A method and system for evaluating the seismic effect of high-rise shear wall structures

Abstract

The present invention discloses a method and system for evaluating the seismic effect of high-rise shear wall structures, specifically relating to the technical field of high-rise shear walls. By accurately collecting low-cycle fatigue parameters, a concrete and steel degradation model considering fatigue effects is constructed, which is applied to the seismic analysis of shear wall structures, simulating the influence of main earthquake and multiple aftershock loading on structural performance, drawing a bearing capacity degradation curve through multiple loading results, analyzing the abnormal state of degradation rate, combining the changes in residual stiffness and bearing capacity, judging the risk of abnormal structural stiffness and bearing capacity, and dividing the risk into high, medium and low levels, and proposing appropriate reinforcement or preventive measures for different risk levels, effectively making up for the deficiency of existing seismic evaluation that does not fully consider material fatigue effects, and being able to more accurately predict the damage accumulation of high-rise shear wall structures in frequent aftershocks, thereby reducing the risk of premature structural failure and improving the durability and safety of buildings in earthquakes.

Description

Technical Field

[0001] The present invention relates to the technical field of high-rise shear walls, and in particular to a method and system for evaluating the seismic effect of a high-rise shear wall structure. Background Art

[0002] The seismic performance assessment of high-rise shear wall structures involves analyzing and evaluating the seismic performance of high-rise buildings employing shear wall structures during earthquakes. This assessment focuses on the stability, deformation, and load-bearing capacity of the structure under seismic forces to ensure the building's safety in earthquakes of varying intensities. Shear wall structures are a primary structural system for resisting horizontal seismic forces. By enhancing the rigidity and strength of the walls, they enable the building to more effectively absorb and dissipate seismic energy, reducing deformation and preventing collapse.

[0003] In the actual assessment process, a series of simulation analysis methods, such as time-history analysis, response spectrum analysis, or pushover analysis, are typically used to simulate the structural response under earthquakes. These analyses can determine the deformation pattern of shear walls, the distribution of internal forces, and the stress conditions within the components, thereby assessing their seismic effectiveness. The assessment results can help designers optimize the structural design to meet seismic code requirements, while also providing reliable data support for engineering practice, ensuring the stability and safety of high-rise buildings during earthquakes.

[0004] The existing technology has the following shortcomings:

[0005] Under strong earthquakes, the concrete and steel materials in shear wall structures will be subjected to repeated stress-strain cycles. Especially in the case of long-term or multiple aftershocks, the materials will produce "low-cycle fatigue" phenomenon. This fatigue effect will gradually reduce the strength and ductility of the material, resulting in a significant decrease in the stiffness and bearing capacity of the structure. Moreover, this effect is usually not fully considered in conventional seismic assessments. The analysis usually assumes that the material properties do not degrade with the number of earthquake loadings. Therefore, in actual earthquakes, the structure may fail prematurely due to fatigue damage. Especially in areas with frequent aftershocks, the core structural part of the shear wall may suffer irreversible damage, leading to the risk of unexpected collapse. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for evaluating the seismic effect of a high-rise shear wall structure to address the deficiencies in the background technology.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for evaluating the seismic effect of a high-rise shear wall structure, comprising the following steps:

[0008] S1: Collect and determine the low-cycle fatigue parameters of the material, including cyclic stress-strain curve, yield strength decay rate and ductility degradation rate. Based on the low-cycle fatigue parameters, construct concrete and steel degradation models suitable for seismic analysis;

[0009] S2: Apply the material fatigue degradation model to several components such as shear walls and beams and columns. Set up earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks. Load the seismic waves one by one, record the residual stiffness of the structure after each earthquake loading, and determine the impact of fatigue accumulation on the structure.

[0010] S3: When the fatigue cumulative effect has a high degree of influence on the structure, the bearing capacity degradation curves of shear walls and different components under cyclic loads are drawn through the simulation results of multiple loadings, and the rate of change of the curves is analyzed to determine the abnormal state of the structural degradation rate;

[0011] S4: Comprehensively analyze the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate to assess the risk of abnormal structural stiffness and bearing capacity;

[0012] S5: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken;

[0013] S6: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement plan is designed to enhance the durability of the shear wall structure.

[0014] Preferably, in S2, after analyzing the variation trend of the residual stiffness, an abnormal variation index of the residual stiffness is generated to judge the influence of the fatigue accumulation effect on the structure. The method for obtaining the abnormal variation index of the residual stiffness is:

[0015] After multiple earthquake loadings, the residual stiffness data of multiple sets of key components are obtained, which are recorded as X = x1, x2, ..., x n ], where each x n It is a multidimensional vector containing the residual stiffness information of different components at different times. The input data matrix is: X∈R m×n , where m represents different observation samples, n represents the stiffness characteristics of different components, and the covariance matrix Σ is calculated as follows: Z is the standardized matrix, T is the matrix transpose, solve the eigenvalues ​​and eigenvectors of the covariance matrix Σ, sort the eigenvalues ​​from large to small, select the eigenvectors corresponding to the first k largest eigenvalues ​​to form the matrix W, and project the data into a low-dimensional space through the matrix W to obtain the principal component matrix: Y = Z·W; where Y∈R m×k It is the data matrix after dimensionality reduction. The normal samples in the historical data are used as training data. SVM is used for binary classification training to learn the data distribution pattern under normal and abnormal conditions. The decision function of SVM is: f(y) = sgn(w·y+b); where: y∈R k Represents the sample after dimensionality reduction, w is the weight vector of the classification hyperplane, and b is the bias term; sgn represents the sign function. For the newly observed residual stiffness data xnew, the newly observed data is standardized to obtain znew, and the standardized data is transformed into a low-dimensional space through W to obtain the principal component ynew = znew·W; ynew is input into the trained SVM classifier to determine its status: if f(ynew) = 1, it is determined to be normal; if f(ynew) = -1, it is determined to be abnormal; if it is determined to be abnormal, its distance HK from the hyperplane is calculated as the residual stiffness abnormal change index, and the expression is: HK is the abnormal change index of residual stiffness.

