Method for modeling internal defects of concrete and evaluating seismic performance

Abstract

The application discloses a kind of concrete internal defect modeling and seismic performance evaluation method, belong to concrete structure safety performance evaluation field.It includes: to the concrete structure is carried out quasi-static loading test, obtains quasi-static test result;The nondestructive testing result of internal defect of existing building concrete structure is statistically analyzed, and the defect characteristic index is obtained;Based on quasi-static test result and defect characteristic index, the random defect finite element model of concrete structure is constructed;Defect characteristic statistics are carried out to random defect finite element model and are simulated and solved, and the structure seismic performance index is obtained;Comprehensive analysis is carried out between structure seismic performance index and the relationship of defect characteristic index, quantifies the influence of defect characteristic index on structure seismic performance index, and the seismic performance evaluation result of concrete structure is obtained.The application solves the problem that existing technology is difficult to quantitatively describe concrete internal defect and evaluate seismic performance accordingly, provides basis for concrete structure safety evaluation.

Description

Technical Field

[0001] This invention relates to the field of safety performance assessment of concrete structures, and in particular to a method for modeling internal defects in concrete and assessing its seismic performance. Background Technology

[0002] With the acceleration of urban health checks and urban renewal, the demand for existing building inspections and structural safety performance assessments is increasing. Furthermore, with the development of various non-destructive testing equipment and technologies for concrete, more and more internal defects in the concrete of existing buildings are being detected and exposed. However, although existing reinforced concrete testing methods, combined with machine learning, can detect defects such as voids, inclusions, and cracks within concrete structures, they lack a quantitative description of these internal defects. Therefore, it is impossible to determine the impact of defect type, size, and distribution on structural mechanical performance, especially seismic performance. This poses significant challenges to structural health monitoring and maintenance, and also creates many hidden dangers for the long-term safe use of structures. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for modeling internal defects in concrete and evaluating its seismic performance. This method solves the problem of difficulty in quantitatively describing internal defects in concrete and evaluating its seismic performance in existing urban physical examinations, and provides a basis for structural safety evaluation in urban physical examinations.

[0004] The technical solution provided by this invention is as follows:

[0005] A method for modeling internal defects in concrete and evaluating its seismic performance, the method comprising:

[0006] S1: Conduct a quasi-static loading test on the concrete structure and obtain the quasi-static test results;

[0007] S2: Based on the non-destructive testing results of internal defects in existing building concrete structures, statistical analysis is performed on the internal defects of the concrete structures to obtain defect characteristic indicators.

[0008] The defect characteristic indicators include the location, volume, location distribution parameters, and volume distribution parameters of each internal defect;

[0009] S3: Based on the quasi-static test results and the defect characteristic index, construct a finite element model of random defects in the concrete structure;

[0010] S4: Perform defect feature statistics on the random defect finite element model and simulate the solution to obtain the structural seismic performance index under different defect conditions;

[0011] S5: Analyze the relationship between the structural seismic performance index and the defect characteristic index, quantify the impact of the defect characteristic index on the structural seismic performance index, and obtain the seismic performance evaluation results of the concrete structure.

[0012] Furthermore, S2 includes:

[0013] S21: The internal defects of the existing building concrete structure are three-dimensionally imaged by non-destructive testing methods to obtain three-dimensional volume data of the internal defects of the concrete, which is used as the non-destructive testing result.

[0014] S22: Discretize the three-dimensional volumetric data space of the internal defects of the concrete into a uniform three-dimensional voxel mesh, and extract the defects to obtain the defect voxel set of each internal defect;

[0015] S23: Calculate the location and volume of each internal defect based on the defect voxel set of each internal defect;

[0016] S24: Construct corresponding probability density functions based on the location and volume of internal defects, respectively, to characterize the location distribution parameters and volume distribution parameters of internal defects.

[0017] Furthermore, the probability density functions used for the location and volume of the internal defects are normal distribution and Wilson distribution, respectively.

[0018] Furthermore, S3 includes:

[0019] S31: Divide the concrete block to be evaluated into a three-dimensional uniform mesh to obtain several three-dimensional finite element elements;

[0020] S32: Randomly select a finite element that is not occupied by an existing defect cluster as the seed element of the current defect cluster;

[0021] The defect clusters include multiple internal defects that are spatially aggregated.

