86 results about "Aftershock" patented technology

The invention relates to a multi-dimensional seismic risk assessment method based on seismic oscillation space-time distribution. The method comprises the following steps: S1, selecting IM indexes; s2, key structure parameters are extracted; s3, recording an engineering demand parameter EDP; s4, selecting an optimal IM index; s5, establishing an agent model of structural seismic response; s6, establishing an earthquake prediction equation; s7, establishing an aftershock risk analysis model; s8, obtaining aftershock intensity samples of the positions of the target structures; s9, obtaining an aftershock vulnerability curve; and S10, determining repair time and repair cost according to the repair of the structure and the repair of the structure function so as to finally determine the total repair cost of the structure of the regional line and the anti-seismic capacity after repair. Compared with the prior art, the method has the advantages of improving the accuracy of earthquake risk estimation and the like.

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.

The invention discloses a rapid assessment method and system for shock resistance toughness of main and aftershocks based on a cycle-Transform network, and the method comprises the steps: building a finite element model of a building structure, inputting a plurality of groups of earthquake accelerations of main and aftershocks into the model, and obtaining a database comprising a plurality of groups of accelerations and corresponding structure displacement responses; building a loop-Transform neural network, and carrying out the training and optimization of the loop-Transform neural network; inputting the main shock and aftershock sequence into the optimized cycle-Transform neural network, predicting the displacement response of the structure under the double amplitude modulation main shock and aftershock sequence, then obtaining the building damage probability corresponding to different earthquake intensities under the main shock and aftershock situation by using maximum likelihood estimation, evaluating the corresponding damage index, calculating the recovery time, and finally obtaining the building damage probability corresponding to the different earthquake intensities under the main shock and aftershock situation. A function recovery curve of the structure is predicted, and shock resistance toughness evaluation is completed. According to the method, the precision of structure accumulated damage prediction under the aftershock effect is improved, and the calculation complexity and time consumption of toughness evaluation under the main and aftershock earthquake scene are remarkably reduced.

The invention discloses a physical and data hybrid driven suspension bridge dynamic response prediction method under main and aftershocks, and relates to the technical field of suspension bridge earthquake response prediction. Determining an acceleration time program sequence of main and aftershock vibration; aiming at the ground anchor type steel truss suspension bridge, considering the uncertainty of key parameters, and establishing a finite element model; collecting bridge output parameters, establishing a data set and dividing the data set into a training set and a verification set; carrying out normalization processing on the data; establishing a physical constraint equation, and solving a main beam midspan longitudinal displacement wave under physical constraint; constructing a machine learning model by using the training set based on ANN and LSTM; establishing a machine learning model loss function; verifying the machine learning model; and a machine learning model is utilized to predict main beam midspan longitudinal displacement waves. According to the method, the displacement time history considering the uncertainty of the key parameters and under the action of main and aftershock seismic actions can be calculated, and the adverse effect caused by the fact that the uncertainty of the key parameters and the aftershock stage cannot be considered in a traditional prediction method is made up.

The invention discloses a main shock and aftershock full-life vulnerability analysis method based on active learning, and belongs to the field of structural anti-seismic safety evaluation. The method solves the problems that an existing full-life analysis method needs high calculation cost and does not consider the influence of environmental factors and main shock and aftershock coupling effects on the initial damage of the structure. The agent model is constructed based on high-precision and low-precision data fusion, and a meta-learning mechanism is introduced to describe internal association among different degradation states, so that the sample utilization efficiency is improved; a generalized learning function is combined with a two-stage active learning strategy to dynamically select a training sample point with the most information amount, efficient refinement of a prediction model in each degradation scene is gradually realized, the calculation cost is further reduced, and the agent model prediction precision is improved. And finally, through a time-varying two-dimensional limit state equation, explicitly considering the correlation between the initial damage evolved along with time and the structure residual capacity, and effectively fusing the coupling effect of environmental degradation and the seismic sequence. The method can be applied to structure vulnerability analysis.