[0016] Preferably, in S3, after analyzing the change trend of the bearing capacity degradation curve, a structural degradation rate drift index is generated to determine the abnormal state of the structural degradation rate. The degradation rate drift index is obtained by:

[0017] The bearing capacity degradation data of the shear wall and beam-column connection points are collected and processed to obtain the degradation rate sequence v = [v1, v2, ..., v t ], where v t Denotes the degradation rate of bearing capacity after the t-th cycle loading; Deterioration rate data: Among them C i is the residual bearing capacity after the i-th loading, and the degradation rate v i Indicates the rate of decline of bearing capacity with the number of loading times; the exponentially weighted moving average is used to smooth the time series data of the degradation rate. The calculation formula is: EWMA t =αv t +(1-α)EWMA t-1 Among them, EWMA t is the exponentially weighted moving average after the tth cycle loading, v t is the actual degradation rate after the t-th cycle loading, α is the smoothing coefficient, and its value range is 0<α≤1; the initial value EWMA0 is set to the value of the first item v1; the degradation rate drift index is used to measure the actual degradation rate v tThe degree of deviation from the smoothed degradation rate is calculated as follows: GH = | v t -EWMA t| ; Where GH is the degradation speed drift index.

[0018] Preferably, in S4, the residual stiffness abnormal change index and the structural degradation rate drift index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the risk value label of abnormal structural stiffness and bearing capacity as the prediction target, and takes minimizing the sum of prediction errors of all risk value labels of abnormal structural stiffness and bearing capacity as the training target. The machine learning model is trained until the sum of prediction errors reaches convergence, and the model training is stopped. The risk value of abnormal structural stiffness and bearing capacity is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0019] Preferably, in S5, based on the evaluation results, the risk of abnormalities in the structural stiffness and bearing capacity is divided into different levels, namely, a high risk level, a medium risk level, and a low risk level, specifically:

[0020] Comparing the obtained risk values ​​of abnormal structural stiffness and bearing capacity with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and comparing the risk values ​​of abnormal structural stiffness and bearing capacity with the first standard threshold and the second standard threshold respectively;

[0021] If the risk value of abnormality in structural stiffness and bearing capacity is greater than the second standard threshold, it means that the risk of abnormality in structural stiffness and bearing capacity is high, and a high risk signal is generated, and it is classified as a high risk level;

[0022] If the risk value of abnormal structural stiffness and bearing capacity is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the risk of abnormal structural stiffness and bearing capacity is medium, and a medium risk signal is generated and classified as a medium risk level;

[0023] If the risk value of abnormality in structural stiffness and bearing capacity is less than the first standard threshold, it means that the risk of abnormality in structural stiffness and bearing capacity is low. At this time, a low risk signal is generated and it is classified as a low risk level.

[0024] Preferably, in S6, when the risk of abnormality of the structural stiffness and bearing capacity is at a medium risk level, further analysis is performed on the risk of abnormality of the structural stiffness and bearing capacity within a fixed time period, specifically:

[0025] When the risk of abnormalities in structural stiffness and bearing capacity is at a medium risk level, that is, the risk values ​​of abnormalities in structural stiffness and bearing capacity generated within a fixed time period are greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk values ​​generated within subsequent fixed time periods that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a data set is established. The mean and standard deviation of the data set are calculated, and after analysis, a reinforcement plan is designed based on the analysis results to enhance the durability of the shear wall structure.

[0026] Preferably, if the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, the mean of the abnormal coefficients is high, indicating that the stiffness and bearing capacity of the structure have entered a stable moderate deterioration state; in this case, no early warning signal is generated, and a conventional reinforcement plan should be designed and regularly monitored to ensure that the structure remains stable during long-term use;

[0027] If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, it indicates that the structure is in an unstable deterioration state. At this time, a first-level early warning signal is generated, and a reinforcement plan is designed to strengthen the risk-concentrated areas and increase the monitoring frequency.

[0028] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, it indicates that the overall condition of the structure is good. At this time, a secondary warning signal is generated, and local repair is mainly carried out;

[0029] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, it indicates that the stiffness and bearing capacity of the structure are stable. At this time, a third-level warning signal is generated and no reinforcement measures are required, but normal monitoring should continue.

[0030] The present invention also provides a high-rise shear wall structure seismic effect evaluation system, which includes a degradation model construction module, an earthquake loading simulation module, a degradation rate analysis module, a comprehensive risk analysis module, a risk classification module, and a reinforcement scheme design module;

[0031] Degradation model construction module: collects and determines the low-cycle fatigue parameters of the material, including cyclic stress-strain curves, yield strength decay rate, and ductility degradation rate. Based on the low-cycle fatigue parameters, concrete and steel degradation models suitable for earthquake analysis are constructed;

[0032] Earthquake Loading Simulation Module: This module applies the material fatigue degradation model to several components such as shear walls and beams and columns. It then sets earthquake loading conditions, including a loading sequence combining a main shock with multiple aftershocks. Seismic waves are loaded one after another, and the residual stiffness of the structure after each earthquake loading is recorded to determine the impact of cumulative fatigue on the structure.

[0033] Deterioration rate analysis module: When the cumulative fatigue effect has a high impact on the structure, the module uses the simulation results of multiple loadings to draw the bearing capacity degradation curves of shear walls and different components under cyclic loads, analyzes the rate of change of the curves, and determines the abnormal state of the structural degradation rate;

[0034] Comprehensive risk analysis module: This module comprehensively analyzes the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate, and assesses the risk of abnormal structural stiffness and bearing capacity.

[0035] Risk grading module: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken;

[0036] Reinforcement scheme design module: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement scheme is designed to enhance the durability of the shear wall structure.