[0022] S33: Randomly generate the total number of defect units contained in the current defect cluster using a normal distribution or a Weibull distribution;

[0023] S34: Starting from the seed unit, gradually add adjacent finite element units to the current defect cluster until the total number of finite element units in the current defect cluster reaches the total number of defect units.

[0024] In each step, one element is randomly selected from all finite element elements adjacent to the boundary of the current defect cluster that are not occupied by existing defect clusters and added to the current defect cluster.

[0025] S35: Return to S32 and generate the next defect cluster until the total number of generated defect clusters reaches the set quantity threshold, or the sum of the total number of defect units in all defect clusters reaches the set volume threshold.

[0026] S36: Set stiffness and / or strength parameters for all finite element elements contained in defect clusters to be lower than those for normal finite element elements.

[0027] Furthermore, in the process of randomly selecting seed elements, randomly generating the total number of defect elements, and randomly selecting finite element elements to add to the current defect cluster, the same random seed is used. Controlling stochastic processes;

[0028]

[0029] in, The global base seed value is CaseID, which is the number of the different concrete blocks to be evaluated, and RealizationID is the sequence number of the different random defect finite element models constructed under the same concrete block to be evaluated.

[0030] Furthermore, S4 includes:

[0031] S41: Perform defect feature statistics on the random defect finite element model;

[0032] The defect features include basic geometric statistics, defect path correlation evaluation indicators, and spatial clustering evaluation indicators.

[0033] S42: Simulate and solve the random defect finite element model to output the structural seismic performance index under different defect conditions;

[0034] The structural seismic performance indicators include one or more of the following: structural reaction-displacement hysteresis curve, concrete tensile-compressive damage cloud map, structural energy dissipation curve, and stiffness degradation curve.

[0035] Furthermore, the basic geometric statistics include the centroid, spatial dispersion, and volume statistics of internal defects;

[0036] Wherein, the three-dimensional coordinates of the centroid are: ;

[0037]

[0038] Let be the body-center three-dimensional coordinates of the i-th internal defect. , This represents the total number of internal defects.

[0039] The spatial dispersion is determined by the degree of dispersion of the internal defects in the x-direction. The degree of dispersion in the y direction and the degree of dispersion in the z direction express;

[0040]

[0041] The volume statistics include the total volume of internal defects. Average volume and volume variation coefficient ;

[0042]

[0043]

[0044]

[0045] Let be the volume of the i-th internal defect. This represents the standard deviation of the internal defect volume.

[0046] Furthermore, the defect path association evaluation index includes the degree of path damage and the concentration of defect paths;

[0047] The degree of path damage and the concentration of defective paths are calculated through the following process:

[0048] The force transmission path is determined based on the mechanical properties of the concrete block to be evaluated, and the distance d from each internal defect to the force transmission path is calculated.

[0049] The influence area of ​​the path is determined based on the bandwidth parameter w of the set force transmission path, and internal defects with a distance d not greater than w are regarded as path-related defects.

[0050] The degree of path damage is calculated using the following formula. and defect path concentration :

[0051]

[0052]

[0053] in, This refers to the set of finite element elements contained within the path influence region of the force transmission path. This is the set of finite element elements contained in all path-related defects.

[0054] Furthermore, the defect path association evaluation index also includes the defect penetration index;

[0055] The defect penetration index is calculated through the following process:

[0056] The path-affected area is divided equally along the path direction. Segment by segment, count whether path association defects exist within each segment, and find the maximum number of segments consecutively containing path association defects. ;

[0057] The defect penetration index is calculated using the following formula. :

[0058] .

[0059] Furthermore, the spatial clustering evaluation index The calculation formula is as follows:

[0060]

[0061] in, Let j be the volume of the j-th internal defect. , Let be the spatial weight between the i-th and j-th internal defects.

