46 results about "Seismic engineering" patented technology

The invention discloses a buried pipeline earthquake vulnerability analysis method based on three-parameter square normal distribution, belongs to the field of civil engineering and earthquake engineering, and solves the problem of failure probability calculation deviation caused by the fact that an existing logarithmic normal distribution model cannot accurately reflect skewness characteristics of structural response data. The method comprises the steps of generating seismic oscillation input data, establishing a buried pipeline finite element model, performing nonlinear time-history analysis, extracting pipeline response data, calculating buried pipeline failure probabilities based on three-parameter square normal distribution under different seismic oscillation intensities, and drawing a vulnerability curve. According to the method, a complete system covering seismic oscillation simulation, buried pipeline response analysis and seismic vulnerability analysis is constructed, and the calculation precision of the buried pipeline seismic failure probability is improved by introducing three-parameter square normal distribution capable of accurately reflecting the structure response data skewness characteristics; therefore, an earthquake vulnerability model which is wider in application range and higher in precision is provided.

The invention provides a site classification method and system based on a regional gradient-Vs30 mapping model, electronic equipment and a storage medium, and relates to the technical field of seismic engineering and site classification.The method comprises the steps that a grid where a drilling point in a to-be-measured area is located is used as a center pixel, a nine-square window is constructed, and a Vs30 mapping model is constructed; and calculating elevation change rates of the central pixel and the adjacent pixels in x and y directions based on the digital elevation data, and further obtaining a terrain slope value through a slope formula. A slope value is converted into a soil layer shear wave velocity predicted value by utilizing a pre-constructed regional gradient-Vs30 mapping model, and the model is optimized through local drilling data, so that the regional applicability is improved. And finally, according to the mapping relationship between the interval and the site category, classifying the predicted values and generating a site classification spatial distribution diagram. According to the method, the site classification precision and the engineering applicability are improved.

This invention provides a prototype pipe pile shaking table test system for simulating the effects of foundation and superstructure, belonging to the fields of structural engineering, earthquake engineering, and foundation engineering. It includes a shaking table, a prototype pipe pile specimen horizontally arranged on the shaking table surface, a foundation action simulation component, a pile top axial compression simulation component, a pile cap simulation component, and a superstructure dynamic simulation component. The foundation action simulation component is fixed to the shaking table surface and covers the outside of the prototype pipe pile specimen. The pile top axial compression simulation component penetrates the inside of the prototype pipe pile specimen and extends to the outside of the pile bottom. The pile cap simulation component is connected to the top of the prototype pipe pile specimen, and the superstructure dynamic simulation component is positioned above the pile cap simulation component. This invention, using the above-mentioned test system, solves the problems of existing prototype pipe pile shaking table tests, such as large space requirements, high cost of foundation simulation, difficulty in loading real superstructures, inconvenient adjustment of working conditions, difficulty in comprehensively simulating pile-soil-structure interactions, and inability to accurately measure dynamic changes in pile axial force.

The application discloses a kind of based on spatial intelligence's active fault three-dimensional model automatic construction method, belong to geological exploration and earthquake engineering technical field;Method includes: collection data, analysis and extract minimum complete subdirectory;Through adaptive threshold hierarchical clustering combined with improved RANSAC algorithm, the exclusive small earthquake cluster of each fault is automatically identified;Based on local weighted regression, three-dimensional automatic slice is made to small earthquake cluster, and each profile fault interpretation line is fitted by moving least square method;Finally, active fault three-dimensional fine model is constructed;Test model rationality and output model file;The application realizes the automation, quantification of fault modeling whole process, can be fused multi-source data and realize multi-element constraint modeling, adapt complex fault zone modeling, and the fine three-dimensional model constructed can provide key data support for fault present-day deformation inversion, three-dimensional potential source research, earthquake geological disaster assessment, with higher engineering application value.