[0037] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0038] 1. The present invention, through a systematic evaluation method and model, can deeply analyze the fatigue cumulative effect of high-rise shear wall structures under earthquakes and multiple aftershocks, effectively making up for the deficiency of traditional seismic assessment that does not fully consider the low-cycle fatigue effect of materials. By collecting the low-cycle fatigue parameters of concrete and steel bars, a fatigue degradation model is established, and the changes in residual stiffness and bearing capacity are analyzed in the loading simulation, the residual stiffness abnormal change index and the degradation rate drift index are generated, and the machine learning model is used to predict the risk of these characteristic vectors, thereby accurately identifying the risk level of structural degradation. By scientifically dividing high, medium and low risk levels and multi-level early warning signals, the present invention can flexibly and accurately respond to different degrees of degradation.

[0039] 2. This invention not only improves the accuracy of seismic assessments but also supports the design of targeted reinforcement measures. By comprehensively analyzing fatigue accumulation and degradation rates, structures with medium-risk levels are further monitored. Combined with mean and standard deviation analysis, this method provides data support for the design of appropriate reinforcement solutions. Ultimately, this method can dynamically monitor the durability and stability of shear wall structures and conduct preventive maintenance in areas prone to aftershocks, reducing the risk of premature structural failure and collapse and significantly improving the seismic safety and service life of buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1 Flow chart of the method of the present invention.

[0042] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] Example 1, please refer to Figure 1 and Figure 2 As shown, the method for evaluating the seismic effect of a high-rise shear wall structure described in this embodiment includes the following steps:

[0045] S1: Collect and determine the low-cycle fatigue parameters of the material, including cyclic stress-strain curve, yield strength decay rate and ductility degradation rate. Based on the low-cycle fatigue parameters, construct concrete and steel degradation models suitable for seismic analysis;

[0046] S2: Apply the material fatigue degradation model to several components such as shear walls and beams and columns. Set up earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks. Load the seismic waves one by one, record the residual stiffness of the structure after each earthquake loading, and determine the impact of fatigue accumulation on the structure.

[0047] S3: When the fatigue cumulative effect has a high degree of influence on the structure, the bearing capacity degradation curves of shear walls and different components under cyclic loads are drawn through the simulation results of multiple loadings, and the rate of change of the curves is analyzed to determine the abnormal state of the structural degradation rate;

[0048] S4: Comprehensively analyze the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate to assess the risk of abnormal structural stiffness and bearing capacity;

[0049] S5: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken;

[0050] S6: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement plan is designed to enhance the durability of the shear wall structure.

[0051] In S1, the low-cycle fatigue parameters of the material are collected and determined. The low-cycle fatigue parameters include the cyclic stress-strain curve, yield strength decay rate, and ductility degradation rate. Based on the low-cycle fatigue parameters, a concrete and steel degradation model suitable for seismic analysis is constructed. Specifically:

[0052] The cyclic stress-strain curve describes the relationship between stress and strain of a material under repeated loading and reveals its hysteresis characteristics and energy dissipation capacity. This curve is the core input of the fatigue degradation model. The steps to obtain it are as follows:

[0053] Material testing: Repeated loading tests are performed on concrete and steel specimens, usually using a quasi-static loading method and applying different stress amplitudes to simulate the cyclic effects of seismic loads.

[0054] Testing process: Each specimen is repeatedly loaded to a certain strain level and unloaded, gradually increasing the strain level until failure, while recording the stress and strain at each cycle.

[0055] Hysteresis curve: The stress-strain hysteresis curve is drawn in each cycle to reveal the plastic deformation and nonlinear behavior of the specimen.

[0056] Energy dissipation: Calculate the area of ​​each hysteresis loop to characterize the energy dissipation of the material during each cyclic loading, helping to evaluate changes in material ductility.

[0057] The yield strength decay rate indicates the decay rate of the yield strength of a material under low-cycle fatigue and is usually used to evaluate the extent of material strength reduction under repeated seismic loading. The method for obtaining it is as follows:

[0058] Experimental data recording: When each cyclic loading reaches the yield point, record the yield stress value.

[0059] Fitting decay curve: Fit the yield stress value with the number of loading cycles to form a decay curve of yield strength with the number of cycles.

[0060] Calculate the attenuation rate: Determine the yield strength attenuation rate through the slope of the attenuation curve, which characterizes the downward trend of the yield strength under repeated cycles.

[0061] The ductility degradation rate indicates the degree of ductility reduction of the material in low-cycle fatigue cycles, which is crucial for evaluating the plastic deformation capacity of the structure under repeated vibration. The specific steps to obtain it include:

[0062] Determination of initial ductility: A single loading test is performed on the material to measure its maximum deformation capacity and ductility coefficient (generally defined as the ratio of the material's ultimate deformation capacity to its yield deformation capacity).

[0063] Fatigue ductility test: In a fatigue test with multiple cyclic loading, the maximum strain of each cycle is recorded until the ultimate strain is reached. Ductility decreases gradually with the number of cycles.

[0064] Calculation of ductility degradation rate: The ductility coefficient is fitted with the change of the number of cycles to obtain the ductility degradation rate, which describes the rate of ductility reduction.

[0065] After collecting the aforementioned low-cycle fatigue parameters, a fatigue degradation model suitable for earthquake analysis was constructed based on this data. The following are the detailed steps for model construction:

[0066] A hysteresis skeleton curve is established based on the cyclic stress-strain data. The skeleton curve can reflect the ultimate stress and strain of the material under monotonic loading and unloading, and reflects the changes in stiffness and bearing capacity after fatigue damage accumulation.

[0067] The yield strength decay rate and ductility degradation rate are introduced into the skeleton curve to simulate the stiffness and strength decay of the material after each cycle.

[0068] This is programmed into the analysis software so that the hysteresis curve adjusts to each loading change, reflecting the impact of material degradation on the structural stiffness and load-bearing capacity.