[0062] The present invention has the following beneficial effects:

[0063] The method for modeling internal defects in concrete and evaluating its seismic performance provided by this invention can quickly establish a finite element model of a reinforced concrete structure based on non-destructive testing results, conduct statistical analysis and simulation solutions of model defects, and finally combine defect statistical parameters and numerical simulation results to form a method for evaluating the seismic performance of concrete structures with random defects. This method is applicable to the detection of defects in existing reinforced concrete and the evaluation of its seismic performance, and solves the problem that it is difficult to quantitatively describe internal defects in concrete and evaluate seismic performance based on this in existing urban physical examinations. It provides a basis for the evaluation of structural safety in urban physical examinations. Attached Figure Description

[0064] Figure 1 A flowchart of the concrete internal defect modeling and seismic performance evaluation method of the present invention;

[0065] Figure 2 This is a schematic diagram illustrating the generation of defect clusters. Detailed Implementation

[0066] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0067] This invention provides a method for modeling internal defects in concrete and evaluating its seismic performance, such as... Figure 1 As shown, the method includes:

[0068] S1: Conduct a quasi-static loading test on the concrete structure to obtain the quasi-static test results.

[0069] Among them, the seismic performance test of concrete structure is mainly based on full-scale and scaled-down structural models. The quasi-static loading test is used to obtain the reaction force-displacement hysteresis curve, structural failure characteristics and other quasi-static test results. At the same time, the quasi-static test results can provide verification support data for finite element simulation analysis and verify the material constitutive relationship and mesh convergence in the subsequent finite element modeling process.

[0070] S2: Based on the non-destructive testing results of internal defects in existing building concrete structures, statistical analysis is performed on the internal defects of the concrete structures to obtain defect characteristic indicators.

[0071] This step is based on the results of non-destructive testing of concrete structures. It analyzes the types and statistical laws of internal defects in concrete, and extracts the location, volume, location distribution parameters and volume distribution parameters of each internal defect for subsequent finite element model establishment.

[0072] In one example, this step is implemented as follows:

[0073] S21: The internal defects of the concrete structure of the existing building are imaged in three dimensions using non-destructive testing methods to obtain three-dimensional volume data of the internal defects of the concrete, which is used as the result of non-destructive testing.

[0074] Currently, commonly used non-destructive testing methods for concrete in engineering include ultrasonic array testing and ground penetrating radar testing. These non-destructive testing methods, combined with machine learning, can perform three-dimensional imaging and defect statistics on internal defects of concrete structures.

[0075] S22: Discretize the three-dimensional volumetric data space of internal defects in concrete into a uniform three-dimensional voxel mesh, and extract the defects to obtain the defect voxel set for each internal defect.

[0076] This invention performs statistical analysis on internal defects in concrete based on existing non-destructive testing imaging results. It preprocesses the three-dimensional volume data of internal defects in concrete obtained by non-destructive testing such as ground penetrating radar or ultrasonic arrays, including offset / focus imaging and spatial discretization into a uniform voxel grid. Finally, it extracts the defect voxel set through adaptive threshold segmentation and connected component analysis.

[0077] S23: Calculate the location and volume of each internal defect based on the defect voxel set of each internal defect.

[0078] For volume V, the defect size is expressed as volume:

[0079]

[0080] This represents the total number of defect voxels contained within the internal defects. For radar imaging in Spatial resolution in three directions (unit: mm).

[0081] For location, let the body center C of the internal defect be used as an example:

[0082]

[0083] These are the center coordinates of the defect voxel.

[0084] For ultrasonic array detection, isotropic resampling is further increased to correct for differences in depth and lateral resolution, and a reflection amplitude-aperture model is used to estimate the volume of crack-like defects. A is the surface area of ​​the crack. (To estimate the opening degree).

[0085] For ground-penetrating radar detection, if there is insufficient resolution, sub-voxel boundary interpolation can be introduced to correct volume errors. This method is based directly on discrete voxel data for calculation, without the need to reconstruct the defect surface. It is compatible with irregular, multi-connected and thin plate-shaped defects, and provides accurate geometric feature input for subsequent defect statistical analysis and structural seismic performance assessment.

[0086] This invention directly utilizes discrete voxel data from non-destructive testing (ultrasonic array, ground penetrating radar) imaging, calculates the volume of irregular defects by voxel counting, and calculates the body center by averaging the voxel center coordinates, without the need to reconstruct the defect surface, and is compatible with any irregular shape such as cracks and holes.

[0087] S24: Construct corresponding probability density functions based on the location and volume of internal defects, respectively, to characterize the location distribution parameters and volume distribution parameters of internal defects.

