A method and system for testing seismic performance of engineering structures

Abstract

The present invention relates to a method and system for testing the seismic performance of an engineering structure, and relates to the field of seismic performance testing, including: obtaining the deployment area information of a target engineering structure and a three-dimensional model of the engineering structure, and obtaining a first set of vibration use cases; scaling the three-dimensional model of the engineering structure, and obtaining a three-dimensional model of a test engineering structure; scaling the first set of vibration use cases, and obtaining a second set of vibration use cases; manufacturing a test engineering structure, traversing the second set of vibration use cases, performing a pseudo-dynamic test, and obtaining a first set of seismic performance test results; when the first set of seismic performance test results has no limit seismic performance, optimizing the second set of vibration use cases, and obtaining a third set of vibration use cases; when the first set of seismic performance test results has limit seismic performance, outputting the limit seismic performance. The technical problem that the vibration use cases in the prior art are subjectively configured by users, resulting in a long time for each seismic performance test and low test efficiency is solved.

Description

Technical Field

[0001] The present invention relates to the field of seismic performance testing, and in particular to a seismic performance testing method and system for engineering structures. Background Art

[0002] The seismic performance test of engineering structures is an important means to ensure the safety of engineering structures. The traditional seismic performance test method mainly relies on the laboratory simulation of earthquake environment. By artificially configuring various vibration use cases, the engineering structure model is subjected to pseudo-dynamic tests to obtain its seismic performance data.

[0003] There are many deficiencies in the existing technology. First, the configuration of the vibration test case depends entirely on the user's experience and subjective judgment, lacking scientific basis and systematicity. In addition, the vibration test cases configured by different users may vary greatly, resulting in poor repeatability and consistency of the test results. Second, the user needs to manually adjust the vibration test case based on the results after each test, which is not only time-consuming and labor-intensive, but also inefficient. For engineering structures that require real-time monitoring of seismic performance, this test method of gradually adjusting the test case is difficult to meet the principle of "early detection, early resolution", which may result in the inability to timely and accurately judge the safety of the engineering structure at critical moments.

[0004] In summary, how to quickly and accurately determine the ultimate seismic performance of engineering structures without relying on the user's subjective configuration of vibration use cases to meet the needs of real-time monitoring of the seismic resistance of engineering structures has become an urgent problem to be solved. Summary of the invention

[0005] The present invention aims to solve the technical problem that in the prior art, since the vibration use case is subjectively configured by the user, the user needs to gradually intervene and adjust, which may result in a long time for each seismic performance test and thus low test efficiency. A seismic performance test method and system for engineering structures are provided to solve the problem.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] In a first aspect, the present invention provides a method for testing the seismic performance of an engineering structure, comprising:

[0008] Obtain the deployment area information and three-dimensional model of the target engineering structure;

[0009] Perform vibration use case mining according to the deployment area information to obtain a first vibration use case set;

[0010] Scaling the three-dimensional model of the engineering structure in equal proportion to obtain a three-dimensional model of the experimental engineering structure, wherein the three-dimensional model of the experimental engineering structure has a scaling scale label;

[0011] Scaling the first set of vibration use cases according to the scaling label to obtain a second set of vibration use cases;

[0012] manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, traversing the second set of vibration use cases, performing a pseudo-dynamic test on the test engineering structure, and obtaining a first set of seismic performance test results;

[0013] When the first seismic performance test result set has no limit seismic performance, optimizing the second vibration case set according to the first seismic performance test result set to obtain a third vibration case set to perform a pseudo-dynamic test;

[0014] When the first seismic resistance test result set has the ultimate seismic resistance, the ultimate seismic resistance is output.

[0015] In a second aspect, the present invention provides a seismic performance testing system for an engineering structure, comprising:

[0016] Structural data acquisition module, used to obtain the deployment area information of the target engineering structure and the three-dimensional model of the engineering structure;

[0017] A vibration use case mining module, used to mine vibration use cases according to the deployment area information to obtain a first vibration use case set;

[0018] An engineering structure scaling module, used for scaling the engineering structure three-dimensional model in equal proportion to obtain a test engineering structure three-dimensional model, wherein the test engineering structure three-dimensional model has a scaling scale label;

[0019] A vibration case scaling module, configured to scale the first vibration case set according to the scaling scale label to obtain a second vibration case set;

[0020] a pseudo-dynamic test module, configured to manufacture a test engineering structure according to the three-dimensional model of the test engineering structure, traverse the second set of vibration use cases, perform a pseudo-dynamic test on the test engineering structure, and obtain a first set of seismic performance test results;

[0021] A case optimization module, configured to optimize the second vibration use case set according to the first seismic performance test result set when the first seismic performance test result set has no limit seismic performance, so as to obtain a third vibration use case set and perform a pseudo-dynamic test;

[0022] The test result output module is used to output the ultimate seismic resistance performance when the first seismic resistance performance test result set has the ultimate seismic resistance performance.

[0023] The beneficial effects of the present invention are as follows: first, by mining use cases in the engineering structure deployment region, a first set of vibration use cases that meet regional needs is obtained, thereby avoiding the arbitrariness and uncertainty of the user's subjective configuration of use cases, and improving the scientificity and reliability of the test results. Secondly, case optimization is performed based on the test results of the first set of vibration use cases until the output is measured when the ultimate seismic performance is measured, thereby realizing automatic iterative optimization of use cases without manual step-by-step adjustment, greatly shortening the test cycle, improving test efficiency, and meeting the timeliness requirements of real-time monitoring of the seismic resistance of engineering structures. In addition, the method can also iteratively optimize use cases according to demand, has strong adaptability and flexibility, and is conducive to quickly determining the ultimate seismic performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a flow chart of a method for testing the seismic performance of an engineering structure provided by the present invention;

[0025] Figure 2 A schematic diagram of a flow chart of a pseudo-dynamic test for a method for testing the seismic performance of an engineering structure provided by the present invention;

[0026] Figure 3 A structural schematic diagram of a seismic performance testing system for engineering structures provided by the present invention.

[0027] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0028] Structural data collection module 100, vibration case mining module 200, engineering structure scaling module 300, vibration case scaling module 400, pseudo-dynamic test module 500, case optimization module 600, test result output module 700. DETAILED DESCRIPTION

[0029] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0031] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0032] Embodiment 1:

[0033] like Figure 1 As shown, an embodiment of the present invention provides a method for testing the seismic performance of an engineering structure, comprising the steps of:

[0034] S10: Obtaining deployment area information of the target engineering structure and a three-dimensional model of the engineering structure;

[0035] Specifically, the target engineering structure refers to the engineering structure that needs to be tested for its seismic performance; the deployment area information refers to the specific geographical location of the engineering structure, which can help determine the characteristics and intensity of seismic activity in the area and provide a basis for subsequent vibration use case mining; the three-dimensional model of the engineering structure is a three-dimensional digital model of the engineering structure constructed using technologies such as computer-aided design (CAD), which contains detailed information such as the size, shape, and material properties of the structure. The model is used for subsequent simulation analysis and testing, and can intuitively display the various parts of the structure and their interrelationships, providing a basis for accurately evaluating the seismic performance of the structure.

[0036] In detail, firstly, the deployment area information of the target engineering structure is collected through the geographic information system (GIS) technology, and the detailed geographical location, geological conditions, seismic activity history and other data of the area where the structure is located are obtained. At the same time, the CAD software is used to construct its three-dimensional model according to the design drawings and construction materials of the engineering structure. This step plays a fundamental role in the whole plan, and provides the necessary data and model support for the subsequent steps such as vibration case mining, model scaling and pseudo-dynamic testing. Accurate regional information helps to more accurately mine vibration cases that meet the actual seismic environment, and the detailed three-dimensional model is the prerequisite for subsequent simulation tests and performance analysis, ensuring the scientificity and accuracy of the whole test method.