The application discloses a kind of based on visual element's floating non-structural member earthquake damage identification method, belong to the technical field of earthquake engineering;Including the following steps: with double flow length time sequence convolution video understanding model as foundation to build the earthquake damage identification system of floating non-structural member, the model is composed of three-channel convolutional neural network and bidirectional long time sequence prediction model;Spatial time sequence information is extracted using three-channel convolutional neural network, and the feature map and time sequence information are built using bidirectional long time sequence prediction model;Finally, the feature fusion of two kinds of ways is carried out, so as to realize the class prediction and damage state identification of floating non-structural member in earthquake damage video.The application method combines double flow video understanding model and long time sequence convolution processing model, utilizes the feature fusion of spatial information and time sequence information, strengthens the information sharing and fusion between each video frame, and makes more accurate judgment on the post-disaster situation of building.

The invention discloses a complex terrain seismic oscillation amplification coefficient prediction and correction method and system based on a convolutional neural network model, and belongs to the application of a deep learning method in the field of seismic engineering. The method comprises the following steps: firstly, respectively establishing seismic oscillation databases of isolated terrains and regional complex terrains; training by using a BP neural network to obtain an isolated terrain seismic oscillation amplification coefficient prediction model, and training by using a convolutional neural network to obtain a regional terrain influence coefficient prediction model; and multiplying the isolated terrain prediction coefficient of the to-be-measured site by the regional terrain influence prediction coefficient to obtain an accurately corrected seismic oscillation amplification coefficient. The method overcomes the defect that an existing method neglects the regional terrain coupling effect, effectively improves the prediction precision of the seismic oscillation amplification coefficient under the complex terrain condition, and provides more reliable technical support for engineering aseismic design and seismic risk assessment.

The invention relates to the technical field of seismic engineering and artificial intelligence, in particular to a seismic oscillation selection method and system based on spectral feature extraction and clustering, and discloses a seismic oscillation record selection method based on principal component analysis dimensionality reduction and K-means clustering. The method comprises the following steps: firstly, acquiring candidate records from a database according to set seismic parameters of a site, and performing pulse and non-pulse label division; amplitude modulation and normalization preprocessing are carried out on the response spectrum data, and a high-dimensional acceleration response spectrum is compressed into low-dimensional potential features through PCA dimension reduction; and finally, on the basis of a dimension reduction result, performing grouping by applying a K-means clustering method, and performing record selection and response spectrum comparison. The system comprises a record reading and processing module, a feature extraction module, a feature clustering module, a verification module and an output module, velocity pulse and non-pulse classification is carried out before an alternative record data set is constructed, and a near fault velocity pulse effect is considered at the beginning of record selection.

This invention discloses a machine learning-based method for predicting earthquake-induced sand liquefaction deformation, relating to the fields of earthquake engineering and machine learning. The method constructs a multi-source coupled earthquake dataset using engineering survey reports and monitoring data from historical earthquake cases. This dataset undergoes missing value processing, outlier detection, feature selection, and standardization to obtain an earthquake-induced sand liquefaction deformation dataset. Based on this dataset, a prediction model is constructed. The root mean square error, mean absolute error, and coefficient of determination of the model are calculated. Based on corresponding thresholds, the model is adjusted for hyperparameters using Bayesian optimization or encapsulated using Flask. New earthquake-induced sand liquefaction feature data is then processed and input into the encapsulated model to obtain the predicted earthquake-induced sand liquefaction deformation results. This method can effectively predict earthquake-induced sand liquefaction deformation, providing support for earthquake disaster prevention and engineering design.

The invention discloses a near-fault multi-point multi-dimensional fully non-stationary seismic oscillation dimension reduction simulation method and system, and belongs to the technical field of seismic engineering and disaster prevention and reduction. The method provided by the invention comprises the following steps: firstly, screening near-fault strong earthquake records containing horizontal and vertical velocity pulses, fitting the extracted pulses by using a Gabor wavelet model, identifying low-frequency key parameters and establishing probability distribution; a high-frequency component is simulated by a 1D-nV non-stationary random vector process, and dimensionality reduction is carried out in combination with a random orthogonal function; and generating a representative point set through number theory point selection and equal probability inverse transformation, substituting the representative point set into the model to generate high and low frequency speed time histories, and superposing the high and low frequency speed time histories to obtain a seismic oscillation sample with complete probability information. The system comprises a data screening module, a parameter identification module and the like. According to the method, samples meeting the precision are generated by using extremely few random variables, the simulation efficiency is improved, and the method can be combined with the probability density evolution theory for engineering structure dynamic response and anti-seismic reliability analysis.