The invention provides a main shock and aftershock sequence side slope vulnerability analysis method and system based on double response indexes, and relates to the technical field of earthquake side slope vulnerability analysis, and the method comprises the steps: building a GeoStudio side slope model according to the physical parameters of a side slope soil body, and obtaining the initial static safety coefficient and critical acceleration of the side slope; performing incremental dynamic time history analysis according to the main earthquake ground motion data, the slope model and the initial static safety coefficient of the slope to obtain a soil shear strength parameter value after the main earthquake; updating the initial critical acceleration of the side slope to obtain the critical acceleration of the side slope after the main earthquake; performing aftershock increment dynamic time history analysis according to the aftershock oscillation data and the critical acceleration of the side slope after the main shock to obtain a safety coefficient value and an accumulated displacement result of the soil body after the aftershock; and defining a vulnerability function based on the logarithmic normal cumulative distribution function to obtain a multi-level slope vulnerability curve. According to the method, the slope risk assessment precision and the visual decision-making capability are improved.

The invention provides a mine vibration safety detection method and system during mining blasting. The method comprises the following steps: acquiring three-component signals of explosion fluctuation at a plurality of sampling points in a warning range through an intelligent sensor network during mine blasting; decomposing each three-component signal into a plurality of attenuation waves, determining cross correlation coefficients of the attenuation waves in the same component direction among different sampling points, and performing reverse time back propagation on each sampling point according to all the cross correlation coefficients to obtain a reverse time wave field of blasting vibration at each sampling point; inputting all the reverse time wave fields into a pre-trained multi-seismic-source positioning model to obtain a plurality of explosion points; and determining the risk value of miss-fire of each blast hole in the detonation network according to the coordinate positions of all the explosion points, and performing explosion aftershock evaluation on the mine according to all the risk values. According to the scheme, the miss-fire risk of each blast hole can be evaluated after mine blasting.

The invention discloses a multi-source data fusion-based power transmission tower earthquake damage assessment and emergency response method and system. The method comprises the steps of collecting and preprocessing multi-source data of geological disasters of a power transmission tower; constructing a multi-modal fusion-digital twinborn driven dynamic transmission tower earthquake damage evaluation model, and evaluating the preprocessed multi-source data; and generating a corresponding emergency response strategy based on the evaluation result. According to the method, the multi-source data of the geological disaster of the power transmission tower is analyzed and processed by using the multi-modal fusion-digital twinning driven dynamic power transmission tower earthquake damage assessment model, so that the current damage can be comprehensively assessed according to the multi-source data, the evolution trend under aftershock and secondary disaster can be accurately predicted, and prospective early warning is realized.

The invention relates to the technical field of slope stability prediction, in particular to a slope stability evolution prediction method of a main shock and aftershock sequence cumulative damage mechanism. The method comprises the following steps: determining an initial strength parameter of a slope sliding surface rock-soil body and a dynamic strength attenuation parameter under the action of a main shock and aftershock sequence through an indoor cyclic load test, and establishing a cumulative damage model; three-dimensional side slope geometric features are obtained, and the spatial orientation and the stress direction of a potential sliding surface are determined; calculating a dynamic sliding force in combination with seismic oscillation parameters, and updating an effective strength parameter and a dynamic anti-sliding force of the sliding surface in real time; and carrying out staged calculation and dynamic evolution analysis by adopting a limit equilibrium method or a strength reduction method. According to the method, by introducing a rock-soil body strength accumulated damage and progressive attenuation mechanism under the cyclic action of the main and aftershock sequences, the damage accumulation process and the stability evolution law of the slope stability along with the development of the main and aftershock sequences are described, so that the progressive instability process and the accumulated instability risk driven by the aftershock after the main shock is triggered can be identified.

The invention belongs to the technical field of earthquake assessment, and particularly relates to an earthquake influence field dynamic assessment method and system based on multi-source information. The invention discloses an earthquake influence field dynamic evaluation method based on multi-source information. Real-time observation data of various earthquake professional sensors are fully utilized, and an earthquake influence field is dynamically evaluated in different time periods. And multi-source information such as seismic basic parameters, a seismic source mechanism solution, a seismic source fracture process, station seismic oscillation parameters and aftershock information is fused to comprehensively evaluate and correct the seismic influence field.