[0088] Within a given measurement range, this invention uses the Weibull distribution and the normal distribution to characterize the location distribution parameters and volume distribution parameters of defects, respectively. The specific steps are as follows:

[0089] 1) Weibull statistical modeling of defect size parameters:

[0090] Extract the volume of all detected defects within the measurement range. (or equivalent diameter) Since the defect size is usually positive and right-skewed, a Weibull distribution is used for fitting, and its probability density function is:

[0091]

[0092] The shape parameter k and the scale parameter are calculated using maximum likelihood estimation. The k-value reflects the degree of concentration of the defect volume. This indicates that minor defects are dominant. This indicates the existence of a characteristic dominant volume. The model represents the characteristic volume value and can be used to describe the statistical regularity of defect size within the measurement range.

[0093] 2) Normal distribution representation of defect spatial location:

[0094] Establish a coordinate system with a corner point of the measurement range as the origin. The body-centered coordinates of all defects It is treated as a three-dimensional random vector. Assuming that the coordinate components are independent or considering correlation, a multivariate normal distribution is used for fitting.

[0095]

[0096] Its expected value is:

[0097]

[0098] The mean vector The covariance matrix represents the average location of the defective population. The dispersion of defects in three directions is described and estimated using the sample mean and sample covariance matrix. This normal model can quantitatively characterize the spatial clustering tendency and dispersion characteristics of defects within the measurement range.

[0099] The Weibull distribution and the normal distribution together constitute a complete probabilistic statistical description of the defect characteristics within the measurement range: the former is used to randomly generate the defect size, and the latter is used to randomly generate the defect's spatial coordinates. The final statistical parameters are... The data can be directly input into the subsequent finite element model for random defect generation and probabilistic seismic performance assessment. If the amount of detection data is small, Bootstrap resampling or the introduction of Bayesian prior information can be used to enhance the robustness of statistical parameters; if the defect size exhibits a bimodal distribution, a hybrid Weibull model can be used instead. All of the above methods fall within the scope of protection of this invention.

[0100] S3: Based on the results of quasi-static tests and defect characteristic indicators, a finite element model of random defects in concrete structures is constructed.

[0101] The finite element modeling of random defects in concrete structures is first compared with the results of quasi-static tests to ensure the accuracy of the model calculations. Then, the random element method is used, combined with defect characteristic indicators from concrete defect detection, to model internal random defects. The basic idea of ​​this method is to divide the structural mesh into several finite element elements, and then randomly select a portion of these finite element elements as defect elements, assigning these defect elements lower material stiffness or strength parameters.

[0102] Since internal defects in concrete (such as pores, crack clusters, and inclusions) typically exhibit a non-uniform cluster distribution in space, rather than an independent, uniform, and random distribution, and each defect cluster comprises multiple internal defects arranged in an aggregated manner in space, this invention proposes a random defect modeling method based on cluster growth, specifically including the following steps:

[0103] S31: Divide the concrete block to be evaluated into a three-dimensional uniform mesh to obtain several three-dimensional finite element elements, each of which represents a minimum defect element.

[0104] S32: Randomly select a finite element that is not occupied by an existing defect cluster as the seed element of the current defect cluster.

[0105] S33: Randomly generate the total number of defect units Starget contained in the current defect cluster using a normal distribution or a Weibull distribution.

[0106] Specifically: if a log-normal distribution is used, then ,in It was obtained by fitting actual detection data.

[0107] If the Weibull distribution is used, its probability density function is: Shape parameter k and scale parameter It is also determined based on statistics.

[0108] Finally, the sampled Starget is rounded to the nearest integer and used as the total number of units that the current cluster aims to achieve.

[0109] S34: Starting from the seed element, gradually add adjacent finite element elements to the current defect cluster until the total number of finite element elements in the current defect cluster reaches the total number of defect elements.

[0110] Specifically, starting with the seed element, adjacent finite element elements are gradually added to the current cluster according to their adjacency relationships. At each step, one adjacent finite element is randomly selected from all candidate adjacent finite element elements at the current cluster boundary. Figure 2 As shown. Repeat this process until the total number of elements in the current cluster reaches Starget. If a finite element is encountered that is already occupied by another defect cluster during the growth process, it is skipped and a new candidate finite element is selected to ensure that different defect clusters do not overlap.

[0111] S35: Return to S32 and generate the next defect cluster until the total number of generated defect clusters reaches the set quantity threshold, or the sum of the total number of defect units in all defect clusters reaches the set volume threshold.