The invention discloses a site differentiation earthquake response analysis method and system based on earthquake motion types, and belongs to the technical field of building earthquake-proof engineering, and the method comprises the steps: dividing a target site into three types of sites according to the obtained drilling information of the target site, and constructing a site model corresponding to each type of site; respectively calculating dynamic response results of each site model under the near-field pulse type seismic motion and the near-field non-pulse type seismic motion by adopting a time domain nonlinear analysis method; generating two groups of surface response spectrum amplification coefficients according to the dynamic response result of each site model, and comparing peak values with periods; and according to a comparison result of the peak value and the period, combining each site model to obtain a differential earthquake response analysis result. According to the method, the significant amplification effect of pulse type seismic oscillation on a soft soil site and the significant amplification effect of non-pulse type seismic oscillation on a medium-hard soil site are effectively revealed, and the problem that the difference of seismic oscillation types is not fully considered in existing analysis is solved.

The invention discloses an FE-VFIFE hybrid calculation method for nonlinear dynamic analysis of a complex structure, which is suitable for the fields of seismic engineering, ocean engineering and the like which require real-time simulation and analysis of the complex structure, the FE and the VFIFE are combined, dimension reduction of each linear substructure is realized through a modal superposition method in a conventional finite element, and the nonlinear dynamic analysis of the complex structure is realized. The whole system is decoupled into a series of modal equations, and the calculation amount is reduced; vector type finite elements are used for calculating the internal force of the nonlinear part, the situation that a global stiffness matrix is repeatedly updated in the calculation process is avoided, meanwhile, a Chang method is adopted for unconditional stable integration, and mass and damping correction factors are introduced to improve the calculation precision, increase the integration step length and improve the calculation efficiency. The method can be efficiently applied to real-time hybrid simulation of the local nonlinear problem of a complex structure system, and powerful support is provided for performance evaluation and design optimization of the complex structure under the action of complex dynamic loads such as earthquakes and marine environments.

A speed pulse recognition method based on zero-crossing closing interval and decoupling criterion belongs to the field of earthquake engineering and seismic signal processing, and comprises the following steps: step 1, speed time history acquisition and pretreatment; step 2, candidate interval of closing pulse is constructed; step 3, energy calculation and candidate screening; step 4, global energy concentration window construction and screening: cumulative energy curve is constructed, and consistent constraint screening is carried out based on the global energy concentration window; step 5, adjacent interval continuity discrimination and boundary marking: the continuity of adjacent intervals is discriminated, and the decoupling and to-be-merged boundary are marked; step 6, merging and reorganization iteration and pulse group generation; step 7, energy concentration review and determination output: the concentration is reviewed, and the pulse type and key parameters are output. And a speed pulse recognition device based on zero-crossing closing interval and decoupling criterion is provided. The present application improves the reliability and engineering applicability of the pulse recognition result under complex seismic motion conditions.

The utility model relates to the technical field of structure anti -seismic engineering discloses a kind of intercolumnar energy dissipation shock absorber for silo, including base, the top of base is slidably connected with base, and self-adapting buffer assembly is arranged between base and base, multiple support columns are evenly arranged on the side of base away from base, symmetric shock absorber assembly and tensile energy-absorbing component are arranged between a pair of support columns, and fixed component is arranged in the middle of support column, symmetric shock absorber assembly includes box one, and box one is assembled in the middle of support column, and box two is slidably connected in the inside of box one. By the cooperation of support column, box one, box two, box three, lead to groove, wave buffer piece, honeycomb elastic sheet and other structures, convert seismic energy into deformation energy and heat energy inside material, multidirectional reduce vibration impact, ensure support column stability, and then stabilize the overall structure of silo.