[0069] Based on experimental data, set the parameters for strain hardening and softening. Strain hardening describes the increase in strength after yielding, while strain softening describes the decrease in strength after the limit state.

[0070] A damage model based on energy dissipation is adopted, and the accumulated hysteresis loop area of ​​each cycle is used as the damage index. The accumulated energy dissipation is used to describe the low-cycle fatigue damage degree of the material.

[0071] According to the yield strength decay rate and ductility degradation rate, an equation relating the number of cycles to the material property decay is established and embedded into the model, enabling the model to dynamically update the mechanical properties of concrete and steel bars.

[0072] The fatigue degradation model parameters are input into the structural analysis software as material properties for components such as shear walls, beams, and columns, enabling the model to reflect actual degradation under low-cycle fatigue. By setting a loading sequence for the main shock and aftershocks, the dynamic response of the structure under long-term and repeated earthquakes is simulated. After each loading cycle, the hysteresis skeleton curve and damage accumulation are updated based on the material degradation and applied to the next loading condition to dynamically reflect changes in structural performance as fatigue accumulates.

[0073] The accuracy of the model was verified by comparing the model outputs (such as yield strength and ductility) with experimental data. Fatigue parameters in the model (such as ductility degradation rate and yield strength decay rate) were adjusted to minimize the error between the model predictions and experimental values, ensuring that the model effectively reflects the actual low-cycle fatigue behavior.

[0074] S2: Apply the material fatigue degradation model to several components such as shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks. Load the seismic waves one by one, record the residual stiffness of the structure after each earthquake loading, and determine the impact of fatigue accumulation on the structure.

[0075] Based on the previously constructed material fatigue degradation model, its properties are applied to load-bearing components such as shear walls and beams and columns. The model should be able to dynamically update the residual stiffness and strength of the components after each earthquake loading. Fatigue assessment is performed on shear walls and beams and columns that are likely to be subjected to the greatest stresses. This is particularly true for those located in critical locations along the seismic force transmission path, such as the base of the shear wall and the connection points between the wall and beams and columns, as these locations are more susceptible to stress concentration and fatigue damage.

[0076] When designing a loading sequence, a combination of mainshocks and aftershocks should be considered to reflect the repeated loading conditions experienced in actual earthquakes. Typically, a series of seismic waves is used, consisting of a mainshock and multiple aftershocks. The loading intensity and spacing of the aftershocks can be referenced from the historical earthquake record in the target area. The loading sequence is set in the model to ensure that the mainshock and aftershocks are applied sequentially in a predetermined time sequence. Aftershocks should contain a variety of frequencies and magnitudes to simulate the fatigue response of the structure under multiple vibration modes.

[0077] Sequentially apply earthquake waves to the structural model, running a fatigue analysis after each load. This ensures the model accurately captures the deformation and damage accumulation caused by each earthquake load.

[0078] Update material parameters: After each loading event, the material properties (e.g., stiffness and ductility) of the shear walls and beam-column components are updated to simulate the strength and ductility degradation caused by low-cycle fatigue. After each seismic loading event, the residual stiffness of the structure as a whole and of each key component is calculated and recorded to assess the stiffness degradation of the shear walls and beams. A curve is plotted showing the change in residual stiffness as a function of the number of cycles after each loading event, visually demonstrating the impact of fatigue damage on stiffness.

[0079] After analyzing the variation trend of the residual stiffness, the residual stiffness abnormal variation index is generated to judge the impact of the fatigue cumulative effect on the structure. The method for obtaining the residual stiffness abnormal variation index is as follows:

[0080] After multiple earthquake loadings, the residual stiffness data of multiple sets of key components are obtained, which are recorded as X = x1, x2, ..., x n ], where each x n Is a multidimensional vector containing the residual stiffness information of different components at different times. Input data matrix: X∈R m×n , where m represents different observation samples (such as the residual stiffness data of each loading cycle), and n represents the stiffness characteristics (feature dimensions) of different components or different time points. In order to eliminate the dimensional differences of different features, each feature is standardized to a data with a mean of 0 and a standard deviation of 1, and the covariance matrix Σ is calculated, which is expressed as: Z is the standardized matrix, T is the matrix transpose, solve the eigenvalues ​​and eigenvectors of the covariance matrix Σ, sort the eigenvalues ​​from large to small, select the eigenvectors corresponding to the first k largest eigenvalues ​​to form the matrix W, and project the data into a low-dimensional space through the matrix W to obtain the principal component matrix: Y = Z·W; where Y∈R m×k It is the data matrix after dimensionality reduction, which contains the main feature information.

[0081] Normal samples in historical data (i.e., the principal component matrix Y under normal residual stiffness conditions) are used as training data and marked as “normal”. If there are known abnormal samples, they are marked as “abnormal”.

[0082] Use SVM for binary classification training to learn the data distribution pattern under normal and abnormal conditions. The decision function of SVM is: f ( y ) =sgn ( w·y+b ) ; where: y∈R k represents the sample after dimensionality reduction, w is the weight vector of the classification hyperplane, and b is the bias term; sgn represents the sign function, the output 1 represents "normal" and -1 represents "abnormal".

[0083] For the newly observed residual stiffness data xnew, the newly observed data is standardized to obtain znew, and the standardized data is transformed into a low-dimensional space through W to obtain the principal component ynew = znew·W; ynew is input into the trained SVM classifier to judge its state: if f(ynew) = 1, it is judged to be normal; if f(ynew) = -1, it is judged to be abnormal.

[0084] If it is judged to be abnormal, the distance HK from the hyperplane is calculated as the residual stiffness abnormal change index, and the expression is: HK is the abnormal change index of residual stiffness.