[0037] S20: performing vibration use case mining according to the deployment area information to obtain a first vibration use case set;

[0038] Specifically, vibration use case mining refers to the process of screening out earthquake events associated with the target engineering structure deployment area from a large amount of seismic data, and extracting its characteristic parameters such as seismic waveform, acceleration, frequency, and fluctuation amplitude; the first vibration use case set is a set of vibration use cases customized for the deployment area information obtained through the vibration use case mining process, which contains the typical characteristics of seismic activity in the area. Preferably, any vibration use case includes at least vibration feature time series information, and the vibration features at any moment include vibration acceleration, vibration velocity and vibration displacement. When storing specifically, the vibration features at any moment construct a three-dimensional coordinate, and the specific coordinate values ​​of the three-dimensional coordinates are the first weight*vibration acceleration, the second weight*vibration velocity and the third weight*vibration displacement, respectively. The first weight, the second weight and the third weight are user preset parameters, respectively, which characterize the importance of the three features and can be used in subsequent pseudo-dynamic tests to evaluate the seismic performance of engineering structures under actual earthquakes.

[0039] In detail, this step extracts a series of vibration feature record time series information by analyzing the earthquake monitoring record data in the deployment area, and then aggregates and analyzes the time series information and fuses them to finally obtain the first vibration use case set.

[0040] The specific process includes: extracting multiple groups of vibration feature record time series information from the earthquake monitoring record data, performing cluster analysis on the time series information, classifying similar time series information into the same cluster, and then fusing the time series information in each cluster to obtain vibration feature fused time series information representing the earthquake characteristics of the cluster, and using these fused time series information as vibration use cases to form a first vibration use case set.

[0041] Vibration case mining provides scientific and reasonable vibration cases for subsequent pseudo-dynamic tests, ensuring that the test results can truly reflect the seismic performance of engineering structures in actual earthquake environments, avoiding the arbitrariness and uncertainty of user-subjective configuration of cases in traditional methods, and improving the scientificity and reliability of test results.

[0042] S30: scaling the three-dimensional model of the engineering structure in equal proportion to obtain a three-dimensional model of the experimental engineering structure, wherein the three-dimensional model of the experimental engineering structure has a scaling scale label;

[0043] Specifically, scaling refers to the process of reducing all dimensions of the three-dimensional model of the engineering structure by the same scale factor to adapt to the limitations of laboratory test space and equipment, while keeping the geometric shape and relative size relationship of the structure unchanged, ensuring that the scaled model has similar mechanical properties to the original structure; the three-dimensional model of the experimental engineering structure is a three-dimensional model of the engineering structure used for laboratory testing after proportional scaling. It has similar geometric features and mechanical properties to the original structure and can be used for subsequent pseudo-dynamic tests; the scaling scale label is the scale factor information marked on the three-dimensional model of the experimental engineering structure, which is used to indicate the proportional relationship between the model size and the actual engineering structure size, so as to facilitate the accurate conversion of the model's response data into the response data of the actual engineering structure when analyzing the test results.

[0044] In detail, first determine the appropriate scaling factor, which is usually determined based on the space and equipment conditions of the laboratory and the purpose of the test. Then use the scaling algorithm in computer graphics to scale all dimensions of the three-dimensional model of the engineering structure in proportion to obtain the three-dimensional model of the experimental engineering structure, and annotate the scaling scale label in the model.

[0045] This step plays a bridging role in the entire plan, converting the three-dimensional model of the actual engineering structure into a test model suitable for laboratory tests, providing a physical basis for subsequent pseudo-dynamic tests. At the same time, by scaling the scale labels, it ensures that the test results can accurately reflect the seismic performance of the actual engineering structure, providing strong support for the safety assessment of the engineering structure.

[0046] S40: scaling the first vibration use case set according to the scaling scale label to obtain a second vibration use case set;

[0047] Specifically, scaling processing refers to the process of adjusting the vibration parameters in the first vibration case set according to the scaling scale label of the three-dimensional model of the test engineering structure, so as to ensure that the vibration effect during the test is consistent with the seismic effect that the actual engineering structure may suffer in terms of mechanical effect; the second vibration case set is a set of vibration case studies suitable for the three-dimensional model of the test engineering structure obtained after scaling processing. The vibration parameters of these cases have been adjusted according to the scaling scale and can be used in subsequent pseudo-dynamic tests.

[0048] In detail, the scaling scale label of the three-dimensional model of the test engineering structure is first read, and then the acceleration, displacement and other parameters of each vibration case in the first vibration case set are scaled according to the scale to obtain the second vibration case set.

[0049] Specifically, if the scale of the model is 1:10, the acceleration and velocity parameters in the vibration test case should remain unchanged, while the displacement parameter should be reduced to 1 / 10 of the original. This step plays an adaptive role in the entire scheme. It ensures that the vibration effect during the test can truly simulate the impact of actual earthquakes on engineering structures, provides a scientific and reasonable vibration test case for subsequent pseudo-dynamic tests, and ensures the accuracy and reliability of the test results.

[0050] S50: manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, traversing the second set of vibration use cases, performing a pseudo-dynamic test on the test engineering structure, and obtaining a first set of seismic performance test results;

[0051] Specifically, manufacturing a test engineering structure refers to producing a physical test engineering structure model based on the specific size, shape, material properties and other information of the three-dimensional model of the test engineering structure, using appropriate manufacturing processes and materials, for subsequent pseudo-dynamic tests to simulate the response behavior of the actual engineering structure under earthquake action; pseudo-dynamic test: is a test method that simulates the structural response under earthquake action, by applying a dynamic load similar to the earthquake waveform to the test engineering structure, measuring the reaction force of the structure to evaluate the seismic performance of the structure; the first set of seismic performance test results is a set of seismic performance data obtained through pseudo-dynamic tests that reflects the seismic performance of the test engineering structure under different vibration use cases, including hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient, ductility coefficient and other indicators.

[0052] In detail, this step first manufactures a test engineering structure based on the three-dimensional model of the test engineering structure, then traverses each vibration use case in the second vibration use case set, and performs pseudo-dynamic tests on the test engineering structure in turn; during the test, a dynamic load corresponding to the vibration use case is applied by a loading device, and sensors and a data acquisition system are used to monitor and record the reaction data of the test engineering structure in real time; after the test, the collected data is analyzed and processed, and various seismic performance indicators are calculated to form a first seismic performance test result set.

[0053] This step plays a core role in the entire plan. Through actual test operations, detailed seismic performance data of the test engineering structure under different earthquakes are obtained, which provides a direct basis for subsequent seismic performance evaluation and optimization, and ensures the accuracy and practicality of the test results.

[0054] S60: when the first seismic performance test result set has no ultimate seismic performance, optimizing the second vibration case set according to the first seismic performance test result set to obtain a third vibration case set to perform a pseudo-dynamic test;

[0055] Specifically, unlimited seismic performance means that in the current seismic performance test results, the engineering structure has not yet demonstrated its maximum bearing capacity or damage limit under earthquake action, that is, the structure has not shown obvious yielding, damage or failure during the test, and its ultimate seismic performance cannot be directly determined; case optimization refers to the adjustment and improvement of vibration use cases based on existing seismic performance test results, so as to more accurately simulate the earthquake conditions that may trigger the ultimate seismic performance of the structure, so as to obtain seismic performance data that is closer to the actual limit state; the third vibration use case set is a new set of vibration use cases obtained after case optimization. These use cases contain adjusted earthquake parameters and are used in subsequent pseudo-dynamic tests to further explore and determine the ultimate seismic performance of the engineering structure.