[0112] Once the total number of finite element elements within the current defect cluster reaches the total number of defect elements, expansion stops. Then, return to step S32 to begin generating the next defect cluster, until the total number of defect clusters generated within the measurement range reaches a preset value, or the total defect volume reaches a predetermined threshold.

[0113] S36: Set stiffness and / or strength parameters for all finite element elements contained in defect clusters to be lower than those for normal finite element elements.

[0114] Because the spatial distribution of defects is random, this invention proposes a defect distribution sample generation method based on random seed control to ensure the reproducibility of simulation results while maintaining the random independence between different samples. Specifically:

[0115] Suppose we need to generate N random defect finite element models for a specific concrete block to be evaluated, with each model corresponding to one simulation. In this invention, the random number sequence for each simulation is uniquely determined by a random seed, Seed, which is calculated using the following formula:

[0116]

[0117] in, This is a global base seed value, set by the user based on system time or a fixed constant.

[0118] CaseID is the number of the different concrete blocks to be evaluated, and its value is a non-negative integer.

[0119] RealizationID is the serial number of the different random defect finite element models constructed for the same concrete block to be evaluated.

[0120] The factor of 1000 is used to numerically isolate different combinations of CaseID and RealizationID, ensuring the uniqueness of the mapping.

[0121] After setting the random seed, during the process of randomly selecting seed elements, randomly generating the total number of defect elements, and randomly selecting finite element elements to add to the current defect cluster (i.e., the growth path), the same random seed is used. Controlling random processes.

[0122] This invention can generate a random defect distribution model that conforms to the spatial aggregation characteristics of actual concrete defect clusters. All random processes (seed location, cluster size, growth path) are controlled by the same random seed, ensuring the reproducibility of simulation results and statistical independence between different implementations. The defect cluster generation process is as follows: Figure 2 As shown.

[0123] In this way, multiple defect models with the same statistical parameters but different spatial distributions can be generated, thus providing a sample basis for subsequent statistical analysis.

[0124] S4: Perform defect feature statistics on the random defect finite element model and conduct simulation to obtain the structural seismic performance index under different defect conditions.

[0125] As an improvement to an embodiment of the present invention, this step includes:

[0126] S41: Perform defect feature statistics on the random defect finite element model.

[0127] To comprehensively quantify the geometric characteristics, spatial distribution patterns, and correlation with key force transmission paths of a stochastic defect finite element model, this invention establishes a set of evaluation index systems, including basic geometric statistics, defect path correlation evaluation indexes, and spatial clustering evaluation indexes.

[0128] 1. Basic geometric statistics include the centroid, spatial dispersion, and volume statistics of internal defects.

[0129] For the center of gravity, its three-dimensional coordinates are: .

[0130]

[0131] in, Let be the body-center three-dimensional coordinates of the i-th internal defect. , This represents the total number of internal defects.

[0132] Spatial dispersion is determined by the degree of dispersion of internal defects in the x-direction. The degree of dispersion in the y direction and the degree of dispersion in the z direction The greater the dispersion, the more dispersed the defect distribution; conversely, the smaller the dispersion, the more concentrated the defect distribution.

[0133]

[0134] Volume statistics include the total volume of internal defects. Average volume and volume variation coefficient It is used to describe the overall level and variability of defect size.

[0135]

[0136]

[0137]

[0138] Let be the volume of the i-th internal defect. This represents the standard deviation of the internal defect volume.

[0139] 2. The evaluation indicators for defect path association include path damage degree, defect path concentration, and defect penetration index.

[0140] Under seismic loads, concrete members typically form primary force transmission paths. If internal defects are concentrated in these critical areas, the structure's load-bearing capacity will be significantly weakened. Therefore, this invention proposes a defect path dependence characterization method, the specific process of which is as follows:

[0141] (1) Determine the force transmission path based on the mechanical properties of the concrete block to be evaluated, and calculate the distance d from each internal defect to the force transmission path.

[0142] The force transmission path needs to be determined based on structural mechanics analysis. Taking the diagonal compression member path of a shear wall as an example, this path can be represented by the following straight-line equation:

[0143]

[0144] Where a, b, and c are the path straight line parameters, which are determined by the coordinates of the two endpoints of the path.

