A structural entity detection sampling method

Abstract

The application discloses a structure entity detection sampling method, comprising the following steps: identifying key force transmission components from a structure to be detected based on topology optimization; performing on-site entity detection on the key force transmission components to obtain detection data; establishing a structure numerical model based on the detection data, performing structure analysis on the structure numerical model to obtain performance indexes; and performing structure reliability analysis based on the performance indexes. The application can improve the accuracy and efficiency of structure detection and identification, reduce misjudgment caused by unreasonable sampling, and reduce detection cost.

Description

Technical Field

[0001] This invention belongs to the field of structural entity detection and sampling technology, and particularly relates to a structural entity detection and sampling method. Background Technology

[0002] In the field of building engineering, structural inspection and assessment are crucial steps in evaluating the safety, durability, and reliability of building structures. Currently, the sampling for structural component inspection mainly relies on the quantity requirements specified in the standards, employing random sampling methods. Specifically, inspectors randomly select a certain number of components according to the standards, then conduct performance tests on these sampled components, including material strength and geometric dimensions, and infer the overall structural performance based on the sampling test results. However, this traditional random sampling method has significant drawbacks. Because the strength of building materials such as concrete is inherently random, and the actual performance of each component varies, random sampling cannot ensure that components crucial to the overall mechanical performance of the structure are selected. More importantly, this method completely ignores the overall mechanical characteristics of the structure and the force transmission path. In practical applications, the various components in a structure are not equally important; some components play a key role in load transfer and overall stability, while others are relatively less important. Traditional random sampling methods lack scientific theoretical guidance, making it difficult to systematically identify and inspect these critical force-transmitting components. This leads to test results heavily relying on the random selection during sampling, resulting in analysis conclusions that are prone to significant fluctuations and cannot accurately and stably reflect the actual reliability level of the structure. The uncertainty caused by unreasonable sampling introduces the risk of misjudgment in structural safety assessments. This could lead to unsafe components going undetected or unnecessary reinforcement of safe structures, resulting in wasted resources. Engineering practice has attempted to mitigate this problem by increasing the sampling quantity, but this significantly increases the manpower, material, and time costs of testing, reducing efficiency. Therefore, there has long been an urgent need in this field for a testing method capable of scientifically identifying key components and conducting efficient sampling based on this identification, in order to fundamentally improve the accuracy and efficiency of structural inspection and assessment. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a sampling method for structural entity detection, which can improve the accuracy and efficiency of structural detection and identification, reduce misjudgments caused by unreasonable sampling, and lower detection costs.

[0004] To achieve the above objectives, the present invention provides a structural entity detection sampling method, comprising: Key force-transmitting components are identified from the structure to be tested based on topology optimization; On-site physical testing was conducted on the key force transmission components to obtain test data; A structural numerical model is established based on the detection data, and structural analysis is performed on the structural numerical model to obtain performance indicators. Structural reliability analysis is performed based on the aforementioned performance indicators.

[0005] Optionally, identifying key force-transmitting components from the structure to be detected based on topology optimization includes: A digital model of the structure to be inspected is established. The digital model includes the number of spans, number of stories, node and unit numbers, loads, component cross-sectional properties, initial material properties, and boundary constraints of the structure. Set topology optimization parameters, set volume fraction constraints for the structure according to the number of component inspections required by the specification, and adopt a material interpolation scheme; The topology optimization solution is performed, and the relative density of the material is used as the design variable by using the variable density method. The global stiffness matrix is ​​integrated through iterative loops, the displacement response is solved, the objective function and constraint function are calculated, the gradient is calculated, the design variables are updated and the convergence is judged until the convergence criterion is met. The threshold of the design variables is calculated based on the material volume fraction constraint, and the components with material density greater than the threshold are selected as key force transmission components.

[0006] Optionally, topology optimization parameters can be set including: The volume fraction constraint is set according to the number of tests required by the specification, whereby the volume fraction is the ratio of the volume of the tested component material to the total volume of the component material; The SIMP model is used as the material interpolation scheme, in which the elastic modulus of the material varies with the relative density and drives the intermediate density value to tend to 0 or 1 through a penalty factor.