[0056] In detail, firstly, various indicators in the first set of seismic performance test results are analyzed, such as hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, etc., to identify the gap between the seismic performance of the structure under the current test conditions and the actual limit state; then, based on these analysis results, the seismic parameters in the second vibration use case set are adjusted, such as increasing the seismic acceleration, prolonging the seismic action time or changing the seismic waveform, etc., to simulate stronger seismic actions; by constructing a fitness function and setting corresponding thresholds, the degree of proximity between the seismic performance indicators and the limit state is quantitatively evaluated to guide the optimization direction of the vibration use case, and finally, the third set of vibration use cases is obtained, and the pseudo-dynamic test is performed again.

[0057] This step plays a key iterative optimization role in the entire program. By continuously adjusting and optimizing vibration use cases, it gradually approaches the ultimate seismic performance of engineering structures, improves the comprehensiveness and accuracy of test results, and provides more reliable data support for the safety assessment of engineering structures.

[0058] S70: When the first seismic resistance test result set has ultimate seismic resistance, output ultimate seismic resistance.

[0059] Specifically, having ultimate seismic performance means that in the seismic performance test results, the engineering structure has demonstrated its maximum bearing capacity or damage limit under earthquake action, that is, the structure has shown obvious yielding, damage or failure phenomena during the test, such as plastic deformation, component fracture or overall collapse. The corresponding seismic intensity and structural response data at this time are the embodiment of the ultimate seismic performance; output ultimate seismic performance: refers to the organization, analysis and presentation of various data and indicators reflecting the ultimate seismic performance of the engineering structure obtained during the test, for subsequent safety assessment and engineering decision-making.

[0060] In detail, firstly, the key response data when the structure reaches the limit state, such as maximum displacement, maximum acceleration, maximum strain, etc., and the corresponding seismic action parameters, such as peak acceleration, peak displacement and peak velocity, etc., are identified from the first set of seismic performance test results; then, these data are comprehensively analyzed to calculate the ultimate seismic performance indicators, such as ultimate bearing capacity, ultimate displacement angle, ultimate ductility coefficient, etc.; finally, these ultimate seismic performance data are output in the form of charts, reports, etc., to clearly show the ultimate bearing capacity and damage characteristics of the engineering structure under the action of earthquake.

[0061] This step plays a role in summarizing and presenting the entire plan, providing final and critical performance data for the safety assessment of engineering structures, helping engineers and decision makers understand the performance of structures under extreme seismic conditions, and thus formulate corresponding reinforcement, renovation or reconstruction plans to ensure the safety and reliability of engineering structures.

[0062] Further, vibration use case mining is performed according to the deployment area information to obtain a first vibration use case set. Step S20 includes the following steps:

[0063] S21: extracting earthquake monitoring record data according to the deployment area information, wherein the earthquake monitoring record data includes first vibration characteristic record time sequence information, second vibration characteristic record time sequence information, and up to Nth vibration characteristic record time sequence information;

[0064] S22: performing aggregate analysis on the first vibration feature record timing information, the second vibration feature record timing information, and the Nth vibration feature record timing information to obtain a first cluster of vibration feature record timing information, a second cluster of vibration feature record timing information, and the Mth cluster of vibration feature record timing information;

[0065] S23: traversing the first cluster of vibration feature recording time sequence information, the second cluster of vibration feature recording time sequence information until the Mth cluster of vibration feature recording time sequence information is merged to obtain the first vibration feature fusion time sequence information, the second vibration feature fusion time sequence information until the Mth vibration feature fusion time sequence information;

[0066] S24: adding the first vibration feature fusion timing information, the second vibration feature fusion timing information, and so on until the Mth vibration feature fusion timing information into the first vibration use case set.

[0067] Specifically, earthquake monitoring record data refers to the seismic activity data recorded over a long period of time in the target engineering structure deployment area by earthquake monitoring equipment (such as seismographs, accelerometers, etc.), including the time series information of the characteristic parameters of seismic waves such as acceleration, velocity, displacement, etc. changing over time; vibration characteristic record time series information: refers to the time series formed by recording the changes of a certain characteristic parameter of the seismic wave (acceleration, velocity, displacement) over time in the earthquake monitoring record data, such as the first vibration characteristic record time series information, the second vibration characteristic record time series information, etc. Each time series information represents the change process of a certain characteristic parameter in a earthquake event.

[0068] Aggregation analysis refers to the comprehensive analysis of multiple vibration feature record time series information, and classifies them into several clusters according to the similarity or correlation of earthquake features. Each cluster contains earthquake events with similar characteristics, such as the first cluster of vibration feature record time series information, the second cluster of vibration feature record time series information, etc. Through aggregation analysis, the main characteristics and types of earthquake activities in the region can be identified;

[0069] Fusion refers to the comprehensive processing of multiple vibration feature record time series information within the same cluster, extracting the representative characteristics of the cluster earthquake events, and forming a fused vibration feature time series information, such as the first vibration feature fusion time series information, the second vibration feature fusion time series information, etc. The fused time series information can more accurately reflect the typical characteristics of the cluster earthquake events.

[0070] In detail, first, according to the deployment area information, the earthquake monitoring record data of the area, including multiple vibration feature record time series information, are extracted from the earthquake monitoring database; then, these time series information are aggregated and analyzed, and they are divided into several clusters according to the similarity of earthquake characteristics using clustering algorithms and other methods, and each cluster represents a type or characteristic of earthquake activity in the area; then, the vibration feature record time series information in each cluster is fused to extract the representative characteristics of the earthquake events in the cluster to form fused vibration feature time series information; finally, these fused vibration feature time series information are added as vibration use cases to the first vibration use case set.

[0071] This step plays a role in data mining and feature extraction in the entire plan. Through in-depth analysis and processing of earthquake monitoring data, vibration use cases that meet the earthquake characteristics of the target engineering structure deployment area are discovered, providing a scientific and reasonable earthquake simulation basis for subsequent seismic performance testing, ensuring the accuracy and pertinence of the test results.

[0072] Further, the first vibration feature recording timing information, the second vibration feature recording timing information, and the Nth vibration feature recording timing information are aggregated and analyzed to obtain a first cluster of vibration feature recording timing information, a second cluster of vibration feature recording timing information, and the Mth cluster of vibration feature recording timing information. Step S22 includes the following steps:

[0073] S221: randomly extracting the i-th vibration feature record timing information and the j-th vibration feature record timing information from the first vibration feature record timing information, the second vibration feature record timing information to the N-th vibration feature record timing information, where i≠j, N≥i≥1, and N≥j≥1;

[0074] S222: the i-th vibration characteristic recording time sequence information has a first time sequence length, and the j-th vibration characteristic recording time sequence information has a second time sequence length;

[0075] S223: performing a multi-time alignment distance evaluation on the i-th vibration feature recording time series information and the j-th vibration feature recording time series information according to the first time series length and the second time series length to obtain a vibration similarity coefficient;

[0076] S224: when the vibration similarity coefficient is greater than or equal to the vibration similarity coefficient threshold, adding the i-th vibration feature recording time sequence information and the j-th vibration feature recording time sequence information into the same cluster vibration feature recording time sequence information;

[0077] S225: Otherwise, add the i-th vibration feature recording timing information and the j-th vibration feature recording timing information into the hetero-cluster vibration feature recording timing information.

[0078] Specifically, random extraction refers to randomly selecting two different time series information from a set of vibration characteristic record time series information, where i and j represent the serial numbers of the two time series information respectively, and i is not equal to j, to ensure that the two extracted time series information are different earthquake event data; the first time series length and the second time series length refer to the number of time points contained in the i-th and j-th vibration characteristic record time series information respectively, that is, the time span of each time series information recording the change of earthquake characteristic parameters. Different time series lengths mean that the earthquake duration or sampling frequency recorded by the two time series information may be different; multi-moment alignment distance evaluation is a method for comparing the similarity of two time series information by comparing the different time points of a time series information. The time point of a time series is aligned with the corresponding time point of another time series information, and the distance between them is calculated to evaluate the similarity of the two time series information; the vibration similarity coefficient is a quantitative indicator obtained by multi-moment alignment distance evaluation, which is used to indicate the similarity of the two vibration feature record time series information. The larger the value, the more similar the two time series information are; the vibration similarity coefficient threshold is a preset critical value, which is used to determine whether the two vibration feature record time series information are similar enough to be classified into the same cluster; when the vibration similarity coefficient is greater than or equal to the threshold, it is considered that the two time series information have similar seismic characteristics and should be classified into the same cluster; otherwise, it is considered that their seismic characteristics are quite different and should be classified into different clusters. By gradually performing aggregation analysis on all vibration feature record time series information, the vibration feature record time series information of multiple clusters is finally obtained.