[0085] The larger the residual stiffness abnormal variation index, the further the current observed value deviates from the normal state, indicating that the shear wall structure has significantly degraded in stiffness after repeated loading and that the cumulative fatigue effect has a significant impact on the structure. This means that critical parts of the structure may have suffered significant damage, with stiffness and bearing capacity rapidly decreasing. Continued loading may further exacerbate the expansion of microscopic cracks in the material and failure of joints. A high index value often indicates a potential risk of structural instability, and urgent reinforcement or repair may be required to restore the structure's seismic resistance and safety.

[0086] If the residual stiffness abnormal variation index is small, it indicates that the current residual stiffness is close to the normal state, indicating that the cumulative fatigue effect on the structure is low, and the structural stiffness and bearing capacity are still within a controllable range. This situation generally reflects that the structure still maintains good elasticity and ductility under the current earthquake loading conditions, the bearing capacity degrades slowly, and the overall performance is relatively stable. A small abnormal variation index indicates that the structure has not yet reached a significant state of damage and can continue to withstand aftershocks or further earthquake loads. No immediate reinforcement measures are required, but its changing trend still needs to be closely monitored.

[0087] S3: When the cumulative fatigue effect has a high degree of influence on the structure, the bearing capacity degradation curves of shear walls and different components under cyclic loads are drawn through the simulation results of multiple loadings, and the rate of change of the curves is analyzed to determine the abnormal state of the structural degradation rate.

[0088] Through preliminary simulation analysis, the bearing capacity (or ultimate bearing capacity) of key components such as shear walls and beams and columns is recorded after each cyclic loading cycle. This bearing capacity data can be derived from the loading sequence of the main shock and multiple aftershocks, and can reflect the fatigue damage of components under different loading conditions.

[0089] Bearing capacity record: For each key component (such as the bottom of the shear wall, connection nodes, beam-column connection parts, etc.), record the decrease in its bearing capacity after each loading.

[0090] Data normalization: Normalize the bearing capacity values ​​into percentages (relative to the initial bearing capacity) to facilitate subsequent comparisons and curve drawing.

[0091] Using the bearing capacity data under multiple loadings, the "bearing capacity degradation curves" of different components are drawn, that is, the curves showing the changes in the bearing capacity of the components with the number of loading cycles. These curves can intuitively show the changes in the bearing capacity of each component at different loading stages. The horizontal axis (X-axis): the number of loading cycles, usually expressed as the loading sequence or time sequence; the vertical axis (Y-axis): the residual bearing capacity (normalized percentage), which gradually decreases from 100% (initial value), reflecting the degradation of bearing capacity caused by the cumulative effect of fatigue. For example, if the bearing capacity of the shear wall drops from the initial 100% to 80% after the 10th aftershock, and then drops to 50% after the 20th aftershock, its degradation rate and trend can be clearly observed.

[0092] By analyzing the degradation curve, we focus on the rate of bearing capacity degradation, that is, the rate at which the bearing capacity decreases with the number of loading times. This can help determine whether fatigue damage is accumulating at an abnormal rate: if the degradation curve changes linearly, it means that the bearing capacity degrades at a fixed rate. This usually means that the structure is relatively stable under cyclic loading. However, if the curve decreases nonlinearly (such as a steep drop or a sudden turn), it may indicate that fatigue damage is intensifying. By calculating the slope of the curve at different loading stages, we observe whether there is a significant increase in the slope change. The larger the slope of the curve, the faster the bearing capacity degrades and the higher the degradation rate, indicating that the component may be close to the ultimate damage state.

[0093] After analyzing the changing trend of the bearing capacity degradation curve, the structural degradation rate drift index is generated to judge the abnormal state of the structural degradation rate. The method for obtaining the degradation rate drift index is as follows:

[0094] The bearing capacity degradation data of key parts of the structure (such as shear walls and beam-column connection points) are collected and processed to obtain the degradation rate sequence of the structure after each cyclic loading. This degradation rate sequence v=[v1,v2,…,v t ], where v t represents the degradation rate of bearing capacity after the tth cycle loading.

[0095] Degradation rate data: Among them C i is the residual bearing capacity after the i-th loading, and the degradation rate v i Indicates the rate of decrease of bearing capacity with the number of loading times. The exponentially weighted moving average is used to smooth the time series data of degradation rate in order to observe its changing trend more stably and reduce the interference of short-term fluctuations on drift detection. The calculation formula is: EWMA t =αv t +(1-α)EWMAt-1 Among them, EWMA t is the exponentially weighted moving average after the tth cycle loading, v t is the actual degradation rate after the t-th cycle loading, α is the smoothing coefficient, which ranges from 0<α≤1 and controls the sensitivity to new data; the initial value EWMA0 is set to the value of the first item v1; the degradation rate drift index is used to measure the actual degradation rate v t The degree of deviation from the smoothed degradation rate is calculated as follows: GH = | v t -EWMA t| ; Where GH is the degradation speed drift index.

[0096] A large degradation rate drift index indicates that the actual degradation rate of the structure deviates significantly from the smoothed trend, indicating an abnormal acceleration of degradation. This generally indicates that the structure is experiencing significant cumulative fatigue effects during the current loading cycle, with a rapid decline in bearing capacity and an intensification of degradation. In this case, the structure may be experiencing a rapid expansion of internal damage, with the material's fatigue performance approaching its limits and a high risk of failure. This requires close attention, and reinforcement or repair measures are recommended to prevent further deterioration.

[0097] When the degradation rate drift index is small, the actual degradation rate of the structure approaches a smooth trend, indicating a relatively stable degradation rate and a healthy structural state. This generally indicates that the cumulative effects of fatigue are minimal, bearing capacity degradation is slow, and the structure has not yet experienced significant damage or abnormal degradation. At this point, the structure has good durability and ductility and can continue to withstand subsequent seismic loads, but monitoring should still be maintained to observe changes in the degradation trend.

[0098] S4: Comprehensively analyze the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate to assess the risk of abnormal structural stiffness and bearing capacity.