[0007] Optionally, performing topology optimization solutions includes: S1. The global stiffness matrix of the integrated structure is based on the current design variable values ​​and the penalty factor; S2. Solve the system of equations to obtain the global displacement response of the structure under load; S3. Calculate the values ​​of the objective function and constraint function based on the displacement response and design variables; S4. Use numerical methods to calculate the gradients of the objective function and constraint functions with respect to each design variable; S5. Update design variables using an optimizer based on convex approximation; S6. Repeat S1-S5 until the convergence criterion is met.

[0008] Optionally, the data to be obtained may include: material strength, geometric dimensions, initial deformation and defects, and reinforcement information of key force transmission components, in accordance with relevant specifications.

[0009] Optionally, establishing a structural numerical model based on the detection data, and performing structural analysis on the structural numerical model to obtain performance indicators include: A model consistent with the structure to be tested is established using finite element software; Assign the detection data of key force transmission components to the corresponding components in the model; For components that were not detected, random simulation was used to assign probabilistic values ​​based on prior information; Structural analysis is performed on the numerical model of the structure to simulate the performance response of the structure under load and to extract performance indicators.

[0010] Optionally, structural analysis of the numerical model of the structure includes: Perform static or dynamic seismic analysis to simulate the performance response of the structure under the influence of uncertain factors; The extracted performance indicators include the reaction force, displacement, inter-story drift angle, and base shear force at each node of the structure.

[0011] Optionally, structural reliability analysis based on the performance indicators includes: Extract key performance indicators from structural analysis results; The number of structural failures in all simulation samples was counted according to the preset structural failure criteria. The structural failure probability and structural reliability are calculated based on the number of failures and the total number of samples.

[0012] Technical advantages of this invention: This invention discloses a sampling method for structural entity inspection. By identifying and inspecting key force-transmitting components based on topology optimization, it significantly improves the accuracy of structural inspection, making the inspection data more representative of the overall mechanical performance of the structure, reducing misjudgments caused by unreasonable sampling, and thus making structural safety assessments more reliable. Simultaneously, while ensuring accuracy, this method minimizes the workload of inspection, avoids blindly inspecting a large number of non-critical components, effectively shortens the inspection cycle, reduces manpower, material resources, and time costs, and improves inspection efficiency. Attached Figure Description

[0013] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a comparison diagram of the structural response uncertainty between traditional sampling and optimized sampling methods in embodiments of the present invention; Figure 2 This is a comparison chart of the convergence of sampling methods to the true value under different volume fractions in embodiments of the present invention; Figure 3 This is a comparison chart of the overall performance of the sampling method in this invention in terms of uncertainty convergence and true convergence. Figure 4This is a flowchart illustrating a structural entity detection sampling method according to an embodiment of the present invention. Detailed Implementation

[0014] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0015] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0016] like Figure 4 As shown, this embodiment provides a structural entity detection sampling method, including: Key force-transmitting components are identified from the structure to be tested based on topology optimization; On-site physical testing was conducted on the key force transmission components to obtain test data; A structural numerical model is established based on the detection data, and structural analysis is performed on the structural numerical model to obtain performance indicators. Structural reliability analysis is performed based on the aforementioned performance indicators.

[0017] Furthermore, key force-transmitting components identified from the structure under test based on topology optimization include: A digital model of the structure to be inspected is established. The digital model includes the number of spans, number of stories, node and unit numbers, loads, component cross-sectional properties, initial material properties, and boundary constraints of the structure. Set topology optimization parameters, set volume fraction constraints for the structure according to the number of component inspections required by the specification, and adopt a material interpolation scheme; The topology optimization solution is performed, and the relative density of the material is used as the design variable by using the variable density method. The global stiffness matrix is ​​integrated through iterative loops, the displacement response is solved, the objective function and constraint function are calculated, the gradient is calculated, the design variables are updated and the convergence is judged until the convergence criterion is met. The threshold of the design variables is calculated based on the material volume fraction constraint, and the components with material density greater than the threshold are selected as key force transmission components.

