Method and system for evaluating anti-seismic effect of high-rise shear wall structure

Abstract

The invention discloses a high-rise shear wall structure anti-seismic effect evaluation method and system, and particularly relates to the technical field of high-rise shear walls. The method comprises the following steps: accurately collecting low-cycle fatigue parameters, constructing a concrete and steel bar degradation model considering a fatigue effect, applying the model to anti-seismic analysis of a shear wall structure, simulating the influence of main shock and multiple aftershock loading on structural performance, drawing a bearing capacity degradation curve according to multiple loading results, and analyzing a degradation speed abnormal state. The risk that the structural rigidity and the bearing capacity are abnormal is judged by combining the change of the residual rigidity and the bearing capacity, the risk is divided into high, medium and low levels, and proper reinforcement or prevention measures are provided for different risk levels, so that the defect that the material fatigue effect is not fully considered in the existing anti-seismic evaluation is effectively overcome, and the anti-seismic evaluation accuracy is improved. According to the method, the damage accumulation condition of the high-rise shear wall structure in frequent aftershocks can be predicted more accurately, so that the risk of premature failure of the structure is reduced, and the durability and safety of a building in an earthquake are improved.

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 evaluation of high-rise shear wall structures refers to the analysis and evaluation of the seismic performance of high-rise buildings with shear wall structures during earthquakes. This evaluation focuses on the stability, deformation performance and bearing capacity of the structure under the action of seismic forces to ensure the safety of the building under earthquakes of different intensities. The shear wall structure is a major structural system for resisting horizontal seismic forces. By strengthening the stiffness and strength of the wall, the building can more effectively absorb and dissipate seismic energy, reduce deformation and prevent collapse.

[0003] In the actual evaluation process, a series of simulation analysis methods are usually used, such as time history analysis, response spectrum analysis or pushover analysis, to simulate the structural response under earthquake action. Through these analyses, the deformation mode, internal force distribution and stress of the shear wall can be determined, so as to judge its seismic effect. The evaluation results can help designers optimize the structural design to meet the requirements of seismic code, and provide reliable data support for engineering practice to ensure the stability and safety of high-rise buildings in earthquakes.

[0004] The prior art has the following deficiencies:

[0005] Under strong earthquakes, the concrete and steel materials in the shear wall structure will be subjected to repeated stress-strain cycles, especially in the case of long-term or multiple aftershocks, the material 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, and 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, where 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 overcome the shortcomings of the background technology.

[0007] In order to achieve the above 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. According to the low-cycle fatigue parameters, construct the concrete and steel degradation model suitable for seismic analysis;

[0009] S2: Apply the material fatigue degradation model to several components of shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks, load 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 accumulation 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 change rate of the curve is analyzed to determine the abnormal state of the structural degradation rate;

[0011] S4: Comprehensively analyze the impact of fatigue accumulation effect on the structure and the abnormal state of structural degradation rate, and evaluate 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, and a reinforcement plan is designed based on the analysis results 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 groups of key components are obtained, denoted 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. 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 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 patterns 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 symbolic 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 state: 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, the change trend of the bearing capacity degradation curve is analyzed to generate a structural degradation rate drift index, and the abnormal state of the structural degradation rate is determined. The method for obtaining the degradation rate drift index is:

[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 It represents the degradation rate of bearing capacity after the tth 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 decrease 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: 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 term 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 predicts the risk value label of abnormal structural stiffness and bearing capacity with each group of comprehensive feature vectors as the prediction target, and minimizes 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 converges 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, according to the evaluation results, the risk of abnormalities in 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] The obtained risk values ​​of abnormal structural stiffness and bearing capacity are compared 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 the risk values ​​of abnormal structural stiffness and bearing capacity are compared with the first standard threshold and the second standard threshold respectively;

[0021] If the risk value of abnormality of structural stiffness and bearing capacity is greater than the second standard threshold, it means that the risk of abnormality of structural stiffness and bearing capacity is high, and a high risk signal is generated at this time, 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 of structural stiffness and bearing capacity is less than the first standard threshold, it means that the risk of abnormality of 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 value of abnormalities in structural stiffness and bearing capacity generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk values ​​generated in a subsequent fixed time period 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 medium-deterioration state; at this time, no 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 warning signal is generated, and a reinforcement plan is designed to strengthen the risk concentration area 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 state 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, including a degradation model construction module, an earthquake loading simulation module, a degradation rate analysis module, a risk comprehensive analysis module, a risk classification module and a reinforcement scheme design module;