The invention is suitable for the technical field of structural seismic engineering, and provides a construction method, device and equipment of a wading underground structure seismic response prediction model, and the method comprises the steps: firstly, generating a seismic response database based on a simulation result of a structure-soil-fluid coupling finite element model; then, based on a plurality of seismic oscillation sample feature vectors extracted from the plurality of seismic oscillation sample data, performing clustering analysis on the plurality of seismic oscillation sample data to obtain a training data set; and finally, taking the target seismic oscillation sample data and the water depth parameter in the training sample data as input data, taking the seismic response data of a plurality of monitoring points in the training sample data as labels of the input data, training the initial seismic response prediction model, and obtaining a wading underground structure seismic response prediction model. Through the method, the prediction precision and prediction efficiency of the multi-monitoring-point earthquake response of the wading underground structure are improved.

This invention, a method for identifying seismic liquefaction of sand based on the spectral analysis of seismic acceleration data, belongs to the field of seismic engineering. It aims to solve the problems of low efficiency, high cost, and low accuracy in distinguishing between non-liquefiable and liquefiable soft soil sites using traditional methods. The invention collects bidirectional horizontal seismic acceleration time-history data, dividing it into training and testing sets. It extracts peak ground acceleration (PGA) and uses a liquefaction trigger threshold for initial identification; sites not reaching the threshold are directly classified as non-liquefiable. Once the threshold is reached, the data is segmented based on the first time the threshold is reached, and the spectrum is obtained through bandpass filtering and Fourier transform. The differences in the proportion of low-frequency components and the differences in characteristic frequencies are calculated. Using the two features as input, a discrimination model is constructed using logistic regression and maximum likelihood estimation, and the decision boundary is determined. The accuracy is verified using a test set. This method requires only acceleration time-history data, has low discrimination cost and high efficiency, effectively distinguishes between non-liquefiable and liquefiable soft soil sites, has strong anti-interference capabilities and high accuracy, and is suitable for rapid assessment of sand liquefaction after strong earthquakes and for seismic engineering applications.

The application discloses a buried pipeline seismic vulnerability analysis method based on a three-parameter square normal distribution, belongs to the field of civil engineering and earthquake engineering, and solves the problem that an existing lognormal distribution model cannot accurately reflect the skewness characteristics of structural response data, thereby leading to the calculation deviation of failure probability, and the method comprises the following steps: generating seismic input data, establishing a buried pipeline finite element model, performing nonlinear time-history analysis, extracting pipeline response data, calculating the failure probability of the buried pipeline under different seismic intensity based on the three-parameter square normal distribution, and drawing a vulnerability curve; the application builds a complete system covering seismic simulation-buried pipeline response analysis-seismic vulnerability analysis, improves the calculation precision of the seismic failure probability of the buried pipeline by introducing the three-parameter square normal distribution which can accurately reflect the skewness characteristics of the structural response data, and thus a seismic vulnerability model with wider application range and higher precision is provided.

The present application belongs to the field of earthquake engineering and civil engineering, and particularly relates to a method for quickly formulating a demolition scheme of a damaged building based on a genetic algorithm and reinforcement learning. The method comprises the following steps: S1: establishing a data information base of a target damaged building to be demolished, mainly including various building structure types, structure plane arrangement, building site categories, and beam column member sizes; S2: establishing a finite element model for structure progressive collapse analysis according to the collected information; S3: determining the range of members allowed to be demolished during the collapse analysis of the demolished structure according to the collected information; S4: calculating a single demolition effect according to the finite element model for progressive collapse analysis; and S5: selecting a genetic algorithm or a Q-learning reinforcement learning algorithm according to whether the influence of the demolition sequence needs to be considered in actual conditions to obtain an optimal demolition scheme. The present application can be used for quickly formulating a demolition scheme of a severely damaged building in post-earthquake repair and reconstruction.

The invention relates to the technical field of earthquake engineering, in particular to an earthquake vibration model construction and evaluation method based on big data. Comprising a hardware and rule component and a platform architecture component (a relational / non-relational database, a data warehouse, a metadata center, an association rule / clustering algorithm module, an LSTM / GRU earthquake risk prediction model, a resource scheduling model and a scientific research / emergency / public differentiation application function module), the model construction assembly comprises a finite difference / finite element multi-scale site model, a building group / power transmission tower structure-site coupling sub-model, a seismology-deep learning mixed training framework and a model parameter staged correction module.