[0018] Furthermore, setting topology optimization parameters includes: The volume fraction constraint is set according to the number of tests required by the specification, whereby the volume fraction is the ratio of the volume of the tested component material to the total volume of the component material; The SIMP model is used as the material interpolation scheme, in which the elastic modulus of the material varies with the relative density and drives the intermediate density value to tend to 0 or 1 through a penalty factor.

[0019] Specifically, the implementation process of this embodiment includes: According to the specifications, the volume fraction constraint of the structure (volume of inspected component material / total volume of component material) is set based on the number of component inspections. The SIMP model is used as the material interpolation scheme, and its mathematical expression is as follows: ; in, It is the interpolated elastic modulus; It is the minimum elastic modulus (to avoid numerical singularities); It is the relative density, which is used as a design variable and has a value between 0 and 1. p It is a penalty factor (usually greater than 1, which can make the intermediate density value tend to 0 or 1). It is the original elastic modulus of the material.

[0020] Furthermore, performing topology optimization solutions includes: S1. The global stiffness matrix of the integrated structure is based on the current design variable values ​​and the penalty factor; S2. Solve the system of equations to obtain the global displacement response of the structure under load; S3. Calculate the values ​​of the objective function and constraint function based on the displacement response and design variables; S4. Use numerical methods to calculate the gradients of the objective function and constraint functions with respect to each design variable; S5. Update design variables using an optimizer based on convex approximation; S6. Repeat S1-S5 until the convergence criterion is met.

[0021] Specifically, the implementation process of this embodiment includes: The variable density method is used for topology optimization of the structure. The relative density of the material is treated as a continuous design variable between 0 and 1. When the relative density is 0, the component is discarded, and when it is 1, the component is retained. A cyclic iterative method is used to solve the optimization problem, and the penalty factor of the SIMP model is gradually increased in each iteration. p This makes the intermediate density between 0 and 1 more inclined towards 0 or 1. Repeat this process 10 times: Stiffness matrix integration: based on current design variable values ​​and penalty factors for the current stage. p The global stiffness matrix of the entire structure is obtained by integration. ; Solve the system of equations The structure under load is obtained. F Global displacement response under action U .

[0022] Based on displacement response U Given the current design variables, calculate the current values ​​of the objective function and constraint functions.

[0023] Numerical methods (such as the finite difference method) are used to calculate the gradients of the objective function and constraint functions with respect to each design variable.

[0024] Design variable update: Input the function values ​​and their gradients calculated in the previous steps into an optimizer based on convex approximation (such as the moving asymptote method) to solve an approximate subproblem, thereby obtaining a set of updated and better design variables.

[0025] Convergence check: until the convergence criterion (such as the KKT optimality condition) is met, otherwise return and re-enter the loop.

[0026] Finally, the method of calculating the threshold of design variables based on the material volume fraction constraint can be used to select components with material density greater than the threshold, and obtain the optimal structural form under the required number of components, i.e., the key components.

[0027] Furthermore, the acquisition of test data includes: obtaining the material strength, component geometry, initial deformation and defects, and reinforcement information of key force transmission components in accordance with relevant specifications.

[0028] Furthermore, based on the detection data, a structural numerical model is established, and structural analysis is performed on the structural numerical model to obtain performance indicators, including: A model consistent with the structure to be tested is established using finite element software; Assign the detection data of key force transmission components to the corresponding components in the model; For components that were not detected, random simulation was used to assign probabilistic values ​​based on prior information; Structural analysis is performed on the numerical model of the structure to simulate the performance response of the structure under load and to extract performance indicators.

[0029] Specifically, the implementation process of this embodiment includes: A model is built using finite element software, and a consistent model is established for the structure. The real data of the detected key components are assigned to the corresponding components in the model. For the remaining components that were not detected, their material properties and other parameters are assigned probabilistically using random simulation (such as Monte Carlo method) based on prior information (such as material factory data, specifications, etc.).

[0030] Furthermore, structural analysis of the numerical model of the structure includes: Perform static or dynamic seismic analysis to simulate the performance response of the structure under the influence of uncertain factors; The extracted performance indicators include the reaction force, displacement, inter-story drift angle, and base shear force at each node of the structure.

[0031] Specifically, the implementation process of this embodiment includes: Structural analysis, such as static or dynamic seismic analysis, is performed on the established probabilistic model to simulate the performance response of the structure under the influence of various uncertain factors.