According to the main shock and aftershock time sequence rapid construction method and device provided by the invention, the influence of similar source generation mechanisms of main shock and aftershock, similar propagation paths, the same field effect and the like is fully considered, and the main shock and aftershock time sequence is rapidly and accurately constructed. The method comprises the steps that 1, the most dangerous fault plane and the spatial position and size of the fault plane are determined according to seismic fault data, sub-events with different circle center positions are generated randomly, and the size of each sub-event is represented by a radius; 2, determining the magnitude of the aftershock according to the set main shock magnitude and the main and aftershock power law relationship; step 3, according to the magnitude of the main earthquake and the aftershock, using rectangles consistent with the size of a main earthquake source and aftershock source to perform frame selection on the fault plane, and using sub-events contained in the rectangles to form an earthquake source model so as to obtain an earthquake source finite fault model of the main earthquake and the aftershock; 4, a deterministic Green function is obtained through decoupling; and step 5, carrying out convolution on the seismic source finite fault model and the deterministic Green function to obtain a final main shock and aftershock sequence.

The invention provides a prediction model training method, an earthquake prediction method and device and electronic equipment, and relates to the technical field of earthquake monitoring and earthquake prediction. The training method comprises the steps that multiple pieces of aftershock data in historical seismic waveform data of a target area are synthesized with preset noise data, and an earthquake precursor data set is obtained; intercepting a plurality of continuous waveform data containing small seismic group events from the historical seismic waveform data to obtain a random event data set; determining a noise data set by generating a white noise sequence or selecting waveform data of a plurality of noise segments of which the magnitude is below a preset seismic magnitude from historical seismic waveform data; and training the neural network by using the earthquake precursor data set, the random event data set and the noise data set to obtain an earthquake prediction model. According to the invention, the recognition precision of earthquake precursor anomalies is improved.

The invention relates to a physical-data-driven sequence type seismic oscillation generation method. The method comprises the following steps: acquiring a historical seismic record; constructing a main earthquake ground motion random field model based on the physical mechanism of the ground motion field; the physical mechanism comprises a seismic source, a propagation path and a local site model; obtaining main earthquake parameters based on historical earthquake records according to the main earthquake ground motion random field model; the main earthquake parameter is used for predicting an unknown main earthquake seismic record according to a main earthquake seismic oscillation random field model to obtain a main earthquake seismic oscillation time history; and constructing an aftershock seismic oscillation generation model based on a conditional generative adversarial network model according to the main seismic oscillation time history to realize prediction of a seismic oscillation field. According to the method, the parameters of the main earthquake are generated based on the earthquake motion field of the physical mechanism, the time history of the main earthquake is obtained, the aftershock prediction model is constructed according to the conditional generative adversarial network, the physical mechanism model is combined with artificial intelligence, the model prediction fitness and accuracy are improved, the aftershock is predicted, the prediction effect is improved, complex calculation is avoided, and the model construction efficiency is improved.

The invention discloses a main shock and aftershock sequence earthquake response prediction method based on a knowledge enhancement deep learning model, and belongs to the technical field of building structure earthquake response prediction. The method solves the problems that an existing deep learning model is scarce in training data, poor in capacity of capturing long sequence global correlation and incapable of processing input vibration with different lengths and sampling frequencies. The method specifically comprises the following steps: step 1, establishing a target structure finite element model, and selecting and adjusting seismic oscillation records of main and aftershocks; step 2, constructing a training data set based on the finite element model and the seismic oscillation record; 3, building a knowledge enhancement deep learning model comprising a consistency input coding module, an encoder, a decoder and a linear layer, designing a knowledge enhancement loss function, and training the model by using the training data set; and step 4, inputting the actual main shock and aftershock seismic oscillation sequence into the trained model to obtain a structure displacement response and structure restoring force prediction result. The method can be applied to earthquake response prediction.

The invention provides a main shock and aftershock type seismic oscillation dimensionality reduction simulation method based on engineering characteristics, and the method comprises the steps: determining simulation parameters of a main shock, and obtaining corresponding aftershock parameters through the main shock parameters based on an attenuation model of the main shock and aftershock parameters; based on the main earthquake parameters and the aftershock parameters, fully non-stationary evolution power spectrum models of the main earthquake and the aftershock are established respectively, and the fully non-stationary evolution power spectrum models reflect the non-stationary characteristics of earthquake oscillation; discretizing the requirements of the established main shock and aftershock fully non-stationary evolution power spectrum model for random variables by adopting a number theory point selection mode to obtain a representative point set of basic random variables; substituting the representative point set into a random function to obtain an orthogonal random variable set which can be used for simulation; obtaining a representative sample set of the main shock and aftershock process based on an orthogonal random variable set by using a spectrum representation theory; and through the representative sample set, generating seismic oscillation time history samples of the main shock and the aftershock, and outputting a simulation result.

