchor rod holes is no more than 20mm, and the hole distance in the horizontal and vertical directions is no more than 100mm. Step 3: Drilling rig in place: Check the position of the anchor hole. If there is any deviation, reposition it. If there is no deviation, install the anchor hole drilling rig and level, adjust and stabilize it. Step 4: Drilling operation: The depth, wind pressure and speed of the anchor hole drilling rig are set according to the characteristic parameters in the target learning model. At the same time, the deflection of the anchor hole is not greater than 2%; the drilling depth exceeds 0.5m of the predetermined anchor rod design length; In addition, when the anchor hole drilling rig is drilling, the rock debris in the hole should be discharged in time. If the slag discharge is not smooth, the feeding should be stopped and the slag discharge channel should be cleared by the back and forth movement method. Step 5: Install the anchor rod: Use the locator to fix the position of the anchor rod, place the anchor rod at the center of the drill hole, and insert it to the preset depth; Step 6: Grouting: Use M30 cement mortar for grouting, and the cement grade must not be lower than P42.5; use a grouting pipe to grout from the bottom of the hole upward, control the grouting pressure to be no less than 0.5MPa, and gradually pull the grouting pipe upward and out of the hole as the mortar is poured in.
[0045] Carry out simulated construction according to the construction process plan; Automatically sort the data parameters and construction processes in the construction process plan; Use 3D modeling and AR animation to create virtual construction animations of sorted data parameters and construction processes; Determine the completeness of the completed virtual construction animation. If the completeness is not within a preset range, the completed virtual construction animation is abnormal construction data, and the construction process plan is re-formulated; If the degree of completeness is within the preset range, the completed virtual construction animation is qualified construction data; And mark the qualified construction data as construction decision data.
[0046] Specifically, by confirming the characteristic parameters in the target learning model, the construction process plan can be formulated more accurately. During the drilling operation, the depth, wind pressure, and speed of the anchor hole drill are set according to the characteristic parameters to ensure the accuracy and efficiency of the drilling. At the same time, there are strict requirements for the positioning accuracy of the anchor holes in the measurement and layout stage, which helps to reduce construction deviations and improve construction quality. Through simulated construction, problems and difficulties that may arise in actual construction can be predicted, so that resources can be allocated and optimized in advance. The production of virtual construction animations can help construction personnel better understand the construction process and details, and discover potential safety hazards and risk points in advance. Virtual construction animations, as an intuitive and vivid display method, are conducive to communication and collaboration between different departments and teams. By jointly viewing and analyzing virtual construction animations, all parties can better understand the construction process and requirements, reduce communication barriers, and improve collaboration efficiency.
[0047] In order to solve the problem in the prior art that there is no effective comparison between the simulation animation of the finite element model and the actual construction, which leads to the inability to timely understand the application of the finite element model, please refer to Figure 1-Figure 3 , this embodiment provides the following technical solutions: In S4, the actual construction operation process is monitored in real time, and data parameters are compared between the construction data of the actual construction operation and the construction decision data. The qualified rate of the construction decision data is determined based on the parameter comparison results, including: Apply the data parameters in the construction decision data to the actual construction operation, and monitor the steps of the actual construction operation according to the steps in the construction decision data; If the difference between the data parameters in the actual construction operation step and the data parameters in the construction decision data step is not within the preset range, the construction is terminated; If the difference between the data parameters in the actual construction operation step and the data parameters in the construction decision data step is within a preset range, the data parameters in the actual construction operation and the construction decision data are compared.
[0048] Determine the pass rate of construction decision data based on parameter comparison results; And according to the judgment results, the construction decision data is divided into primary construction, secondary construction and tertiary construction; Among them, the higher the qualification rate of construction decision data, the higher the safety rate of actual construction operation.
[0049] Specifically, by real-time monitoring of the data parameters in the actual construction operation steps and comparing them with the parameters in the construction decision data, deviations or problems in the construction process can be discovered in time. When the difference in data parameters exceeds the preset range, the construction can be terminated in time to avoid further risks and losses. By comparing the actual construction operation with the construction decision data, the qualified rate of the construction decision data can be evaluated. This evaluation helps to identify problems and deficiencies in construction decisions, and then optimize and improve the decision-making process. The construction decision data is divided into primary, secondary and tertiary construction according to the parameter comparison results, which helps to differentiate the management and risk control of construction at different levels. For construction decision data with a higher qualified rate, a more active and efficient construction strategy can be adopted; for construction decision data with a lower qualified rate, it is necessary to strengthen monitoring and intervention to ensure the smooth progress of the construction process. By improving the qualified rate of construction decision data, the safety rate of actual construction operations can be effectively improved.