[0145] (2) Determine the path influence area based on the bandwidth parameter w of the set force transmission path, and regard internal defects with a distance d not greater than w as path-related defects.

[0146] Specifically, for the center point of any internal defect within the measurement range... Its vertical distance d from the above path is:

[0147]

[0148] Then set the path bandwidth parameters. ,like If the defect is located within the path's influence area, then the set of all defects that meet the condition is denoted as Path.

[0149] (3) Calculate the path damage level using the following formula. and defect path concentration :

[0150]

[0151]

[0152] in, This refers to the set of finite element elements contained within the path influence region of the force transmission path. This is the set of finite element elements containing all path-related defects. The degree of path damage. This indicates the proportion of units in the path-affected area occupied by defects, reflecting the degree of damage to critical force transmission paths. Defect path concentration. It represents the proportion of the total defect volume located within the path influence area, reflecting whether defects tend to cluster on the critical path.

[0153] (4) Divide the area affected by the path into N equal parts along the path direction. p Segment by segment, count whether path association defects exist within each segment, and find the maximum number of segments consecutively containing path association defects. Calculate the defect penetration index .

[0154] .

[0155] The closer the value is to 1, the more severe the damage to the structural bearing capacity, as the defect forms a continuous, penetrating zone along the path.

[0156] 3. Spatial Clustering Evaluation Index I is used to quantify the autocorrelation of defect attributes (such as defect volume) in the global space, determining whether the defect distribution is clustered, discrete, or random. The calculation formula is:

[0157]

[0158] in, Let j be the volume of the j-th internal defect. , Let be the spatial weight between the i-th and j-th internal defects; if they are adjacent, then... Take 1, if not adjacent Take 0, or Calculated based on the distance between the two.

[0159] I range of values : This indicates positive autocorrelation (clustered distribution). This indicates negative autocorrelation (uniform dispersion). It represents a random distribution.

[0160] S42: Simulate and solve the finite element model of random defects, and output the seismic performance index of the structure under different defect conditions.

[0161] After modeling is completed, the model solving stage begins, outputting structural reaction-displacement hysteresis curves, concrete tensile and compressive damage cloud maps, structural energy dissipation curves, and stiffness degradation curves for different defect conditions.

[0162] S5: Comprehensively analyze the relationship between structural seismic performance indicators and defect characteristic indicators, quantify the impact of defect characteristic indicators on structural seismic performance indicators, establish a defect-seismic performance evaluation standard, and thus obtain the seismic performance evaluation results of concrete structures.

[0163] The method for modeling internal defects in concrete and evaluating its seismic performance provided by this invention can quickly establish a finite element model of a reinforced concrete structure based on non-destructive testing results, conduct statistical analysis and simulation solutions of model defects, and finally combine defect statistical parameters and numerical simulation results to form a method for evaluating the seismic performance of concrete structures with random defects. This method is applicable to the detection of defects in existing reinforced concrete and the evaluation of its seismic performance, and solves the problem that it is difficult to quantitatively describe internal defects in concrete and evaluate seismic performance based on this in existing urban physical examinations. It provides a basis for the evaluation of structural safety in urban physical examinations.

[0164] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention.

Claims

1. A method for modeling internal defects in concrete and evaluating its seismic performance, characterized in that, The method includes: S1: Conduct a quasi-static loading test on the concrete structure and obtain the quasi-static test results; S2: Based on the non-destructive testing results of internal defects in existing building concrete structures, statistical analysis is performed on the internal defects of the concrete structures to obtain defect characteristic indicators. The defect characteristic indicators include the location, volume, location distribution parameters, and volume distribution parameters of each internal defect; S3: Based on the quasi-static test results and the defect characteristic index, construct a finite element model of random defects in the concrete structure; S4: Perform defect feature statistics on the random defect finite element model and simulate the solution to obtain the structural seismic performance index under different defect conditions; S5: Analyze the relationship between the structural seismic performance index and the defect characteristic index, quantify the impact of the defect characteristic index on the structural seismic performance index, and obtain the seismic performance evaluation results of the concrete structure.

2. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 1, characterized in that, S2 includes: S21: The internal defects of the existing building concrete structure are imaged in three dimensions by non-destructive testing of concrete to obtain three-dimensional volume data of the internal defects of the concrete, which is used as the non-destructive testing result. S22: Discretize the three-dimensional volumetric data space of the internal defects of the concrete into a uniform three-dimensional voxel mesh, and extract the defects to obtain the defect voxel set of each internal defect. S23: Calculate the location and volume of each internal defect based on the defect voxel set of each internal defect; S24: Construct corresponding probability density functions based on the location and volume of internal defects, respectively, to characterize the location distribution parameters and volume distribution parameters of internal defects.

3. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 2, characterized in that, The location and volume of the internal defects are determined using probability density functions of normal and Wilson distributions, respectively.

4. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 3, characterized in that, S3 includes: S31: Divide the concrete block to be evaluated into a three-dimensional uniform mesh to obtain several three-dimensional finite element elements; S32: Randomly select a finite element that is not occupied by an existing defect cluster as the seed element of the current defect cluster; The defect clusters include multiple internal defects that are spatially aggregated. S33: Randomly generate the total number of defect units contained in the current defect cluster using a normal distribution or a Weibull distribution; S34: Starting from the seed unit, gradually add adjacent finite element units to the current defect cluster until the total number of finite element units in the current defect cluster reaches the total number of defect units. In each step, one element is randomly selected from all finite element elements adjacent to the boundary of the current defect cluster that are not occupied by existing defect clusters and added to the current defect cluster. S35: Return to S32 and generate the next defect cluster until the total number of generated defect clusters reaches the set quantity threshold, or the sum of the total number of defect units in all defect clusters reaches the set volume threshold. S36: Set stiffness and / or strength parameters for all finite element elements contained in defect clusters to be lower than those for normal finite element elements.

5. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 4, characterized in that, In the process of randomly selecting seed elements, randomly generating the total number of defect elements, and randomly selecting finite element elements to add to the current defect cluster, the same random seed is used. Controlling stochastic processes; in, The global base seed value is CaseID, which is the number of the different concrete blocks to be evaluated, and RealizationID is the sequence number of the different random defect finite element models constructed under the same concrete block to be evaluated.

6. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 4, characterized in that, S4 includes: S41: Perform defect feature statistics on the random defect finite element model; The defect features include basic geometric statistics, defect path association evaluation indicators, and spatial clustering evaluation indicators. S42: Simulate and solve the random defect finite element model to output the structural seismic performance index under different defect conditions; The structural seismic performance indicators include one or more of the following: structural reaction-displacement hysteresis curve, concrete tensile-compressive damage cloud map, structural energy dissipation curve, and stiffness degradation curve.

7. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 6, characterized in that, The basic geometric statistics include the centroid, spatial dispersion, and volume statistics of internal defects; Wherein, the three-dimensional coordinates of the centroid are ; For the first The body-centered three-dimensional coordinates of an internal defect , This represents the total number of internal defects. The spatial dispersion is determined by the degree of dispersion of the internal defects in the x-direction. The degree of dispersion in the y direction and the degree of dispersion in the z direction express; The volume statistics include the total volume of internal defects. Average volume and volume variation coefficient ; Let be the volume of the i-th internal defect. This represents the standard deviation of the internal defect volume.

8. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 7, characterized in that, The defect path association evaluation indicators include the degree of path damage and the concentration of defect paths; The degree of path damage and the concentration of defective paths are calculated through the following process: The force transmission path is determined based on the mechanical properties of the concrete block to be evaluated, and the distance d from each internal defect to the force transmission path is calculated. The influence area of ​​the path is determined based on the bandwidth parameter w of the set force transmission path, and internal defects with a distance d not greater than w are regarded as path-related defects. The degree of path damage is calculated using the following formula. and defect path concentration : in, This refers to the set of finite element elements contained within the path influence region of the force transmission path. This is the set of finite element elements contained in all path-related defects.

9. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 8, characterized in that, The defect path association evaluation index also includes the defect penetration index. The defect penetration index is calculated through the following process: The path-affected area is divided equally along the path direction. Segment by segment, count whether path association defects exist within each segment, and find the maximum number of segments consecutively containing path association defects. ; The defect penetration index is calculated using the following formula. : 。 10. The method for modeling internal defects in concrete and evaluating its seismic performance according to claim 7, characterized in that, The spatial clustering evaluation index The calculation formula is as follows: in, Let j be the volume of the j-th internal defect. , Let be the spatial weight between the i-th and j-th internal defects.

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