[0079] This step plays a key role in data classification and feature identification in the entire plan. Through scientific similarity evaluation methods, the vibration feature record time series information in the earthquake monitoring data is reasonably classified, laying a solid data foundation for subsequent vibration case mining and seismic performance testing, and ensuring the accuracy and representativeness of the test cases.

[0080] Further, according to the first time sequence length and the second time sequence length, a multi-time alignment distance evaluation is performed on the i-th vibration feature recording time sequence information and the j-th vibration feature recording time sequence information to obtain a vibration similarity coefficient. Step S223 includes the following steps:

[0081] S2231: when the first time sequence length is greater than or equal to the second time sequence length, aligning the first moment of the j-th vibration characteristic recording time sequence information with the first moment, the second moment, and the Q-th moment of the i-th vibration characteristic recording time sequence information, respectively, to obtain a first multi-moment alignment result, wherein the Q-th moment is equal to the moment value of the H-th moment minus the second time sequence length, and the H-th moment represents the end moment of the i-th vibration characteristic recording time sequence information;

[0082] S2232: when the first time sequence length is less than the second time sequence length, aligning the first moment of the i-th vibration characteristic recording time sequence information with the first moment, the second moment and the Y-th moment of the j-th vibration characteristic recording time sequence information respectively, to obtain a second multi-moment alignment result, wherein the Y-th moment is equal to the moment value of the W-th moment minus the first time sequence length, and the W-th moment represents the end moment of the j-th vibration characteristic recording time sequence information;

[0083] S2233: traverse the first multiple-time alignment result or the second multiple-time alignment result, and calculate a distance deviation modulus value sequence set of the alignment moments;

[0084] S2234: traversing the distance deviation modulus value sequence set to calculate the distance deviation modulus mean, and finding the inverse, to obtain an initial vibration similarity coefficient set;

[0085] S2235: Extract the maximum value of the initial vibration similarity coefficient set and set it as the vibration similarity coefficient.

[0086] Specifically, multi-moment alignment refers to aligning two time series information at multiple moments in order to compare their characteristic values ​​at the corresponding moments, such as acceleration, velocity, displacement, etc. The purpose of alignment is to calculate the similarity of the alignment methods of the two time series information at different moments; the distance deviation modulus refers to the absolute value of the difference between the distances of the two time series information at the moment of alignment; the initial vibration similarity coefficient is a quantitative indicator obtained by calculating the mean of the distance deviation modulus and taking the inverse. The smaller the mean, the larger the similarity coefficient, indicating that the two time series information are more similar.

[0087] This step first determines the alignment method according to the length relationship of the two time series information: if the length of the first time series is greater than or equal to the length of the second time series, the first moment of the second time series information is aligned with the first moment, the second moment until the Qth moment of the first time series information, where the Qth moment is equal to the end moment of the first time series information minus the length of the second time series information; conversely, if the length of the first time series is less than the length of the second time series information, the first moment of the first time series information is aligned with the first moment, the second moment until the Yth moment of the second time series information, where the Yth moment is equal to the end moment of the second time series information minus the length of the first time series information; after alignment, the distance deviation modulus of the alignment moment is calculated to form a sequence set; then, this sequence set is traversed, the mean of the distance deviation modulus is calculated, and the inverse is obtained to obtain the initial vibration similarity coefficient set; finally, the maximum value is extracted from the initial vibration similarity coefficient set as the final vibration similarity coefficient.

[0088] This step plays a key role in similarity evaluation in the entire scheme. Through scientific multi-moment alignment and distance deviation modulus calculation methods, the similarity of two time series information is accurately evaluated, providing a reliable similarity indicator for subsequent time series information clustering and vibration case mining, ensuring the accuracy and representativeness of the test cases.

[0089] Further, the first cluster of vibration feature recording time sequence information, the second cluster of vibration feature recording time sequence information, and the Mth cluster of vibration feature recording time sequence information are traversed to fuse the first vibration feature fusion time sequence information, the second vibration feature fusion time sequence information, and the Mth vibration feature fusion time sequence information are obtained. Step S23 includes the following steps:

[0090] S231: Decomposing the first group of vibration characteristic record time series information at a moment to obtain a first moment characteristic record data set, a second moment characteristic record data set, and a Pth moment characteristic record data set;

[0091] S232: traversing the first moment characteristic record data set, the second moment characteristic record data set, until the P-th moment characteristic record data set to remove outliers, and obtaining the first moment characteristic social living record data set, the second moment characteristic social living record data set, until the P-th moment characteristic social living record data set;

[0092] S233: Traversing the first moment characteristic group record data set, the second moment characteristic group record data set until the P-th moment characteristic group record data set to extract the local reachable density maximum value, and obtaining the first vibration feature fusion time series information.

[0093] Specifically, moment decomposition refers to splitting all vibration feature record time series information in a cluster according to time points, extracting the characteristic values ​​of each moment separately, and forming multiple moment feature record data sets, such as the first moment feature record data set, the second moment feature record data set, etc. Each data set contains the characteristic values ​​of all records at that moment; outlier removal refers to identifying and removing those outliers or outliers that are significantly different from most data in each moment feature record data set. Outliers may be caused by measurement errors, data anomalies, etc. Deleting outliers helps to improve the accuracy and reliability of the data; local reachable density maximum value extraction refers to calculating the local reachable density of each data point in the moment feature cluster record data set after outlier removal. The local reachable density is an indicator to measure the density of data points in a local area, and then extracting the local reachable density maximum value, which represents the most typical or representative value of the characteristic value at that moment.

[0094] In detail, this step first decomposes the vibration feature record time series information in each cluster, extracts the feature value of each moment, and forms multiple moment feature record data sets; then, traverses these data sets, deletes outliers, removes abnormal values, and obtains more accurate moment feature group record data sets. Next, traverse these group record data sets, calculate the local reachable density of each data point, and extract the maximum value of the local reachable density, and combine these maximum values ​​in time order to form the vibration feature fusion time series information of the cluster.

[0095] For example, for the first cluster of vibration feature record time series information, after time decomposition, outlier deletion and local reachable density maximum value extraction, the first vibration feature fusion time series information is obtained.

[0096] This step plays a role in data fusion and feature extraction in the entire scheme. By carefully processing and analyzing the vibration feature record time series information in each cluster, the most representative eigenvalues ​​are extracted to form fused vibration feature time series information, which provides a more accurate and reliable earthquake simulation basis for subsequent vibration case mining and seismic performance testing, ensuring the scientificity and effectiveness of the test results.

[0097] Further, such as Figure 2 As shown, a test engineering structure is manufactured according to the three-dimensional model of the test engineering structure, the second vibration use case set is traversed, a pseudo-dynamic test is performed on the test engineering structure, and a first seismic performance test result set is obtained. Step S50 includes the following steps:

[0098] S51: randomly extracting a vibration use case according to the second vibration use case set;

[0099] S52: manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, and performing a pseudo-dynamic test on the test engineering structure according to the vibration characteristic time series information of the vibration test case to obtain reaction time series information of the test engineering structure;

[0100] S53: performing seismic performance mapping on the reaction force time series information of the test engineering structure, obtaining hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient, and setting them as the first seismic performance test result;

[0101] S54: Add the first seismic resistance performance test result to the first seismic resistance performance test result set.