[0050] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
Claims
1. Anti-floating anchor reinforcement construction method based on finite element analysis, characterized in that: The steps include: S1: Construction data confirmation: retrieve the construction parameter data of the anti-floating anchor from the database, perform data preprocessing on the construction parameter data, perform unique coding and labeling on the data preprocessing, and use the construction parameter data with unique coding and labeling as the target construction data; S2: Data finite element model construction: retrieve the data parameters in the target construction data, import the retrieved data parameters into the finite element learning model for model construction, and obtain the target learning model after the construction is completed; S3: Confirmation of reinforcement construction plan: retrieve the target learning model, confirm the characteristic parameters in the target learning model, formulate a construction process plan based on the characteristic parameters in the target learning model, simulate the formulated construction process plan, and use the simulation construction results as construction decision data; S4: Construction plan monitoring and evaluation: Carry out actual construction operations in the construction area based on the construction decision data, and monitor the actual construction operation process in real time. Compare data parameters between the construction data of the actual construction operation and the construction decision data, and determine the qualification rate of the construction decision data based on the parameter comparison results.
2. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 1 is characterized in that: The construction parameter data in S1 is preprocessed and uniquely coded, including: First, the construction parameter data is obtained, wherein the construction parameter data includes groundwater level data, soil layer unit weight data, anchor working length data, cement and steel bar quality data, slurry test block test data and anchor pullout resistance test data; The acquired construction parameter data is subjected to data preprocessing, wherein the data preprocessing includes sequentially performing data cleaning, data integration, data transformation, data reduction and data standardization on the construction parameter data; The construction parameter data after data preprocessing is uniquely coded and labeled, wherein the unique coding label is a unique ID assigned by the self-incrementing ID of the database and then a timestamp is added; The target construction data is obtained after the unique coding number.
3. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 2 is characterized in that: Import the retrieved data parameters in S2 into the finite element learning model for model construction, including: Retrieving the finite element learning model from the model database; Among them, the finite element learning model includes anchor data, construction soil data and construction structure data; At the same time, the interaction rules between the anchor and the soil are determined based on the anchor data and the construction soil data, and the interaction rules are obtained from the database; Importing each data parameter in the target construction data into the finite element learning model, and making the data parameter of the target construction data correspond to the corresponding part in the finite element learning model; The corresponding parts are data parts with the same attributes; After the data parameters of the target construction data are matched with the corresponding parts of the finite element learning model, the load and boundary conditions of the corresponding finite element learning model are adjusted; Among them, the loads and boundary conditions include the deadweight of the soil, the buoyancy data caused by the change of groundwater level, and the external load data.
4. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 3 is characterized in that: Importing the retrieved data parameters in S2 into the finite element learning model for model construction also includes: Meshing the finite element learning model after adjusting the load and boundary conditions; Among them, the finite element learning model is meshed using the transformation and extension method; The finite element learning model is meshed according to the conversion and extension method, and the generated mesh data is set according to the construction plan; Performing quality inspection on the generated mesh, wherein the quality inspection includes the shape, uniformity and distortion degree of the mesh; When the mesh quality is not within the standard range, the mesh is automatically adjusted; when the mesh quality is within the standard range, a finite element learning model with mesh division completed is obtained; Solve the parameters of the finite element learning model after meshing; Before solving the parameters, the type of parameters to be solved must be confirmed. The parameter types to be solved include construction material properties, load size, and boundary conditions. After the parameter type to be solved is confirmed, the gradient descent method is used to iteratively solve the parameter until the parameter to be solved meets the preset maximum number of iterations; After the parameters of the finite element learning model are solved, the displacement function is confirmed for the grids divided in the finite element learning model, where each grid corresponds to the displacement function; The stiffness matrix of each grid is confirmed according to the displacement function and elastic mechanics, and the stiffness matrix of each grid is assembled according to the positional relationship in the finite element learning model to form an overall stiffness matrix; The load and boundary conditions of the finite element learning model are imported into the overall stiffness matrix, and finally a complete finite element learning model constructed with target construction data is obtained; And mark the completed finite element learning model as the target learning model.
5. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 4 is characterized in that: The generated mesh is subjected to quality inspection, including: Obtain the aspect ratio of each grid unit according to the length of the long side and the wide side of each grid unit; A first grid quality evaluation coefficient is obtained according to a change in the aspect ratio of each grid unit; wherein the first grid quality evaluation coefficient is obtained by the following formula: ； Among them, X 01 represents the first grid quality evaluation coefficient; n represents the number of grids; B i represents the aspect ratio corresponding to the i-th grid; B0 represents the preset aspect ratio threshold; B i-1 Indicates the aspect ratio of the i-1th grid; B c Indicates the preset aspect ratio difference threshold between two adjacent grids; Obtaining a second grid quality evaluation coefficient using the area corresponding to each grid unit; The first grid quality evaluation coefficient and the second grid quality evaluation coefficient are used to obtain a grid quality evaluation coefficient, wherein the grid quality evaluation coefficient is obtained by the following formula: ； Where X represents the grid quality evaluation coefficient; X 01 represents the first grid quality evaluation coefficient; X 02 represents the second grid quality evaluation coefficient; Comparing the grid quality evaluation coefficient with a preset quality evaluation coefficient threshold; When the mesh quality evaluation coefficient exceeds a preset quality evaluation coefficient threshold, the mesh quality is determined to be abnormal, and a mesh quality abnormality alarm is issued; When the mesh quality evaluation coefficient does not exceed the preset quality evaluation coefficient threshold, the curvature of each side length of the mesh is calculated to obtain the curvature and value of the four side lengths corresponding to each mesh; When the curvature and value corresponding to each grid are non-zero, it is determined that the current grid is distorted; When the number of distorted meshes exceeds a preset mesh distortion threshold, the mesh quality is judged to be abnormal and a mesh quality abnormality alarm is issued.
6. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 5 is characterized in that: The second grid quality evaluation coefficient is obtained using the area corresponding to each grid unit, including: Extract the area corresponding to each grid cell; Obtaining a second grid quality evaluation coefficient using the area corresponding to each grid unit; Dividing the grid units into a plurality of grid groups according to the principle that every two adjacent grid units form a group; Obtaining the area ratio corresponding to each two adjacent grid cells according to the area of each two adjacent grid cells; The second grid quality evaluation coefficient is obtained by using the area ratio corresponding to each two adjacent grid units; wherein the second grid quality evaluation coefficient is obtained by the following formula: ； Among them, X 02 represents the second grid quality evaluation coefficient; n represents the number of grids; B i Indicates the aspect ratio of the i-th grid; B i-1 represents the aspect ratio of the i-1th grid; S i represents the area corresponding to the i-th grid; S i-1 Represents the area corresponding to the i-1th grid.
7. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 4 is characterized in that: According to the characteristic parameters in the target learning model in S3, a construction process plan is formulated, and the formulated construction process plan is simulated for construction, including: First, the characteristic parameters in the target learning model are confirmed, wherein the characteristic parameters include anchor parameters, rock and soil material parameters, anchor and rock and soil interface material parameters, and geometric size parameters; Formulate construction process plan based on characteristic parameters in the target learning model; The construction process plan includes the following steps: Step 1: Pad construction: The base of the underground garage is manually picked up and the pad construction is carried out after the trench inspection is passed; Step 2: Measurement and setting out: The anchor rods are positioned by setting out and the anchor rod holes are marked. The positioning deviation of the anchor rod holes is no more than 20mm, and the hole distance in the horizontal and vertical directions is no more than 100mm. Step 3: Drilling rig in place: Check the position of the anchor hole. If there is any deviation, reposition it. If there is no deviation, install the anchor hole drilling rig and level, adjust and stabilize it. Step 4: Drilling operation: The depth, wind pressure and speed of the anchor hole drilling rig are set according to the characteristic parameters in the target learning model. At the same time, the deflection of the anchor hole is not greater than 2%; the drilling depth exceeds 0.5m of the predetermined anchor rod design length; In addition, when the anchor hole drilling rig is drilling, the rock debris in the hole should be discharged in time. If the slag discharge is not smooth, the feeding should be stopped and the slag discharge channel should be cleared by the back and forth movement method. Step 5: Install the anchor rod: Use the locator to fix the position of the anchor rod, place the anchor rod at the center of the drill hole, and insert it to the preset depth; Step 6: Grouting: Use M30 cement mortar for grouting, and the cement grade must not be lower than P42.5; use a grouting pipe to grout from the bottom of the hole upward, control the grouting pressure to be no less than 0.5MPa, and gradually pull the grouting pipe upward and out of the hole as the mortar is poured in.
8. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 7 is characterized in that: In S3, a construction process plan will be formulated according to the characteristic parameters in the target learning model, and the formulated construction process plan will be simulated for construction, including: Carry out simulated construction according to the construction process plan; Automatically sort the data parameters and construction processes in the construction process plan; Use 3D modeling and AR animation to create virtual construction animations of sorted data parameters and construction processes; Determine the completeness of the completed virtual construction animation. If the completeness is not within a preset range, the completed virtual construction animation is abnormal construction data, and the construction process plan is re-formulated; If the degree of completeness is within the preset range, the completed virtual construction animation is qualified construction data; And mark the qualified construction data as construction decision data.
9. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 8 is characterized in that: In S4, the actual construction operation process is monitored in real time, and data parameters are compared between the construction data of the actual construction operation and the construction decision data. The qualified rate of the construction decision data is determined based on the parameter comparison results, including: Apply the data parameters in the construction decision data to the actual construction operation, and monitor the steps of the actual construction operation according to the steps in the construction decision data; If the difference between the data parameters in the actual construction operation step and the data parameters in the construction decision data step is not within the preset range, the construction is terminated; If the difference between the data parameters in the actual construction operation step and the data parameters in the construction decision data step is within a preset range, the data parameters in the actual construction operation and the construction decision data are compared.
10. The anti-floating anchor reinforcement construction method based on finite element analysis according to claim 9 is characterized in that: In S4, the actual construction operation process is monitored in real time, data parameters are compared between the construction data of the actual construction operation and the construction decision data, and the qualified rate of the construction decision data is determined according to the parameter comparison results, which also includes: Determine the pass rate of construction decision data based on parameter comparison results; And according to the judgment results, the construction decision data is divided into primary construction, secondary construction and tertiary construction; Among them, the higher the qualification rate of construction decision data, the higher the safety rate of actual construction operation.