The application discloses a fault information independent near-fault pulse type ground motion selection method, belonging to the field of earthquake engineering and structural seismic analysis, and comprises the following steps: step 1, firstly, according to a target risk level, a target peak ground acceleration PGA of a site is determined through a ground motion parameter zoning map; a PGV / PGA recommended value is obtained by inquiring a pre-established PGV / PGA recommended value table according to the PGA value; and secondly, ground motion selection is carried out for the target site; step 2, based on a conditional parameter, it is assumed that the conditional parameter and a spectrum shape parameter joint vector conform to a multivariate normal distribution in a logarithmic space, and the conditional mean vector and the conditional covariance matrix of the four spectrum shape parameters are calculated from a ground motion database as target statistical characteristics by utilizing the conditional distribution properties; and step 3, a two-stage optimization algorithm is adopted to select matched ground motion. The application is free from fault information dependence, effectively incorporates the pulse effect, has strong practicability, is simple to operate, and has good risk consistency.

The utility model relates to the technical field of house corner clamps, and discloses a house reinforcing quakeproof corner clamp which comprises two vertical plates, the tops of the two vertical plates are fixedly connected with mounting plates, connecting mechanisms are arranged in the mounting plates, supporting mechanisms are arranged at the bottoms of the vertical plates, reinforcing mechanisms are arranged at the tops of the supporting mechanisms, and the reinforcing mechanisms are arranged on the tops of the supporting mechanisms. A welding mechanism is arranged at the top of the vertical plate; the supporting mechanism comprises two connecting plates, the tops of the two connecting plates are slidably connected to the bottoms of the two vertical plates correspondingly, a transverse plate is fixedly connected to the bottoms of the two connecting plates, and two connecting bolts are connected to the interior of the transverse plate in a threaded mode. According to the utility model, the corner clamp becomes a standardized module through the synergistic effect of all the mechanisms, modular connection and installation can be directly carried out in the house reinforcing and earthquake-proof engineering, complicated field processing and adaptation are not needed, the installation efficiency and effect are greatly improved, and the installation difficulty and cost are reduced.

The application relates to a post-earthquake traffic system function loss evaluation method based on road width and belongs to the field of earthquake engineering. The application is to solve the problem that the analysis method of emergency connectivity in the existing post-earthquake traffic system operation function evaluation cannot judge the seismic resilience level of the traffic system. The post-earthquake traffic system function loss evaluation method based on road width is characterized in that: firstly, redundant independent paths between each starting node and each target node in the pre-earthquake path network are removed, and the remaining independent paths are searched; then, the function loss of the post-earthquake emergency stage path network is analyzed to update the width, length and number of each road section in the post-earthquake path network, the independent paths are searched again, and the path information of each independent path after the earthquake is obtained; finally, the emergency function indexes of the pre-earthquake and post-earthquake urban road traffic systems are calculated by using the path information of each independent path before and after the earthquake, and then the function loss of the urban road traffic system in the post-earthquake emergency stage is calculated and obtained.

The invention relates to the technical field of seismic engineering, in particular to a multi-condition seismic oscillation generation method based on a diffusion model, which comprises the following steps: firstly, extracting multi-dimensional condition parameters such as a response spectrum, a peak acceleration, Arias intensity, energy arrival time, significant duration and a Husid curve from a seismic oscillation acceleration time history, and carrying out normalization processing; the acceleration time history is converted into a normalized logarithmic magnitude spectrum through short-time Fourier transform to serve as a learning target; constructing a Transform condition encoder to capture a coupling relationship of multi-source conditions, designing a de-noising network based on U-Net, and integrating a cross attention mechanism to realize accurate condition injection; a denoising diffusion probability model training strategy is adopted to optimize the noise prediction network; during reasoning, a denoising diffusion implicit model sampling strategy is adopted, phase information is iteratively reconstructed from an amplitude spectrum through a Griffin-Lim algorithm, and a seismic oscillation acceleration time history is obtained through inverse transformation. According to the invention, cooperative accurate control of the response spectrum and the energy non-stationary characteristic is realized, training is stable, reasoning is efficient, and diversity generation is supported.