[0102] Specifically, the pseudo-dynamic test is a test method that simulates the structural response under earthquake action. By applying dynamic loads similar to earthquake waveforms, i.e., acceleration, displacement, and velocity of vibration, to the test engineering structure, the reaction force of the structure is measured to evaluate the seismic performance of the structure. The reaction force time series information refers to the information on the reaction force of the test engineering structure generated by the earthquake action during the pseudo-dynamic test, which changes with time, including data such as the magnitude, direction, and point of action of the reaction force.

[0103] Seismic performance mapping refers to converting the reaction time series information of the test engineering structure into various parameters reflecting its seismic performance, including hysteretic energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient. These parameters can quantitatively describe the performance of the structure under earthquake. In one implementation method, various parameters of seismic performance such as hysteretic energy dissipation parameters and inter-story displacement angle can be processed according to existing standard calculation formulas; in another implementation method, the reaction time series information of the engineering structure and the recorded data of various parameters can be collected, and the recorded data of various parameters can be used as supervision to train a feedforward neural network, which is set as a seismic performance mapping model to realize the mapping analysis of various parameters of seismic performance.

[0104] According to the vibration characteristic time series information of the selected vibration use case, a pseudo-dynamic test is performed on the test engineering structure, and the corresponding dynamic load is applied. At the same time, the reaction force time series information of the test engineering structure is monitored and recorded in real time by using sensors and data acquisition systems; after the test, the collected reaction force time series information is processed by seismic performance mapping, and seismic performance indicators such as hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient are calculated to form the first seismic performance test results; finally, these test results are added to the first seismic performance test result set.

[0105] Through actual pseudo-dynamic test operations, detailed seismic performance data of the test engineering structure under different earthquake actions were obtained and converted into standardized performance parameters, providing a direct basis for subsequent seismic performance evaluation and optimization, and ensuring the accuracy and practicality of the test results.

[0106] Furthermore, the seismic performance mapping is performed on the reaction time series information of the test engineering structure to obtain hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient, including:

[0107] Obtaining a seismic performance mapping model, wherein the seismic performance mapping model includes a parallel hysteresis energy dissipation parameter mapping channel, an inter-story displacement angle mapping channel, a strength degradation coefficient mapping channel, a stiffness degradation coefficient mapping channel, and a ductility coefficient mapping channel;

[0108] The reaction time series information of the test engineering structure is input into the seismic performance mapping model, and the hysteretic energy dissipation parameter mapping channel, the inter-story displacement angle mapping channel, the strength degradation coefficient mapping channel, the stiffness degradation coefficient mapping channel and the ductility coefficient mapping channel are activated for processing to obtain the hysteretic energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient.

[0109] Specifically, the seismic performance mapping model is a computational model used to convert the reaction time series information of the test engineering structure into seismic performance parameters. It contains multiple parallel mapping channels, each of which is responsible for calculating a specific seismic performance parameter.

[0110] The hysteretic energy dissipation parameter mapping channel is used to calculate the energy consumed by the structure through hysteretic deformation under the action of earthquake, which reflects the energy dissipation capacity of the structure; the inter-story displacement angle mapping channel: used to calculate the ratio of the inter-story displacement of the structure to the story height, which is an important indicator for evaluating the deformation capacity of the structure; the strength degradation coefficient mapping channel is used to calculate the degree of strength degradation of the structure under the action of earthquake, which reflects the strength retention capacity of the structure; the stiffness degradation coefficient mapping channel is used to calculate the degree of stiffness degradation of the structure under the action of earthquake, which reflects the deformation control ability of the structure; the ductility coefficient mapping channel is used to calculate the ratio of the maximum displacement that the structure can withstand under the action of earthquake to the yield displacement, which reflects the ductility performance of the structure.

[0111] First, a seismic performance mapping model is constructed or obtained, which includes multiple parallel mapping channels, each channel is for a seismic performance parameter, then, the reaction time series information of the test engineering structure is input into the model, and each mapping channel is activated for processing. Specifically, the hysteretic energy dissipation parameter mapping channel will calculate the hysteretic energy dissipation parameter of the structure under earthquake action according to the reaction time series information, the inter-story displacement angle mapping channel will calculate the inter-story displacement angle, the strength degradation coefficient mapping channel will calculate the strength degradation coefficient, the stiffness degradation coefficient mapping channel will calculate the stiffness degradation coefficient, and the ductility coefficient mapping channel will calculate the ductility coefficient; through the coordinated work of these mapping channels, comprehensive seismic performance parameters are finally obtained. Preferably, any mapping channel can be generated by collecting the reaction time series information of the engineering structure and the parameter recording data, and training the feedforward neural network with the parameter recording data as supervision.

[0112] By converting the reaction force time series information obtained from the test into intuitive seismic performance parameters, it provides a quantitative basis for subsequent seismic performance analysis and structural safety assessment, ensuring the accuracy and comprehensiveness of the assessment results.

[0113] Further, when the first seismic performance test result set has no limit seismic performance, the second vibration case set is optimized according to the first seismic performance test result set to obtain a third vibration case set to perform a pseudo-dynamic test, and step S60 includes the following steps:

[0114] S61: obtaining a hysteresis energy dissipation parameter threshold, an inter-story displacement angle threshold, a strength degradation coefficient threshold, a stiffness degradation coefficient threshold, and a ductility coefficient threshold;

[0115] S62: construct an initial fitness function, wherein the initial fitness function is a minimum fitness function:

[0116] ,

[0117] ,

[0118] in, Characterize the initial fitness function, Characterize the zth seismic performance test results, The hysteresis energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient are characterized in sequence, It represents the hysteresis energy dissipation parameter threshold, inter-story displacement angle threshold, strength degradation coefficient threshold, stiffness degradation coefficient threshold and ductility coefficient threshold. e represents the natural constant. The weights of the hysteresis energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient are sequentially characterized;

[0119] S63: When When both are satisfied, the fitness function takes the initial fitness function;

[0120] S64: When When is satisfied, delete the first item of the initial fitness function and obtain the fitness function. When is satisfied, delete the second term of the initial fitness function and obtain the fitness function. When is satisfied, delete the third term of the initial fitness function and obtain the fitness function. When is satisfied, delete the fourth item of the initial fitness function and obtain the fitness function. When is satisfied, delete the fifth item of the initial fitness function to obtain the fitness function, where Can be triggered simultaneously;

[0121] S65: When When triggered simultaneously, it is considered that the ultimate seismic resistance performance is met;

[0122] S66: Based on the fitness function and according to the first seismic performance test result set, the second vibration case set is optimized to obtain a third vibration case set for executing a pseudo-dynamic test.

[0123] Specifically, the hysteresis energy dissipation parameter threshold, the inter-story displacement angle threshold, the strength degradation coefficient threshold, the stiffness degradation coefficient threshold and the ductility coefficient threshold represent the thresholds of the seismic performance parameter limits; and the fitness initial function is constructed, wherein the fitness initial function is the minimum fitness function (the smaller the function value, the better the use case): , ,when When both are satisfied, the fitness function takes the initial fitness function; when When is satisfied, delete the first item of the initial fitness function and obtain the fitness function. When is satisfied, delete the second term of the initial fitness function and obtain the fitness function. When is satisfied, delete the third term of the initial fitness function and obtain the fitness function. When is satisfied, delete the fourth item of the initial fitness function and obtain the fitness function. When is satisfied, delete the fifth item of the initial fitness function to obtain the fitness function, where Can be triggered simultaneously; when When triggered at the same time, it is considered to meet the ultimate seismic performance, and the corresponding use case can be directly output, which is also the convergence condition of the test; based on the fitness function, according to the first seismic performance test result set, the second vibration use case set is optimized, and the third vibration use case set is obtained to perform pseudo-dynamic tests until a use case that meets the ultimate seismic performance is obtained. By constructing a fitness function to guide the rapid optimization of vibration use cases, it is helpful to quickly obtain the required use cases.