[0032] Furthermore, structural reliability analysis based on the aforementioned performance indicators includes: Extract key performance indicators from structural analysis results; The number of structural failures in all simulation samples was counted according to the preset structural failure criteria. The structural failure probability and structural reliability are calculated based on the number of failures and the total number of samples.

[0033] Specifically, the implementation process of this embodiment includes: Key performance indicators are extracted from the simulation results, such as the reaction force, displacement, inter-story drift angle, and base shear force at each node of the structure.

[0034] The statistical distribution of performance indicators was analyzed, and the number of structural failures in all simulation samples was counted based on preset structural failure criteria (e.g., inter-story drift angle exceeding the limit). n .

[0035] Number of times n Perform statistical analysis to calculate structural reliability: ; ; in, This represents the probability of structural failure. n The number of failures. N For the total number, For structural reliability.

[0036] An application example of this invention: Step 1: Establish and analyze the original structural model. 1.1 A two-dimensional planar frame structure with 10 floors and 8 spans is set as the research object.

[0037] 1.2 Assign material properties to all components of the model. Assume that the concrete strength of each component is consistent. Use the Burr distribution to simulate the random distribution of concrete strength. Assign material properties to each component and apply loads according to the specifications.

[0038] 1.3 Use finite element software (such as OpenSees) to perform pushover analysis on the benchmark model, and record its displacement-base reaction curve and maximum base reaction, etc., as a performance standard for subsequent comparison.

[0039] Step 2: Structural Optimization 2.1 Based on the original structure, establish a digital model, including defining the number of spans, number of stories, node and unit numbers, loads, component section properties, initial material properties, boundary constraints, and degrees of freedom of the structure.

[0040] 2.2 Setting the material volume fraction: In this embodiment, we calculated nine cases with volume fractions ranging from 10% to 90%, and set the objective function as the total strain energy of the structure.

[0041] 2.2 Run the topology optimization program to calculate the optimal structural layout under different volume fraction constraints from 10% to 90%, and record and save the results for the next step of analysis.

[0042] Step 3: Construct and analyze the "optimized sampling group" and the "traditional sampling control group". 3.1 Optimize the sampling group: 3.1.1 Based on the optimization results of step 2, select the key components for the corresponding cases.

[0043] 3.1.2 Establish 1000 analysis models using OpenSees. In each model, assign the selected key components the original structural component attributes obtained in step 1; the attributes of the remaining unselected components are then randomly assigned using the Burr distribution.

[0044] 3.1.3 Pushover analysis was performed on each of the 1000 models.

[0045] 3.2 Traditional sampling control group: 3.2.1 Randomly select the same number of components as the optimized sampling group.

[0046] 3.2.2 Using the same method as the optimized sampling group, 1000 models were established. Randomly selected components were assigned the corresponding component attributes of the original structure, and the remaining components were randomly assigned values ​​and Pushover analysis was performed.

[0047] Step 4: Statistical analysis and comparative verification of the results. 4.1 Data extraction: The base node reaction forces of the optimized sampling group and the traditional control group were statistically analyzed, and the displacement-base reaction force curve and the maximum base reaction force of each model were calculated.

[0048] 4.2 Comparative Analysis: Calculate and compare the standard deviation and variance of the maximum basal reaction force results for the optimized sampling group and the traditional control group.

[0049] 4.3 Compare the statistical results of the two groups with the performance data of the “real” structure obtained in step one.

[0050] 4.4 Conclusion: By comparing the data dispersion (standard deviation / variance), the superiority of the topology optimization-based sampling method over the traditional random sampling method in reducing the uncertainty of structure detection is verified, as well as the reliability of its evaluation results with the real data.

[0051] like Figures 1-3 The results show that the topology-optimized sampling method outperforms traditional sampling methods in terms of both uncertainty (reflected by the standard deviation) and accuracy (reflected by comparison with real structural data) of the maximum base reaction force. As the volume fraction increases, the convergence speed of the data results in terms of uncertainty and convergence to the true value is significantly faster than that of traditional methods, and the difference widens with increasing volume fraction.