[0031] Degradation model building module: 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, build concrete and steel degradation models suitable for earthquake analysis;

[0032] Earthquake loading simulation module: Apply the material fatigue degradation model to several components of shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks, load 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;

[0033] Deterioration rate analysis module: 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 change rate of the curve is analyzed to determine the abnormal state of the structural degradation rate;

[0034] Comprehensive risk analysis module: Comprehensively analyze the impact of fatigue accumulation effect on the structure and the abnormal state of structural degradation rate, and evaluate 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, and a reinforcement scheme is designed based on the analysis results 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 can deeply analyze the fatigue cumulative effect of high-rise shear wall structures under earthquakes and multiple aftershocks through systematic evaluation methods and models, 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, so as to accurately identify the risk degree 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. The present invention not only improves the accuracy of seismic assessment, but also supports the design of targeted reinforcement measures. Through comprehensive analysis of fatigue accumulation and degradation rate, further monitoring of structures with medium risk levels is carried out, and combined with mean and standard deviation analysis, data support is provided for the design of reasonable reinforcement schemes. Ultimately, this method can realize dynamic monitoring and preventive maintenance of the durability and stability of shear wall structures in areas with frequent aftershocks, reduce the risk of premature failure and collapse of the structure, and significantly improve the seismic safety and service life of the building. 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 drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1 The present invention is a flow chart of the method.

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

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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. According to the low-cycle fatigue parameters, construct the concrete and steel degradation model suitable for seismic analysis;

[0046] S2: Apply the material fatigue degradation model to several components of shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks, load 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 accumulation 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 change rate of the curve is analyzed to determine the abnormal state of the structural degradation rate;

[0048] S4: Comprehensively analyze the impact of fatigue accumulation effect on the structure and the abnormal state of structural degradation rate, and evaluate 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, and a reinforcement plan is designed based on the analysis results 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 cyclic stress-strain curve, yield strength decay rate and ductility degradation rate. According to the low-cycle fatigue parameters, the concrete and steel degradation models suitable for seismic analysis are constructed, specifically:

[0052] The cyclic stress-strain curve describes the stress-strain relationship of a material under repeated loads 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] Test 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 in each cycle.

[0055] Hysteresis curve: Stress-strain hysteresis curve is plotted 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 in each cyclic loading, helping to evaluate the change 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 attenuation 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 decreasing 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 in repeated vibrations. The specific acquisition steps 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 cycles of 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: Fit the change of ductility coefficient with the number of cycles to obtain the ductility degradation rate, which describes the speed of ductility reduction.

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

[0066] The 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 in 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 according to each loading change, reflecting the effect of material degradation on the stiffness and load-bearing capacity of the structure.

[0069] Based on experimental data, set the parameters of strain hardening and softening. The strain hardening part is used to describe the strength increase after yielding, and the strain softening part is used to describe the strength decrease in 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, the equation of cycle number and material performance decay is established and embedded in the model, so that the model can dynamically update the mechanical properties of concrete and steel bars.

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

[0073] The accuracy of the model is verified by comparing the model output results (such as yield strength, ductility, etc.) with the experimental data. The fatigue parameters in the model (such as ductility degradation rate and yield strength decay rate) are adjusted to minimize the error between the model prediction value and the experimental value to ensure that the model can effectively reflect the real low-cycle fatigue behavior.

[0074] S2: Apply the material fatigue degradation model to several components of 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 material fatigue degradation model constructed previously, 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. Shear walls and beams and columns that may be subjected to the maximum stress are selected for fatigue assessment, especially those located in key locations along the seismic force transmission path, such as the bottom of the shear wall, the connection point between the wall and the beam, etc., which are more prone to stress concentration and fatigue damage.