[0124] Further, based on the fitness function, according to the first seismic performance test result set, the second vibration case set is optimized to obtain a third vibration case set to perform a pseudo-dynamic test, and step S66 includes the following steps:

[0125] S661: Based on the fitness function, traverse the first seismic performance test result set to perform evaluation and obtain a fitness evaluation value set;

[0126] S662: sorting the second vibration use case set from small to large according to the fitness evaluation value set to obtain a sorting result of the second vibration use case set;

[0127] S663: taking the first number of head use cases of the sorted result of the second vibration use case set as migration targets, and taking the second number of tail use cases of the sorted result of the second vibration use case set as migration objects;

[0128] S664: Based on the migration target, the vibration similarity coefficient of the migration object is increased and adjusted to obtain the third vibration use case set to perform a pseudo-dynamic test.

[0129] Specifically, the fitness evaluation value set refers to a set of values ​​obtained by evaluating each test result in the first seismic performance test result set through the fitness function, and each value represents the degree of gap between the corresponding test result and the ultimate seismic performance; according to the value size in the fitness evaluation value set, the vibration case studies in the second vibration case study set are rearranged, and the case studies with smaller fitness evaluation values ​​are placed in the front, indicating that the test results corresponding to these case studies are closer to the ultimate seismic performance; the migration target and migration object refer to selecting a part of the case studies with better performance as the migration targets and selecting a part of the case studies with worse performance as the migration objects in the sorted second vibration case study set. The purpose is to adjust the parameters of the migration object to make it closer to the migration target, thereby optimizing the entire vibration use case set; the vibration similarity coefficient increase adjustment refers to adjusting the vibration parameters of the migration object to increase its similarity with the migration target. Specifically, the vibration acceleration, displacement and velocity parameters of the migration object can be appropriately adjusted to the vibration acceleration, displacement and velocity parameters of the migration object according to a preset step size. This is because the migration object is closer to the ultimate seismic resistance performance. Through adjustment, the probability of the third vibration use case set being close to the ultimate seismic resistance performance is increased, reducing the optimization time cost; if the third vibration use case set has a use case that meets the ultimate seismic resistance performance, it is output, otherwise the loop is executed.

[0130] Through scientific fitness evaluation and parameter adjustment methods, the set of vibration use cases is continuously optimized to more comprehensively cover possible earthquake conditions, thereby improving the accuracy and reliability of test results and providing more comprehensive data support for the safety assessment of engineering structures.

[0131] The embodiment of the present invention provides a method for testing the seismic performance of an engineering structure, which has at least the following technical effects:

[0132] First, use case mining is performed through the deployment area of ​​the engineering structure to obtain the first set of vibration use cases that meet the regional needs, avoiding the arbitrariness and uncertainty of the user's subjective configuration of the use case, and improving the scientificity and reliability of the test results. Secondly, the case optimization is performed according to the test results of the first set of vibration use cases until the output is measured when the ultimate seismic performance is measured, realizing the automatic iterative optimization of the use case without manual step-by-step adjustment, greatly shortening the test cycle, improving the test efficiency, and meeting the timeliness requirements of real-time monitoring of the seismic resistance of the engineering structure. In addition, this method can also iteratively optimize the use case according to the needs, with strong adaptability and flexibility, which is conducive to the rapid determination of the ultimate seismic performance.

[0133] Embodiment 2:

[0134] like Figure 3 As shown, based on the same inventive concept as the method for testing the seismic performance of an engineering structure provided in Embodiment 1, an embodiment of the present invention further provides a system for testing the seismic performance of an engineering structure, comprising:

[0135] The structural data acquisition module 100 is used to obtain the deployment area information of the target engineering structure and the three-dimensional model of the engineering structure;

[0136] A vibration use case mining module 200 is used to perform vibration use case mining according to the deployment area information to obtain a first vibration use case set;

[0137] The engineering structure scaling module 300 is used to scale the engineering structure three-dimensional model in equal proportion to obtain a test engineering structure three-dimensional model, wherein the test engineering structure three-dimensional model has a scaling scale label;

[0138] A vibration case scaling module 400 is used to scale the first vibration case set according to the scaling scale label to obtain a second vibration case set;

[0139] A pseudo-dynamic test module 500 is used to manufacture a test engineering structure according to the three-dimensional model of the test engineering structure, traverse the second set of vibration use cases, perform a pseudo-dynamic test on the test engineering structure, and obtain a first set of seismic performance test results;

[0140] A case optimization module 600 is used to optimize the second vibration use case set according to the first seismic performance test result set when the first seismic performance test result set has no limit seismic performance, so as to obtain a third vibration use case set and perform a pseudo-dynamic test;

[0141] The test result output module 700 is used to output the ultimate seismic resistance performance when the first seismic resistance performance test result set has the ultimate seismic resistance performance.

[0142] Furthermore, the vibration use case mining module 200 executes the following steps:

[0143] Extracting earthquake monitoring record data according to the deployment area information, wherein the earthquake monitoring record data includes first vibration characteristic record time sequence information, second vibration characteristic record time sequence information, and up to Nth vibration characteristic record time sequence information;

[0144] Performing aggregate analysis on the first vibration feature record timing information, the second vibration feature record timing information, and up to the Nth vibration feature record timing information to obtain a first cluster of vibration feature record timing information, a second cluster of vibration feature record timing information, and up to the Mth cluster of vibration feature record timing information;

[0145] Traversing the first cluster of vibration feature recording time sequence information, the second cluster of vibration feature recording time sequence information until the Mth cluster of vibration feature recording time sequence information is merged to obtain the first vibration feature fusion time sequence information, the second vibration feature fusion time sequence information until the Mth vibration feature fusion time sequence information;

[0146] The first vibration feature fusion timing information, the second vibration feature fusion timing information, and so on until the Mth vibration feature fusion timing information are added into the first vibration use case set.

[0147] Furthermore, the vibration use case mining module 200 executes the following steps:

[0148] Randomly extracting the i-th vibration feature record timing information and the j-th vibration feature record timing information from the first vibration feature record timing information, the second vibration feature record timing information to the N-th vibration feature record timing information, wherein i≠j, N≥i≥1, N≥j≥1;

[0149] The i-th vibration characteristic recording time sequence information has a first time sequence length, and the j-th vibration characteristic recording time sequence information has a second time sequence length;

[0150] According to the first time series length and the second time series length, performing a multi-time alignment distance evaluation on the i-th vibration feature recording time series information and the j-th vibration feature recording time series information to obtain a vibration similarity coefficient;

[0151] When the vibration similarity coefficient is greater than or equal to a vibration similarity coefficient threshold, adding the i-th vibration feature recording time sequence information and the j-th vibration feature recording time sequence information into the same cluster vibration feature recording time sequence information;

[0152] Otherwise, the i-th vibration feature recording timing information and the j-th vibration feature recording timing information are added into the hetero-cluster vibration feature recording timing information.

[0153] Furthermore, the vibration use case mining module 200 executes the following steps:

[0154] When the first time sequence length is greater than or equal to the second time sequence length, aligning the first moment of the j-th vibration characteristic recording time sequence information with the first moment, the second moment, and the Q-th moment of the i-th vibration characteristic recording time sequence information, respectively, to obtain a first multi-moment alignment result, wherein the Q-th moment is equal to the moment value of the H-th moment minus the second time sequence length, and the H-th moment represents the end moment of the i-th vibration characteristic recording time sequence information;

[0155] When the first time sequence length is less than the second time sequence length, the first moment of the i-th vibration characteristic recording time sequence information is respectively aligned with the first moment, the second moment and the Y-th moment of the j-th vibration characteristic recording time sequence information to obtain a second multi-moment alignment result, wherein the Y-th moment is equal to the moment value of the W-th moment minus the first time sequence length, and the W-th moment represents the end moment of the j-th vibration characteristic recording time sequence information;

[0156] Traversing the first multiple-time alignment result or the second multiple-time alignment result, and calculating a distance deviation modulus value sequence set of the alignment moments;

[0157] Traversing the distance deviation modulus value sequence set to calculate the distance deviation modulus mean, and finding the inverse, to obtain an initial vibration similarity coefficient set;

[0158] The maximum value of the initial vibration similarity coefficient set is extracted and set as the vibration similarity coefficient.