The present invention discloses an excitation method for efficient seismic data acquisition based on time and distance intervals. The method comprises the following steps: constructing a first boundary surface model for zero attenuation of the surface wave aftershocks of the preceding shot based on the records of the shot detection points and the minimum time interval at which the shot point near-track background decays to random noise; calculating the distance between the preceding and succeeding shots using a formula for catching up with the surface wave of the preceding shot based on the length of the detector point arrangement; and constructing a second boundary surface model for catching up with the surface wave of the preceding shot based on the linear relationship between the distance between the preceding and succeeding shots and the time interval between the preceding and succeeding shots; using the overlapping area of the two boundary surface models as the non-interference excitation area of the shot point, the non-interference excitation area encompassing the corresponding non-interference time-distance intervals of all preceding and succeeding shots; and performing shot excitation and acquiring seismic data. The present invention not only overcomes the interference of the preceding shot aftershocks on the succeeding shot, but also eliminates quality risks and can improve blasting efficiency by 20-40%.

The utility model provides a high-stability automobile oil pipe joint, which belongs to the technical field of high-stability automobile oil pipe joints and comprises an oil pipe joint main body, a first joint is embedded in the left side of the oil pipe joint main body, an inner joint is embedded in the right side of the oil pipe joint main body, and a second joint is embedded in the middle of the right side of the inner joint. Through the arrangement of the inner connector, when the oil pipe connector body is subjected to external vibration force, the inner connector can buffer the external vibration force through the damping bent plate between the inner connector and the oil pipe connector body, so that an oil pipe is not prone to loosening after being connected with the second connector, and meanwhile the damping bent plate deforms to buffer the external vibration force; the deformed damping bent plate can extrude the second spring to further improve the shock resistance, and the elastic force of the second spring can play a role in rebound resilience on the deformed damping bent plate, so that after the damping bent plate and the second spring are stressed, aftershock cannot be continuously generated easily, and the shock resistance is improved.

The present application relates to the field of earthquake site rescue warning, and relates to a real-time early warning method for secondary collapse risk of RC frame semi-rubble structure under aftershock. The core content of the method includes four modules. Module 1: using the method of seismic simulation shaking table, the whole process of RC frame structure from perfect to semi-rubble to secondary collapse is realized by simulating main-aftershock action, and time history data of dynamic response of secondary collapse structure is obtained. Module 2: high-dimensional continuous horizontal moving time window method is used to process the time history data of dynamic response of structure and obtain the dynamic response characteristics of RC frame semi-rubble secondary collapse structure. Module 3: K-Means machine learning algorithm is used to cluster the secondary collapse risk type of RC frame semi-rubble structure. Module 4: Informer machine learning algorithm is used to realize real-time early warning of secondary collapse risk of RC frame semi-rubble structure. The present application can scientifically and accurately distinguish and real-time predict the secondary collapse risk of RC frame semi-rubble structure under aftershock.

The application discloses a main aftershock induced slope slip prediction method based on multi-parameter regression, aims to utilize a large amount of ground motion data and a Newmark rigid block method to construct an explicit expression for predicting permanent slip displacement of a slope under the action of a main aftershock from peak ground acceleration, peak velocity and slope yield acceleration. The method comprises the following steps: step 1, selecting ground motion data from a ground motion database NGA-West2 and calculating each ground motion parameter and the slope slip displacement under different slope yield accelerations; step 2, identifying optimal ground motion parameters according to effectiveness criteria and applicability criteria; step 3, obtaining a plurality of prediction models through optimal parameters and a multi-parameter nonlinear regression analysis method and then testing the prediction performance to determine a final slip prediction model; and step 4, predicting the permanent slip displacement of the slope under a given seismic scenario and slope condition. Based on the method, the application develops and discloses a main aftershock induced slope slip prediction model with good accuracy, universality and practicability, and has certain guiding significance for slope stability evaluation and seismic design under the action of a main aftershock.