[0052] This invention presents a sampling method for frame structure inspection based on topology optimization. By leveraging a topology optimization algorithm, it can accurately identify key force-transmitting components. Using these components as sampling points makes the inspection data more representative of the overall mechanical performance of the structure, significantly improving the accuracy of the inspection results. Compared to traditional methods, it reduces misjudgments caused by unreasonable sampling, making structural safety assessments more reliable and providing solid data support for subsequent structural maintenance and reinforcement decisions. Secondly, inspection efficiency is greatly improved. Traditional sampling may blindly inspect a large number of components, consuming significant manpower, resources, and time. This invention, while ensuring accuracy, minimizes the workload, shortens the inspection cycle, and reduces inspection costs.

[0053] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A sampling method for structural entity detection, characterized in that, include: Key force-transmitting components are identified from the structure to be tested based on topology optimization; On-site physical testing was conducted on the key force transmission components to obtain test data; A structural numerical model is established based on the detection data, and structural analysis is performed on the structural numerical model to obtain performance indicators. Structural reliability analysis is performed based on the aforementioned performance indicators; Key force-transmitting components identified from the structure under test based on topology optimization include: A digital model of the structure to be inspected is established. The digital model includes the number of spans, number of stories, node and unit numbers, loads, component cross-sectional properties, initial material properties, and boundary constraints of the structure. Set topology optimization parameters, set volume fraction constraints for the structure according to the number of component inspections required by the specification, and adopt a material interpolation scheme; The topology optimization solution is performed. The relative density of the material is used as the design variable by using the variable density method. The global stiffness matrix is ​​integrated through iterative loops, the displacement response is solved, the objective function and constraint function are calculated, the gradient is calculated, the design variables are updated and the convergence is judged. After the convergence criterion is met, the threshold of the design variables is calculated based on the material volume fraction constraint, and the components with material density greater than the threshold are selected as key force transmission components. Based on the detection data, a structural numerical model is established, and structural analysis is performed on the structural numerical model to obtain performance indicators, including: A model consistent with the structure to be tested is established using finite element software; Assign the detection data of key force transmission components to the corresponding components in the model; For components that were not detected, random simulation was used to assign probabilistic values ​​based on prior information; Structural analysis is performed on the numerical model of the structure to simulate the performance response of the structure under load and to extract performance indicators.

2. The structural entity inspection sampling method as described in claim 1, characterized in that, Setting topology optimization parameters includes: The volume fraction constraint is set according to the number of tests required by the specification, whereby the volume fraction is the ratio of the volume of the tested component material to the total volume of the component material; The SIMP model is used as the material interpolation scheme, in which the elastic modulus of the material varies with the relative density and drives the intermediate density value to tend to 0 or 1 through a penalty factor.

3. The structural entity inspection sampling method as described in claim 1, characterized in that, Performing topology optimization solutions includes: S1. The global stiffness matrix of the integrated structure is based on the current design variable values ​​and the penalty factor; S2. Solve the system of equations to obtain the global displacement response of the structure under load; S3. Calculate the values ​​of the objective function and constraint function based on the displacement response and design variables; S4. Use numerical methods to calculate the gradients of the objective function and constraint functions with respect to each design variable; S5. Update design variables using an optimizer based on convex approximation; S6. Repeat S1-S5 until the convergence criterion is met.

4. The structural entity inspection sampling method as described in claim 1, characterized in that, The acquisition of test data includes: obtaining the material strength, geometric dimensions, initial deformation and defects, and reinforcement information of key force transmission components in accordance with relevant specifications.

5. The structural entity inspection sampling method as described in claim 1, characterized in that, Structural analysis of the numerical model of the structure includes: Perform static or dynamic seismic analysis to simulate the performance response of the structure under the influence of uncertain factors; The extracted performance indicators include the reaction force, displacement, inter-story drift angle, and base shear force at each node of the structure.

6. The structural entity inspection sampling method as described in claim 1, characterized in that, Structural reliability analysis based on the aforementioned performance indicators includes: Extract key performance indicators from structural analysis results; The number of structural failures in all simulation samples was counted according to the preset structural failure criteria. The structural failure probability and structural reliability are calculated based on the number of failures and the total number of samples.

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