[0159] Furthermore, the vibration use case mining module 200 executes the following steps:

[0160] Decomposing the first cluster of vibration characteristic record time series information at a moment to obtain a first moment characteristic record data set, a second moment characteristic record data set, and a Pth moment characteristic record data set;

[0161] Traversing the first moment characteristic record data set, the second moment characteristic record data set, until the P-th moment characteristic record data set to delete outliers, to obtain the first moment characteristic group record data set, the second moment characteristic group record data set, until the P-th moment characteristic group record data set;

[0162] The first moment characteristic group record data set, the second moment characteristic group record data set, and the Pth moment characteristic group record data set are traversed to extract the local reachable density maximum value, and the first vibration feature fusion time series information is obtained.

[0163] Furthermore, the pseudo-dynamic test module 500 performs the following steps:

[0164] According to the second set of vibration use cases, randomly extracting vibration use cases;

[0165] Manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, performing a pseudo-dynamic test on the test engineering structure according to the vibration characteristic time series information of the vibration use case, and obtaining the reaction time series information of the test engineering structure;

[0166] Performing seismic performance mapping on the reaction time series information of the test engineering structure to obtain hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient, which are set as the first seismic performance test result;

[0167] The first seismic resistance performance test result is added to the first seismic resistance performance test result set.

[0168] Furthermore, the pseudo-dynamic test module 500 performs the following steps:

[0169] Obtaining a seismic performance mapping model, wherein the seismic performance mapping model includes a parallel hysteresis energy dissipation parameter mapping channel, an inter-story displacement angle mapping channel, a strength degradation coefficient mapping channel, a stiffness degradation coefficient mapping channel, and a ductility coefficient mapping channel;

[0170] The reaction time series information of the test engineering structure is input into the seismic performance mapping model, and the hysteretic energy dissipation parameter mapping channel, the inter-story displacement angle mapping channel, the strength degradation coefficient mapping channel, the stiffness degradation coefficient mapping channel and the ductility coefficient mapping channel are activated for processing to obtain the hysteretic energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient.

[0171] Furthermore, the case optimization module 600 performs the following steps:

[0172] Obtaining the hysteresis energy dissipation parameter threshold, inter-story displacement angle threshold, strength degradation coefficient threshold, stiffness degradation coefficient threshold and ductility coefficient threshold;

[0173] Construct an initial fitness function, wherein the initial fitness function is a minimum fitness function:

[0174] ,

[0175] ,

[0176] in, Characterize the initial fitness function, Characterize the zth seismic performance test results, The hysteresis energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient are characterized in sequence, It represents the hysteresis energy dissipation parameter threshold, inter-story displacement angle threshold, strength degradation coefficient threshold, stiffness degradation coefficient threshold and ductility coefficient threshold. e represents the natural constant. The weights of the hysteresis energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient are sequentially characterized;

[0177] when When both are satisfied, the fitness function takes the initial fitness function;

[0178] when When is satisfied, delete the first item of the initial fitness function and obtain the fitness function. When is satisfied, delete the second term of the initial fitness function and obtain the fitness function. When is satisfied, delete the third term of the initial fitness function and obtain the fitness function. When is satisfied, delete the fourth item of the initial fitness function and obtain the fitness function. When is satisfied, delete the fifth item of the initial fitness function to obtain the fitness function, where Can be triggered simultaneously;

[0179] when When triggered simultaneously, it is considered that the ultimate seismic resistance performance is met;

[0180] Based on the fitness function, according to the first seismic performance test result set, the second vibration case set is optimized to obtain a third vibration case set for executing a pseudo-dynamic test.

[0181] Furthermore, the case optimization module 600 performs the following steps:

[0182] Based on the fitness function, traverse the first set of seismic performance test results to perform evaluation and obtain a set of fitness evaluation values;

[0183] Sorting the second vibration use case set from small to large according to the fitness evaluation value set to obtain a sorting result of the second vibration use case set;

[0184] The first number of head cases of the sorted result of the second set of vibration cases are used as migration targets, and the second number of tail cases of the sorted result of the second set of vibration cases are used as migration objects;

[0185] Based on the migration target, the vibration similarity coefficient of the migration object is increased and adjusted to obtain the third vibration use case set to perform a pseudo-dynamic test.

[0186] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0189] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0191] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0192] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for testing the seismic performance of an engineering structure, characterized in that: include: Obtain the deployment area information and three-dimensional model of the target engineering structure; Perform vibration use case mining according to the deployment area information to obtain a first vibration use case set; Scaling the three-dimensional model of the engineering structure in equal proportion to obtain a three-dimensional model of the experimental engineering structure, wherein the three-dimensional model of the experimental engineering structure has a scaling scale label; Scaling the first set of vibration use cases according to the scaling label to obtain a second set of vibration use cases; manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, traversing the second set of vibration use cases, performing a pseudo-dynamic test on the test engineering structure, and obtaining a first set of seismic performance test results; When the first seismic performance test result set has no limit seismic performance, optimizing the second vibration case set according to the first seismic performance test result set to obtain a third vibration case set to perform a pseudo-dynamic test; When the first seismic resistance test result set has an extreme seismic resistance, outputting an extreme seismic resistance; Wherein, when the first seismic performance test result set has no ultimate seismic performance, case optimization is performed on the second vibration case set according to the first seismic performance test result set to obtain a third vibration case set for performing a pseudo-dynamic test, including: Obtaining the hysteresis energy dissipation parameter threshold, inter-story displacement angle threshold, strength degradation coefficient threshold, stiffness degradation coefficient threshold and ductility coefficient threshold; Construct an initial fitness function, wherein the initial fitness function is a minimum fitness function: ， ， in, Characterize the initial fitness function, Characterize the zth seismic performance test results, The hysteresis energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient are characterized in sequence, It represents the hysteresis energy dissipation parameter threshold, inter-story displacement angle threshold, strength degradation coefficient threshold, stiffness degradation coefficient threshold and ductility coefficient threshold. e represents the natural constant. The weights of the hysteresis energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient are sequentially characterized; when When both are satisfied, the fitness function takes the initial fitness function; when When is satisfied, delete the first item of the initial fitness function and obtain the fitness function. When is satisfied, delete the second term of the initial fitness function and obtain the fitness function. When is satisfied, delete the third term of the initial fitness function and obtain the fitness function. When is satisfied, delete the fourth item of the initial fitness function and obtain the fitness function. When is satisfied, delete the fifth item of the initial fitness function to obtain the fitness function, where Can be triggered simultaneously; when When triggered simultaneously, it is considered to meet the ultimate seismic resistance performance; Based on the fitness function, according to the first seismic performance test result set, the second vibration case set is optimized to obtain a third vibration case set for executing a pseudo-dynamic test.

2. The method according to claim 1, characterized in that Performing vibration use case mining according to the deployment area information to obtain a first vibration use case set includes: Extracting earthquake monitoring record data according to the deployment area information, wherein the earthquake monitoring record data includes first vibration characteristic record time sequence information, second vibration characteristic record time sequence information, and up to Nth vibration characteristic record time sequence information; Performing aggregate analysis on the first vibration feature record timing information, the second vibration feature record timing information, and up to the Nth vibration feature record timing information to obtain a first cluster of vibration feature record timing information, a second cluster of vibration feature record timing information, and up to the Mth cluster of vibration feature record timing information; Traversing the first cluster of vibration feature recording time sequence information, the second cluster of vibration feature recording time sequence information until the Mth cluster of vibration feature recording time sequence information is merged to obtain the first vibration feature fusion time sequence information, the second vibration feature fusion time sequence information until the Mth vibration feature fusion time sequence information; The first vibration feature fusion timing information, the second vibration feature fusion timing information, and so on until the Mth vibration feature fusion timing information are added into the first vibration use case set.