[0099] The residual stiffness abnormal change index and the structural degradation rate drift index are converted into comprehensive feature vectors, which are used as inputs of the machine learning model. The machine learning model uses the risk value labels of abnormal structural stiffness and bearing capacity predicted by each set of comprehensive feature vectors as prediction targets, and takes minimizing the sum of prediction errors of all risk value labels of abnormal structural stiffness and bearing capacity as training targets. The machine learning model is trained until the sum of prediction errors reaches convergence, and the model training is stopped. The risk value of abnormal structural stiffness and bearing capacity is determined based on the model output results. The machine learning model is a polynomial regression model.

[0100] The method for obtaining the risk value of abnormal structural stiffness and bearing capacity is as follows: from the comprehensive feature vector training data of the trained machine learning model, the corresponding function expression is obtained: LR = F(HK, GH); where F is the output function of the model, HK is the residual stiffness abnormal change index, GH is the structural degradation speed drift index, and LR is the risk value of abnormal structural stiffness and bearing capacity.

[0101] S5: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken;

[0102] Comparing the obtained risk values ​​of abnormal structural stiffness and bearing capacity with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and comparing the risk values ​​of abnormal structural stiffness and bearing capacity with the first standard threshold and the second standard threshold respectively;

[0103] If the risk value of abnormality in structural stiffness and bearing capacity is greater than the second standard threshold, it means that the risk of abnormality in structural stiffness and bearing capacity is high, and a high risk signal is generated, and it is classified as a high risk level;

[0104] If the risk value of abnormal structural stiffness and bearing capacity is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the risk of abnormal structural stiffness and bearing capacity is medium, and a medium risk signal is generated and classified as a medium risk level;

[0105] If the risk value of abnormality in structural stiffness and bearing capacity is less than the first standard threshold, it means that the risk of abnormality in structural stiffness and bearing capacity is low. At this time, a low risk signal is generated and it is classified as a low risk level.

[0106] High-risk level measures: Immediately implement emergency reinforcement measures to prevent further structural deterioration or failure. For example, add supports and bracing to improve overall rigidity. Replace or supplement with high-strength rebar or fatigue-resistant concrete to enhance the durability of key areas. Upgrade monitoring systems and install sensors at key locations to monitor changes in rigidity and load-bearing capacity in real time. Restore the structure's load-bearing capacity as quickly as possible to prevent irreversible damage or collapse.

[0107] Medium-risk level measures: Conduct local reinforcement and regular maintenance, while strengthening monitoring. For example, increase support at local joints to reduce areas of concentrated fatigue. Regularly inspect and repair cracks, rust, and other signs of fatigue. Strengthen monitoring, regularly collect and analyze data to ensure that deterioration does not worsen. Slow the rate of structural deterioration while maintaining normal use, and monitor its health to ensure stability during aftershocks and continued use.

[0108] Low-risk level treatment measures: Continue to monitor the structural health and develop a long-term regular inspection and maintenance plan. For example, maintain the existing health monitoring frequency and monitor long-term trends in degradation indicators. Conduct on-site inspections at regular intervals (e.g., six months or a year) to prevent potential fatigue accumulation. Develop a subsequent maintenance plan to extend the service life of the structure. Maintain normal operation to prevent the accumulation of potential degradation and ensure the safety and durability of the structure in the long term.

[0109] S6: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement plan is designed to enhance the durability of the shear wall structure.

[0110] When the risk of abnormalities in structural stiffness and bearing capacity is at a medium risk level, that is, the risk values ​​of abnormalities in structural stiffness and bearing capacity generated within a fixed time period are greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk values ​​generated within subsequent fixed time periods that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a data set is established. The mean and standard deviation of the data set are calculated, and after analysis, a reinforcement plan is designed based on the analysis results to enhance the durability of the shear wall structure.

[0111] If the mean of the anomaly coefficients in the data set is greater than or equal to the reference threshold of the mean of the anomaly coefficients, and the standard deviation of the anomaly coefficients is less than the reference threshold of the standard deviation of the anomaly coefficients, the mean of the anomaly coefficients is high, indicating that the risk value is continuously high, indicating that the stiffness and bearing capacity of the structure have entered a relatively stable medium-deterioration state; a low standard deviation indicates that the degree of deterioration is relatively stable. In this case, no early warning signal is generated. A conventional reinforcement plan should be designed, focusing on strengthening the main load-bearing parts of the shear wall, and regular monitoring should be carried out to ensure that the structure remains stable during long-term use;

[0112] If the mean of the anomaly coefficient is greater than or equal to the reference threshold of the mean of the anomaly coefficient, and the standard deviation of the anomaly coefficient is greater than or equal to the reference threshold of the standard deviation of the anomaly coefficient, it means that the risk value fluctuates at a high level, indicating that the structure may be in an unstable deterioration state, and the bearing capacity degradation rate is sometimes fast and sometimes slow. In this case, a first-level early warning signal is generated, and a reinforcement plan is designed to strengthen the risk-concentrated areas (such as shear wall connection nodes or parts with concentrated stress). The monitoring frequency is increased, and a focus is placed on parts with large risk fluctuations.

[0113] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, the mean of the abnormal coefficient is high and the standard deviation is large, or the mean of the abnormal coefficient is low but the standard deviation is high, it indicates that the overall condition of the structure is relatively good, but occasional sudden moderate deterioration may occur, which may be a transient impact caused by local damage. At this time, a secondary warning signal is generated, and local repair is mainly carried out, focusing on the location of the sudden abnormality. If necessary, local support or reinforcement measures are added, and further observation is made to see whether there is any abnormal fluctuation trend;

[0114] If the mean of the anomaly coefficient is less than the reference threshold for the mean of the anomaly coefficient, and the standard deviation of the anomaly coefficient is less than the reference threshold for the standard deviation of the anomaly coefficient, both the mean and standard deviation of the anomaly coefficient are low, indicating that the stiffness and bearing capacity of the structure are relatively stable, and the degree of moderate deterioration remains at a low level. In this case, a Level 3 warning signal is generated, and no reinforcement measures are required, but normal monitoring should continue. A long-term inspection plan can be developed to maintain the durability of the shear wall structure and ensure its robustness under earthquakes and aftershocks.