The utility model discloses an elastic anti-aftershock supporting device for a tunnel lining structure, which relates to the technical field of bridge tunnel construction and comprises a supporting frame, two supporting blocks are fixedly arranged on the supporting frame, and a first servo electric cylinder and two second servo electric cylinders are mounted on each supporting block. The two sides of the supporting frame are each provided with two third servo electric cylinders, the output end of the first servo electric cylinder is connected with a first connecting plate through a fixing block, and the output ends of the two second servo electric cylinders and the output ends of the two third servo electric cylinders are connected with second connecting plates through connecting assemblies. The first connecting plate and the second connecting plate are connected with a top plate through buffer assemblies. Pulleys are installed at the lower end of the supporting frame, and fixing mechanisms are installed on the two sides of the supporting frame. The adjustable tunnel supporting device can be adjusted to support different tunnels, can provide stable supporting force in different environments, can be widely applied to various tunnel projects, and meets different construction requirements.

The present invention discloses an intelligent prediction model for short-term aftershocks of moderate-strong earthquakes based on deep meta-learning, which is characterized by comprising a multi-source earthquake catalog preprocessing model, a dynamic framework model for predicting short-term aftershocks of moderate-strong earthquakes and a deep meta-learning network for aftershock prediction; the multi-source earthquake catalog preprocessing model comprises three parts, namely, removal of duplicate samples in the earthquake catalog, filling of missing fields such as earthquake energy and processing of earthquake sample abnormal values ​​by a box plot method; the dynamic framework model for predicting short-term aftershocks of moderate-strong earthquakes comprises generation of aftershock prediction model training samples, dynamic selection of aftershock prediction model parameters and flexible switching of multiple deep learning algorithms based on meta-learning. The present invention can break through the dependence on the geophysical relationship between main and aftershock parameters in aftershock prediction research, and use AI algorithms to mine and study the complex, abstract and deep correlation relationships in the earthquake catalog.

To solve the problem that it is difficult for residents who cannot live in a house due to an earthquake, a tsunami, a fire, or the like to secure a lodging place for a restoration worker because the residents cannot go to a house with electricity and water while being safe against an aftershock and a tsunami and protecting privacy.SOLUTION: A wooden artificial island is floated on the sea adjacent to the land, a plurality of dwellings are installed on the wooden artificial island, and the wooden artificial island is fixed by setting a concrete guide on the sea adjacent to the land, passing a log connected to the wooden artificial island through the concrete guide, and stopping the wooden artificial island with two wooden stoppers so that the wooden artificial island does not move in the horizontal direction (left, right, front, and back) but can move in the vertical direction to cope with the ebb and flow of the tide.SELECTED DRAWING: Figure 1

The invention relates to a method for inverting a high-resolution P and S wave velocity ratio time sequence based on seismic wave arrival time. The method comprises the following steps: S1, acquiring longitudinal wave and transverse wave arrival time difference data of a seismic pair; s2, carrying out linear relation fitting on longitudinal wave and transverse wave arrival time difference data, and extracting an average wave velocity ratio of an earthquake pair; s3, constructing an initial wave velocity ratio time sequence according to the earthquake generating moment of the earthquake sequence; s4, constructing a time sequence inversion matrix, and performing inversion solution on the time sequence inversion matrix to obtain a wave velocity ratio time sequence; and S5, judging whether the wave velocity ratio time sequence is stable or not, if the wave velocity ratio time sequence is stable, adding one to the unknown number of the time sequence, returning to S3, and if the wave velocity ratio time sequence is unstable, outputting the inverted wave velocity ratio time sequence. According to the method, the dynamic change rule of the wave velocity ratio of a natural seismic zone or an artificial blasting area can be more accurately revealed.

The invention discloses a post-earthquake evacuation method and system based on image analysis, belongs to the technical field of earthquake engineering, and aims to solve the problems of slow response and inaccurate guidance due to the fact that dynamic changes of secondary disasters such as aftershocks and fire disasters cannot be handled in real time by means of manual guidance and traditional emergency broadcast and static evacuation routes. Comprising the steps of obtaining high-definition image data through an unmanned aerial vehicle, a satellite, a camera, a LiDAR and a spectrum sensor in an earthquake influence area, performing multi-dimensional processing on the image data based on a deep learning model, and generating a risk thermodynamic diagram of a dangerous area; through deep analysis of different types of image data, multiple risk sources in the post-earthquake environment can be identified, the dangerous area thermodynamic diagram is generated according to the risk sources, the system can dynamically plan the optimal evacuation path based on the dangerous area thermodynamic diagram, it is ensured that personnel avoid high-risk areas in the evacuation process, and the personnel evacuation efficiency is improved. Therefore, the evacuation efficiency and the personnel safety are improved.