3. The method according to claim 2, characterized in that Aggregate analysis is performed on the first vibration feature record timing information, the second vibration feature record timing information, and up to the Nth vibration feature record timing information to obtain a first cluster of vibration feature record timing information, a second cluster of vibration feature record timing information, and up to the Mth cluster of vibration feature record timing information, including: Randomly extracting the i-th vibration feature record timing information and the j-th vibration feature record timing information from the first vibration feature record timing information, the second vibration feature record timing information to the N-th vibration feature record timing information, wherein i≠j, N≥i≥1, N≥j≥1; The i-th vibration characteristic recording time sequence information has a first time sequence length, and the j-th vibration characteristic recording time sequence information has a second time sequence length; According to the first time series length and the second time series length, performing a multi-time alignment distance evaluation on the i-th vibration feature recording time series information and the j-th vibration feature recording time series information to obtain a vibration similarity coefficient; When the vibration similarity coefficient is greater than or equal to a vibration similarity coefficient threshold, adding the i-th vibration feature recording time sequence information and the j-th vibration feature recording time sequence information into the same cluster vibration feature recording time sequence information; Otherwise, the i-th vibration feature recording timing information and the j-th vibration feature recording timing information are added into the hetero-cluster vibration feature recording timing information.

4. The method according to claim 3, characterized in that According to the first time sequence length and the second time sequence length, a multi-time alignment distance evaluation is performed on the i-th vibration feature recording time sequence information and the j-th vibration feature recording time sequence information to obtain a vibration similarity coefficient, including: When the first time sequence length is greater than or equal to the second time sequence length, aligning the first moment of the j-th vibration characteristic recording time sequence information with the first moment, the second moment, and the Q-th moment of the i-th vibration characteristic recording time sequence information, respectively, to obtain a first multi-moment alignment result, wherein the Q-th moment is equal to the moment value of the H-th moment minus the second time sequence length, and the H-th moment represents the end moment of the i-th vibration characteristic recording time sequence information; When the first time sequence length is less than the second time sequence length, the first moment of the i-th vibration characteristic recording time sequence information is respectively aligned with the first moment, the second moment and the Y-th moment of the j-th vibration characteristic recording time sequence information to obtain a second multi-moment alignment result, wherein the Y-th moment is equal to the moment value of the W-th moment minus the first time sequence length, and the W-th moment represents the end moment of the j-th vibration characteristic recording time sequence information; Traversing the first multiple-time alignment result or the second multiple-time alignment result, and calculating a distance deviation modulus value sequence set of the alignment moments; Traversing the distance deviation modulus value sequence set to calculate the distance deviation modulus mean, and finding the inverse, to obtain an initial vibration similarity coefficient set; The maximum value of the initial vibration similarity coefficient set is extracted and set as the vibration similarity coefficient.

5. The method according to claim 2, characterized in that Traversing the first cluster of vibration feature recording time sequence information, the second cluster of vibration feature recording time sequence information until the Mth cluster of vibration feature recording time sequence information is merged to obtain the first vibration feature fusion time sequence information, the second vibration feature fusion time sequence information until the Mth vibration feature fusion time sequence information, including: Decomposing the first cluster of vibration characteristic record time series information at a moment to obtain a first moment characteristic record data set, a second moment characteristic record data set, and a Pth moment characteristic record data set; Traversing the first moment characteristic record data set, the second moment characteristic record data set, until the P-th moment characteristic record data set to delete outliers, to obtain the first moment characteristic group record data set, the second moment characteristic group record data set, until the P-th moment characteristic group record data set; The first moment characteristic group record data set, the second moment characteristic group record data set, and the Pth moment characteristic group record data set are traversed to extract the local reachable density maximum value, and the first vibration feature fusion time series information is obtained.

6. The method according to claim 1, characterized in that Manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, traversing the second set of vibration use cases, performing a pseudo-dynamic test on the test engineering structure, and obtaining a first set of seismic performance test results, including: According to the second set of vibration use cases, randomly extracting vibration use cases; Manufacturing a test engineering structure according to the three-dimensional model of the test engineering structure, performing a pseudo-dynamic test on the test engineering structure according to the vibration characteristic time series information of the vibration use case, and obtaining the reaction time series information of the test engineering structure; Performing seismic performance mapping on the reaction time series information of the test engineering structure to obtain hysteresis energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient, which are set as the first seismic performance test result; The first seismic resistance performance test result is added to the first seismic resistance performance test result set.

7. The method according to claim 6, characterized in that The seismic performance mapping is performed on the reaction time series information of the test engineering structure to obtain the hysteretic energy dissipation parameters, inter-story displacement angle, strength degradation coefficient, stiffness degradation coefficient and ductility coefficient, including: Obtaining a seismic performance mapping model, wherein the seismic performance mapping model includes a parallel hysteresis energy dissipation parameter mapping channel, an inter-story displacement angle mapping channel, a strength degradation coefficient mapping channel, a stiffness degradation coefficient mapping channel, and a ductility coefficient mapping channel; The reaction time series information of the test engineering structure is input into the seismic performance mapping model, and the hysteretic energy dissipation parameter mapping channel, the inter-story displacement angle mapping channel, the strength degradation coefficient mapping channel, the stiffness degradation coefficient mapping channel and the ductility coefficient mapping channel are activated for processing to obtain the hysteretic energy dissipation parameter, the inter-story displacement angle, the strength degradation coefficient, the stiffness degradation coefficient and the ductility coefficient.

8. The method according to claim 1, characterized in that Based on the fitness function, according to the first set of seismic performance test results, case optimization is performed on the second set of vibration use cases to obtain a third set of vibration use cases for performing a pseudo-dynamic test, including: Based on the fitness function, traverse the first set of seismic performance test results to perform evaluation and obtain a set of fitness evaluation values; Sorting the second vibration use case set from small to large according to the fitness evaluation value set to obtain a sorting result of the second vibration use case set; The first number of head cases of the sorted result of the second set of vibration cases are used as migration targets, and the second number of tail cases of the sorted result of the second set of vibration cases are used as migration objects; Based on the migration target, the vibration similarity coefficient of the migration object is increased and adjusted to obtain the third vibration use case set to perform a pseudo-dynamic test.

9. A seismic performance testing system for engineering structures, characterized in that: Used to implement the method according to any one of claims 1 to 8, comprising: Structural data acquisition module, used to obtain the deployment area information of the target engineering structure and the three-dimensional model of the engineering structure; A vibration use case mining module, used to mine vibration use cases according to the deployment area information to obtain a first vibration use case set; An engineering structure scaling module, used for scaling the engineering structure three-dimensional model in equal proportion to obtain a test engineering structure three-dimensional model, wherein the test engineering structure three-dimensional model has a scaling scale label; A vibration case scaling module, configured to scale the first vibration case set according to the scaling scale label to obtain a second vibration case set; a pseudo-dynamic test module, configured to manufacture a test engineering structure according to the three-dimensional model of the test engineering structure, traverse the second set of vibration use cases, perform a pseudo-dynamic test on the test engineering structure, and obtain a first set of seismic performance test results; A case optimization module, configured to optimize the second vibration use case set according to the first seismic performance test result set when the first seismic performance test result set has no limit seismic performance, so as to obtain a third vibration use case set and perform a pseudo-dynamic test; The test result output module is used to output the ultimate seismic resistance performance when the first seismic resistance performance test result set has the ultimate seismic resistance performance.

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