[0076] When designing a loading sequence, a combination of main shocks and aftershocks should be considered to reflect the repeated loading in an actual earthquake. A series of seismic waves is usually set, including a main shock and multiple aftershocks. The loading intensity and interval of the aftershocks can refer to the actual earthquake history records in the target area. The loading sequence is set in the model to ensure that the main shock and aftershocks are applied one after another in a predetermined time sequence. Aftershocks should contain different frequencies and magnitudes to simulate the fatigue response of the structure under multiple vibration modes.

[0077] Seismic waves are applied to the structural model sequentially, and fatigue analysis is run after each load. This ensures that the model accurately captures the deformation and damage accumulation caused by each earthquake load.

[0078] Update material parameters: After each loading, update the material properties (such as stiffness and ductility) of shear walls and beam-column components to simulate the strength and ductility degradation caused by low-cycle fatigue effects. After each earthquake loading, calculate and record the residual stiffness of the structure as a whole and each key component to obtain the stiffness degradation of shear walls and beams and columns. Draw a curve of the change of the residual stiffness of the structure with the number of cycles after each loading to intuitively show the degree of influence of fatigue damage on stiffness.

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

[0080] After multiple earthquake loadings, the residual stiffness data of multiple groups of key components are obtained, denoted 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 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 patterns under normal and abnormal conditions. The decision function of SVM is: ( 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, and 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 determine 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 change index is, the further the current observation value deviates from the normal state, indicating that the stiffness of the shear wall structure has significantly degraded after repeated loading, and the fatigue accumulation effect has a high degree of influence on the structure. This means that significant damage may have occurred in key parts of the structure, and the stiffness and bearing capacity are rapidly decreasing. If the loading continues, it may further aggravate the expansion of micro cracks in the material and the failure of the connection parts. High index values ​​often indicate that the structure has a potential risk of instability. At this time, emergency reinforcement or repair may be required to restore the seismic resistance and safety of the structure.

[0086] If the residual stiffness abnormal change index is small, it means that the current residual stiffness is close to the normal state, indicating that the fatigue accumulation effect has a low impact on the structure, and the stiffness and bearing capacity of the structure are still within the controllable range. This situation usually reflects that the structure still maintains good elasticity and ductility under the current earthquake loading condition, the bearing capacity degrades slowly, and the overall performance is relatively stable. A smaller abnormal change index indicates that the structure has not yet reached an obvious damage state and can continue to bear aftershocks or further earthquake loads. There is no need to take immediate reinforcement measures, but its change 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 the early 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. The bearing capacity data can be derived from the loading sequence of the main shock and multiple aftershocks, which 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 points, etc.), record the decrease in bearing capacity after each loading.

[0090] Data standardization: Standardize 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 curve" of different components is drawn, that is, the curve of the change of component bearing capacity with the number of loading cycles. These curves can intuitively show the change of bearing capacity of each component at different loading stages. The horizontal axis (X-axis): the number of loading cycles, usually expressed as 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, which 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 in the curve), it may indicate that fatigue damage has intensified. By calculating the slope of the curve at different loading stages, we observe whether there is a significant increase in the change in slope. 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 change 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 It indicates the rate of decrease of bearing capacity with the number of loading times. The exponential weighted moving average is used to smooth the time series data of the 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] When the degradation rate drift index is large, it means that the actual degradation rate of the structure has obviously deviated from the smoothed trend, indicating that the degradation rate has accelerated abnormally. This usually means that the structure has been significantly affected by fatigue accumulation in the current loading cycle, the bearing capacity has dropped rapidly, and the degradation state is intensifying. At this time, the structure may experience a sharp expansion of internal damage, the fatigue performance of the material has approached the limit, and there is a high risk of failure, which requires high attention, and it is recommended to take reinforcement or repair measures to prevent further deterioration.

[0097] When the degradation rate drift index is small, it indicates that the actual degradation rate of the structure is close to the smooth trend, indicating that the current degradation rate is relatively stable and the structural state is relatively healthy. This situation usually means that the fatigue accumulation effect has little impact on the structure, the bearing capacity degrades slowly, and the structure has not yet suffered significant damage or abnormal degradation. At this time, the structure has good durability and ductility and can continue to withstand subsequent seismic loads, but it should still be monitored to observe changes in subsequent degradation trends.