The invention relates to the technical field of earthquake monitoring, in particular to an intelligent earthquake monitoring system based on the Internet of Things. The adaptive optimization module is used for dynamically generating an aftershock probability density field; the adaptive optimization module is used for configuring a stress release relaxation factor based on a sequence active modal index calculated by a kurtosis characteristic value and a skewness characteristic value of probability space distribution in a current time slice to obtain a primary iteration aftershock probability density field; the structure-oriented constraint adjusting module is used for adjusting the structure-oriented constraint weight based on the deviation degree of the main axis direction of the high-probability aftershock cluster and the fault trend of the known region to obtain a secondary iteration aftershock probability density field; and the aftershock risk assessment module is used for determining whether the reliability of the secondary iteration aftershock probability density field is qualified or not based on the width of the confidence interval of the aftershock occurrence rate, and optimizing the preset active index based on an unqualified condition. The aftershock probability prediction precision is improved.

The invention discloses a maximum aftershock prediction method based on online enhancement, and belongs to the technical field of earthquake prediction. The core of the method is that a personalized prediction model is dynamically generated for a current event through online retrieval of similar historical sequences and data enhancement aiming at a newly occurring main earthquake. Firstly, a historical seismic database is constructed from a global seismic directory, and an initial prediction model is trained. When a new main earthquake occurs, an online process is triggered immediately: based on the characteristics of the new main earthquake, K most similar sequences are quickly retrieved from historical data; then, the similar sequence data are enhanced (such as noise adding and interpolation), so that a diversified enhanced training set is constructed; and finally, performing rapid fine adjustment on the initial model by using the enhancement set to obtain an optimization model for the current main earthquake, and predicting the maximum aftershock magnitude. According to the method, the limitation of one-time cutting of the existing static model is overcome, the problem of inaccurate prediction in an earthquake small sample scene is effectively solved, and the accuracy, robustness and timeliness of prediction are remarkably improved.

The application relates to the field of data processing, and discloses a main aftershock sequence time-frequency feature fusion deep dimension reduction simulation system and method, wherein the main aftershock sequence time-frequency feature fusion deep dimension reduction simulation method comprises the following steps: adaptively segmenting original waveform data from multiple seismic monitoring nodes according to a non-fixed window; performing cross-window aggregation on local amplitude anomalies and burst energy features existing in preliminary sequence representation; dynamically mapping potential aftershock signals and main shock features by constructing a bidirectional association network; iteratively fusing feature differences in a response evolution diagram and node distribution information; introducing a hierarchical dimension reduction processing mechanism based on a digital constraint model, and performing selective compression on signal modes of different granularities; and dynamically comparing and weighting integrating sequence features of different time segments by using an index system. The application has the advantage of improving the real-time performance of large-scale seismic data processing.

This application belongs to the field of earthquake assessment technology, and in particular relates to a method and system for dynamic assessment of earthquake impact fields based on multi-source information. This method utilizes real-time observation data from various specialized seismic sensors to dynamically assess the earthquake impact field over time periods. It also integrates multi-source information, such as basic earthquake parameters, focal mechanism solutions, focal rupture processes, station ground motion parameters, and aftershock information, to comprehensively assess and correct the earthquake impact field.

Method for predicting dynamic response of suspension bridge under main shock and aftershock driven by physical and data hybrid, which relates to the technical field of earthquake response prediction of suspension bridges. Determine the acceleration time history sequence of main shock and aftershock ground motions; for an anchored steel truss suspension bridge, considering the uncertainty of key parameters, establish a finite element model; collect the bridge output parameters, establish a data set and divide it into a training set and a validation set; normalize the data; establish a physical constraint equation and solve the longitudinal displacement wave at the mid-span of the main girder under physical constraints; construct a machine learning model using the training set based on ANN and LSTM; establish a loss function for the machine learning model; verify the machine learning model; use the machine learning model to predict the longitudinal displacement wave at the mid-span of the main girder. It can calculate the displacement time history considering the uncertainty of key parameters and under the action of main shock and aftershock ground motions, and make up for the adverse effects caused by the traditional prediction method due to the inability to consider the uncertainty of key parameters and the aftershock stage.

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.