[0115] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding treatment measures according to different warning signal levels.

[0116] In this embodiment, first, the low-cycle fatigue parameters of the material, such as the cyclic stress-strain curve, the yield strength decay rate and the ductility degradation rate, are collected, and based on this, a fatigue degradation model of concrete and steel bars is constructed. The degradation model is applied to shear walls and beam-column components to simulate the main shock and aftershock loading combination, and the residual stiffness after loading is recorded to evaluate the impact of the fatigue cumulative effect. When the fatigue cumulative effect is found to be significant, the bearing capacity degradation curve is drawn through the results of multiple loadings to analyze the abnormal state of the degradation rate. Then, the degradation rate and the cumulative fatigue effect are combined to evaluate the abnormal risk of structural stiffness and bearing capacity. According to the evaluation results, the risk is divided into high, medium and low levels, and corresponding treatment measures are taken. If the risk is medium, the risk change within a fixed time is further analyzed, and a reinforcement scheme is designed based on the analysis to enhance the durability and seismic performance of the structure.

[0117] Example 2: A high-rise shear wall structure seismic effect assessment system described in this example includes a degradation model construction module, an earthquake loading simulation module, a degradation rate analysis module, a comprehensive risk analysis module, a risk classification module, and a reinforcement scheme design module.

[0118] Degradation model construction module: collects and determines the low-cycle fatigue parameters of the material, including cyclic stress-strain curves, yield strength decay rate, and ductility degradation rate. Based on the low-cycle fatigue parameters, concrete and steel degradation models suitable for earthquake analysis are constructed;

[0119] Earthquake Loading Simulation Module: This module applies the material fatigue degradation model to several components such as shear walls and beams and columns. It then sets earthquake loading conditions, including a loading sequence combining a main shock with multiple aftershocks. Seismic waves are loaded one after another, and the residual stiffness of the structure after each earthquake loading is recorded to determine the impact of cumulative fatigue on the structure.

[0120] Deterioration rate analysis module: When the cumulative fatigue effect has a high impact on the structure, the module uses the simulation results of multiple loadings to draw the bearing capacity degradation curves of shear walls and different components under cyclic loads, analyzes the rate of change of the curves, and determines the abnormal state of the structural degradation rate;

[0121] Comprehensive risk analysis module: This module comprehensively analyzes the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate, and assesses the risk of abnormal structural stiffness and bearing capacity.

[0122] Risk grading module: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken;

[0123] Reinforcement scheme design module: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement scheme is designed to enhance the durability of the shear wall structure.

[0124] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0125] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for evaluating the seismic performance of a high-rise shear wall structure, characterized by: The following steps are included: S1: Collect and determine the low-cycle fatigue parameters of the material, including cyclic stress-strain curve, yield strength decay rate and ductility degradation rate. Based on the low-cycle fatigue parameters, construct concrete and steel degradation models suitable for seismic analysis; S2: Apply the material fatigue degradation model to several components such as shear walls and beams and columns. Set earthquake loading conditions, including a loading sequence combining a main shock and multiple aftershocks. Load the seismic waves one by one, record the residual stiffness of the structure after each earthquake loading, analyze the trend of the residual stiffness, and generate an abnormal residual stiffness change index to determine the impact of fatigue accumulation on the structure. S3: When the fatigue cumulative effect has a high degree of influence on the structure, the bearing capacity degradation curves of shear walls and different components under cyclic loads are plotted based on the simulation results of multiple loadings. The changing rate of the curves is analyzed. After analyzing the changing trend of the bearing capacity degradation curves, the structural degradation rate drift index is generated to determine the abnormal state of the structural degradation rate. S4: Comprehensively analyze the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate to assess the risk of abnormal structural stiffness and bearing capacity; S5: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken; S6: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement plan is designed to enhance the durability of the shear wall structure.

2. A method for evaluating the seismic performance of a high-rise shear wall structure according to claim 1, characterized in that: In S2, the residual stiffness abnormal change index is generated after analyzing the change trend of the residual stiffness to judge the impact of the fatigue cumulative effect on the structure. The method for obtaining the residual stiffness abnormal change index is: After multiple earthquake loadings, the residual stiffness data of multiple sets of key components are obtained, which are recorded as , where each Is a multidimensional vector containing the residual stiffness information of different components at different times. Input data matrix: , where m represents different observation samples, n represents the stiffness characteristics of different components, and the covariance matrix is ​​calculated , the expression is: ; Z is the standardized matrix, T is the matrix transpose, solve the covariance matrix Sort the eigenvalues ​​and eigenvectors of , sort the eigenvalues ​​from large to small, select the eigenvectors corresponding to the first k largest eigenvalues ​​to form a matrix W, and project the data into a low-dimensional space through the matrix W to obtain the principal component matrix: ;in It is the data matrix after dimensionality reduction. The normal samples in the historical data are used as training data. SVM is used for binary classification training to learn the data distribution pattern under normal and abnormal conditions. The decision function of SVM is: ;in: represents the sample after dimensionality reduction, w is the weight vector of the classification hyperplane, and b is the bias term; sgn represents the sign function. For the newly observed residual stiffness data xnew, the newly observed data is standardized to obtain znew. The standardized data is transformed into a low-dimensional space through W to obtain the principal component ; Input ynew into the trained SVM classifier and judge its status: If , it is judged to be normal; if , it is determined to be abnormal; if it is determined to be abnormal, the distance HK from the hyperplane is calculated as the residual stiffness abnormal change index, and the expression is: ; HK is the abnormal change index of residual stiffness.