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

[0099] The residual stiffness abnormal change index and the structural degradation speed drift index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model predicts the risk value label of abnormal structural stiffness and bearing capacity for each group of comprehensive feature vectors as the prediction target, and minimizes 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 converges 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.

[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); wherein 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] The obtained risk values ​​of abnormal structural stiffness and bearing capacity are compared 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 the risk values ​​of abnormal structural stiffness and bearing capacity are compared with the first standard threshold and the second standard threshold respectively;

[0103] If the risk value of abnormality of structural stiffness and bearing capacity is greater than the second standard threshold, it means that the risk of abnormality of structural stiffness and bearing capacity is high, and a high risk signal is generated at this time, 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 of structural stiffness and bearing capacity is less than the first standard threshold, it means that the risk of abnormality of 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 treatment measures: Take emergency reinforcement measures immediately to prevent further deterioration or failure of the structure. For example: Add support and diagonal bracing structures to improve overall stiffness. Replace or supplement high-strength steel bars or concrete with high fatigue resistance to improve the durability of key parts. Upgrade the monitoring system and install sensors at key locations to monitor changes in stiffness and bearing capacity in real time. Restore the bearing capacity of the structure in the shortest possible time to prevent irreversible damage or collapse of the structure.

[0107] Medium risk level treatment measures: local reinforcement and regular maintenance, while strengthening monitoring. For example: increase the support of local nodes to reduce the area of ​​fatigue concentration. Regularly check and repair cracks, rust and other signs of fatigue. Strengthen monitoring, regularly collect data and analyze them to ensure that the deterioration does not worsen further. Slow down the deterioration rate of the structure while maintaining normal use, and monitor its health status to ensure its stability in aftershocks or continued use.

[0108] Low-risk level treatment measures: Continue to monitor the health of the structure and develop long-term regular inspection and maintenance plans. For example: maintain the existing health monitoring frequency and pay attention to the long-term trend of degradation indicators. Conduct on-site inspections at regular intervals (such as half a year or a year) to preventively check for potential fatigue accumulation. Develop a follow-up maintenance plan to extend the service life of the structure. Maintain normal use 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, and a reinforcement plan is designed based on the analysis results 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 value of abnormalities in structural stiffness and bearing capacity generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk values ​​generated in a subsequent fixed time period 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 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 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, and no early warning signal is generated at this time. A conventional reinforcement plan should be designed, focusing on strengthening the main stress-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 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 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; at this time, a first-level warning signal is generated, and a strengthening and reinforcement plan is designed to strengthen the risk concentration areas (such as shear wall connection nodes or parts with concentrated force), and increase the monitoring frequency, focusing 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, and the mean of the abnormal coefficient is low but the standard deviation is high, it indicates that the overall state 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, mainly focusing on local repairs, focusing on the location of sudden abnormalities, adding local support or reinforcement measures when necessary, and continuing to observe whether there will be abnormal fluctuation trends;

[0114] 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, the mean and standard deviation of the abnormal coefficient are low, indicating that the stiffness and bearing capacity of the structure are relatively stable, and the medium deterioration degree remains at a low level. At this time, 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 formulated to maintain the durability of the shear wall structure and ensure its robustness under earthquakes or 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 handling 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 the fatigue degradation model of concrete and steel bars is constructed based on this. The degradation model is applied to shear walls and beam-column components, the main shock and aftershock loading combination is simulated, and the residual stiffness after loading is recorded to evaluate the impact of the fatigue accumulation effect. When the fatigue accumulation 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 structural stiffness and bearing capacity abnormal risks are evaluated by combining the degradation rate and the cumulative fatigue effect. 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] Embodiment 2, a high-rise shear wall structure seismic effect evaluation system described in this embodiment, includes a degradation model construction module, an earthquake loading simulation module, a degradation rate analysis module, a risk comprehensive analysis module, a risk classification module and a reinforcement scheme design module;

[0118] Degradation model building module: 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, build concrete and steel degradation models suitable for earthquake analysis;

[0119] Earthquake loading simulation module: Apply the material fatigue degradation model to several components of shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks, load 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;

[0120] Deterioration rate analysis module: 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 change rate of the curve is analyzed to determine the abnormal state of the structural degradation rate;

[0121] Comprehensive risk analysis module: Comprehensively analyze the impact of fatigue accumulation effect on the structure and the abnormal state of structural degradation rate, and evaluate 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, and a reinforcement scheme is designed based on the analysis results to enhance the durability of the shear wall structure.

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

[0125] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0126] Those of ordinary skill 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 to be beyond the scope of this application.

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

Claims

1. A method for evaluating the seismic effect of a high-rise shear wall structure, characterized in that: The steps include: 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. According to the low-cycle fatigue parameters, construct the concrete and steel degradation model suitable for seismic analysis; S2: Apply the material fatigue degradation model to several components of shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks, load 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; S3: When the fatigue accumulation 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 change rate of the curve is analyzed to determine the abnormal state of the structural degradation rate; S4: Comprehensively analyze the impact of fatigue accumulation effect on the structure and the abnormal state of structural degradation rate, and evaluate 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, and a reinforcement plan is designed based on the analysis results to enhance the durability of the shear wall structure.

2. The method for evaluating the seismic effect 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 influence of the fatigue accumulation effect on the structure. The method for obtaining the residual stiffness abnormal change index is: After multiple earthquake loadings, multiple sets of residual stiffness data of key components are obtained, denoted as X = x1, x2, ..., xn], where each xn is a multidimensional vector containing the residual stiffness information of different components at different times. The input data matrix is: X∈Rm×n, where m represents different observation samples and n represents the stiffness characteristics of different components. The covariance matrix Σ is calculated, and the expression is: 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 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 patterns 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 symbolic 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 state: 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.

3. A method for evaluating the seismic effect of a high-rise shear wall structure according to claim 2, characterized in that: In S3, the change trend of the bearing capacity degradation curve is analyzed to generate the structural degradation rate drift index 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 sequence v = [v1, v2, …, v t ], where v t It represents the degradation rate of bearing capacity after the tth 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 decrease 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: 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 term 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.

4. A method for evaluating the seismic effect 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 speed 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 predicts the risk value labels of abnormal structural stiffness and bearing capacity for each group of comprehensive feature vectors as prediction targets, and minimizes 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 converges, and the model training is stopped. The risk values ​​of abnormal structural stiffness and bearing capacity are determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. A method for evaluating the seismic effect of a high-rise shear wall structure according to claim 4, characterized in that: In S5, according to the evaluation results, the risk of abnormal structural stiffness and bearing capacity is divided into different levels, which are divided into high risk level, medium risk level and low risk level, specifically; The obtained risk values ​​of abnormal structural stiffness and bearing capacity are compared 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 the risk values ​​of abnormal structural stiffness and bearing capacity are compared with the first standard threshold and the second standard threshold respectively; If the risk value of abnormality of structural stiffness and bearing capacity is greater than the second standard threshold, it means that the risk of abnormality of structural stiffness and bearing capacity is high, and a high risk signal is generated at this time, 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 of structural stiffness and bearing capacity is less than the first standard threshold, it means that the risk of abnormality of 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. A method for evaluating the seismic effect of a high-rise shear wall structure according to claim 1, characterized in that: 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 value of abnormalities in structural stiffness and bearing capacity generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the risk values ​​generated in a subsequent fixed time period 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. A method for evaluating the seismic effect of a high-rise shear wall structure according to claim 6, characterized in that: 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 medium deterioration state; No warning signal is generated at this time, and 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 warning signal is generated, and a reinforcement plan is designed to strengthen the risk concentration area 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 state 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 a 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 velocity analysis module, risk comprehensive analysis module, risk classification module and reinforcement scheme design module; Degradation model building module: 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, build concrete and steel degradation models suitable for earthquake analysis; Earthquake loading simulation module: Apply the material fatigue degradation model to several components of shear walls and beams and columns, and set earthquake loading conditions, including a loading sequence of a main shock and multiple aftershocks, load 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; Deterioration rate analysis module: 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 change rate of the curve is analyzed to determine the abnormal state of the structural degradation rate; Comprehensive risk analysis module: Comprehensively analyze the impact of fatigue accumulation on the structure and the abnormal state of structural degradation rate, and evaluate 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, and a reinforcement scheme is designed based on the analysis results to enhance the durability of the shear wall structure.

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