3. The method for evaluating the seismic performance of a high-rise shear wall structure according to claim 2, wherein: In S3, the structural degradation rate drift index is generated after analyzing the change trend of the bearing capacity degradation curve to judge the abnormal state of the structural degradation rate. The method for obtaining the degradation rate drift index is as follows: The bearing capacity degradation data of the shear wall and beam-column connection points are collected and processed to obtain the degradation rate series of the structure after each cyclic loading. ,in Denotes the degradation rate of bearing capacity after the t-th cycle loading; Deterioration rate data: ;in is the residual bearing capacity after the i-th loading, and the degradation rate Indicates the rate of decline of bearing capacity with the number of loading times; the exponentially weighted moving average is used to smooth the time series data of the degradation rate, and the calculation formula is: ;in, is the exponentially weighted moving average after the t-th cycle loading, is the actual degradation rate after the t-th cycle loading, α is the smoothing coefficient, and its value range is ; Initial value Set as first item The degradation rate drift index is used to measure the actual degradation rate. The degree of deviation from the smoothed degradation rate is calculated as follows: ; Where GH is the degradation speed drift index.

4. A method for evaluating the seismic performance of a high-rise shear wall structure according to claim 3, characterized in that: In S4, the residual stiffness abnormal change index and the structural degradation rate drift index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as inputs of the machine learning model. The machine learning model uses each set of comprehensive feature vectors to predict the risk value label of abnormal structural stiffness and bearing capacity as the prediction target, and takes minimizing the sum of prediction errors of all risk value labels of abnormal structural stiffness and bearing capacity as the training target. The machine learning model is trained until the sum of prediction errors reaches convergence, and the model training is stopped. The risk value of abnormal structural stiffness and bearing capacity is determined according to the model output results. Among them, the machine learning model is a polynomial regression model.

5. The method for evaluating the seismic performance of a high-rise shear wall structure according to claim 4, wherein: In S5, based on the assessment results, the risk of abnormalities in structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, specifically; Comparing the obtained risk values ​​of abnormal structural stiffness and bearing capacity with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and comparing the risk values ​​of abnormal structural stiffness and bearing capacity with the first standard threshold and the second standard threshold respectively; If the risk value of abnormality in structural stiffness and bearing capacity is greater than the second standard threshold, it means that the risk of abnormality in structural stiffness and bearing capacity is high, and a high risk signal is generated, and it is classified as a high risk level; If the risk value of abnormal structural stiffness and bearing capacity is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the risk of abnormal structural stiffness and bearing capacity is medium, and a medium risk signal is generated and classified as a medium risk level; If the risk value of abnormality in structural stiffness and bearing capacity is less than the first standard threshold, it means that the risk of abnormality in structural stiffness and bearing capacity is low. At this time, a low risk signal is generated and it is classified as a low risk level.

6. The method for evaluating the seismic performance of a high-rise shear wall structure according to claim 1, wherein: In S6, when the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period, specifically: When the risk of abnormalities in structural stiffness and bearing capacity is at a medium risk level, that is, the risk values ​​of abnormalities in structural stiffness and bearing capacity generated within a fixed time period are greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk values ​​generated within subsequent fixed time periods that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a data set is established. The mean and standard deviation of the data set are calculated, and after analysis, a reinforcement plan is designed based on the analysis results to enhance the durability of the shear wall structure.

7. The method for evaluating the seismic performance of a high-rise shear wall structure according to claim 6, wherein: If the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, the mean of the abnormal coefficients is high, indicating that the stiffness and bearing capacity of the structure have entered a stable moderate deterioration state; If no warning signal is generated at this time, a conventional reinforcement plan should be designed and regularly monitored to ensure that the structure remains stable in long-term use; If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, it indicates that the structure is in an unstable deterioration state. At this time, a first-level early warning signal is generated, and a reinforcement plan is designed to strengthen the risk-concentrated areas and increase the monitoring frequency. If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, it indicates that the overall condition of the structure is good. At this time, a secondary warning signal is generated, and local repair is mainly carried out; If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, it indicates that the stiffness and bearing capacity of the structure are stable. At this time, a third-level warning signal is generated and no reinforcement measures are required, but normal monitoring should continue.

8. A high-rise shear wall structure seismic effect evaluation system, used to implement the high-rise shear wall structure seismic effect evaluation method according to any one of claims 1 to 7, characterized in that: It includes degradation model construction module, earthquake loading simulation module, degradation rate analysis module, comprehensive risk analysis module, risk classification module and reinforcement scheme design module; Degradation model construction module: collects and determines the low-cycle fatigue parameters of the material, including cyclic stress-strain curves, yield strength decay rate, and ductility degradation rate. Based on the low-cycle fatigue parameters, concrete and steel degradation models suitable for earthquake analysis are constructed; Earthquake Loading Simulation Module: This module applies the material fatigue degradation model to several components such as shear walls and beams and columns. It then sets earthquake loading conditions, including a loading sequence combining a main shock with multiple aftershocks. Seismic waves are loaded one after another, and the residual stiffness of the structure after each earthquake loading is recorded to determine the impact of cumulative fatigue on the structure. Deterioration rate analysis module: When the cumulative fatigue effect has a high impact on the structure, the module uses the simulation results of multiple loadings to draw the bearing capacity degradation curves of shear walls and different components under cyclic loads, analyzes the rate of change of the curves, and determines the abnormal state of the structural degradation rate; Comprehensive risk analysis module: This module comprehensively analyzes the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate, and assesses the risk of abnormal structural stiffness and bearing capacity. Risk grading module: Based on the assessment results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, namely high risk level, medium risk level and low risk level, and corresponding measures are taken; Reinforcement scheme design module: When the risk of abnormal structural stiffness and bearing capacity is at a medium risk level, further analysis is conducted on the risk of abnormal structural stiffness and bearing capacity within a fixed time period. Based on the analysis results, a reinforcement scheme is designed to enhance the durability of the shear wall structure.

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