The present invention relates to the field of earthquake engineering technology, and in particular to a novel physical information neural network (gs-PINN) for solving seismic wave equations. The gs-PINN comprises the following steps: first, establishing the novel physical information neural network; second, dividing the configuration points required for network training; third, determining the form of the incident wave; fourth, conducting network training; and fifth, obtaining a solution to the one-dimensional seismic wave equation. The gs-PINN comprises two sub-networks, wherein the parameters of the first hidden layer of each sub-network are non-trainable parameters whose values ​​are determined by the propagation direction of the seismic wave, and the remaining parameters are set as trainable parameters, thereby ensuring that the network itself automatically satisfies the one-dimensional seismic wave equation, effectively preventing the network from falling into a local optimum, significantly reducing the training difficulty, and providing strong technical support for solving the one-dimensional seismic wave equation.

This invention discloses a hybrid FE-VFIFE calculation method for nonlinear dynamic analysis of complex structures, applicable to fields such as earthquake engineering and marine engineering that require real-time simulation and analysis of complex structures. This method combines FE and VFIFE, using the modal superposition method in conventional finite element methods to reduce the dimensionality of each linear substructure, decoupling the entire system into a series of modal equations and reducing computational load. Vector finite element methods are used to calculate the nonlinear internal forces, avoiding repeated updates to the global stiffness matrix during calculation. The Chang method is employed for unconditional stability integration, and mass and damping correction factors are introduced to improve computational accuracy, increase the integration step size, and enhance computational efficiency. This invention can be efficiently applied to real-time hybrid simulation of local nonlinear problems in complex structural systems, providing strong support for performance evaluation and design optimization of complex structures under complex dynamic loads such as earthquakes and marine environments.

The invention provides a post-earthquake building structure damage rapid assessment method and system, and relates to the technical field of earthquake engineering, and the method comprises the following steps: S1, obtaining first acceleration data of a building structure bottom layer when ground vibration acceleration is input, and second acceleration data of a building structure top layer when ground vibration acceleration is input, preprocessing the first acceleration data and the second acceleration data to form two-dimensional input data; s2, constructing and training an expansion residual convolutional neural network model; s3, inputting the two-dimensional input data into an expansion residual convolutional neural network model for processing, and outputting the damage level of the building structure; the problems of high dependence on the number of sensors, limited feature extraction capability, insufficient damage prediction precision and the like in the prior art are solved.

This invention proposes a method and apparatus for identifying ground motion pulses based on a hybrid convolutional and self-attention neural network, belonging to the interdisciplinary field of earthquake engineering and artificial intelligence. It addresses the technical problems of low accuracy and low data efficiency in the automatic identification of pulse-type ground motions in existing technologies. The method includes: acquiring raw ground motion velocity time-history records; constructing a CVT hybrid model and processing the two-dimensional waveform image to obtain a trainable model; training the model: optimizing the CVT hybrid model using a labeled smoothed cross-entropy loss function combined with L2 weight regularization; employing the AdamW optimizer with a cosine annealing learning rate scheduling strategy; performing iterative training; evaluating the trained model to obtain a validated CVT hybrid model that can distinguish ground motion pulse types; performing pulse type discrimination and outputting classification results representing pulse-type and non-pulse-type ground motions. This invention is applied to the automatic identification of near-fault pulse-type ground motions.

The present invention relates to a method of designing a stability and earthquake-resistant engineering structures for use in calculating a resultant earthquake force Q at a horizontal connecting plane (3) for designing of a foundation structure (2) and inertial force F 1 for dimensioning a building structure (1) and for evaluating earthquake resistance of existing structures. For designing of the foundation structure (2), a resistive force R Q 2 at the horizontal connecting plane (3) needs to be determined, which is necessary for preventing failure of the foundation structure (2) leading to failure of a building structure (1). For dimensioning of the building structure (1), the resistive force R Q 1 needs to be determined to ensure earthquake resistance of the building structure (1). Determination of specific parameters of underground sections, the Retaining Engineering Structure and the group of piles provides a means for ensuring earthquake resistance. The horizontal connecting plane (3) is arranged between the building structure (1) and the foundation structure (2).

The application discloses a self-resetting structure opening and closing behavior simulation method and device, equipment, medium and product, and relates to the field of structural engineering and anti-seismic technology. The method first discretizes the self-resetting structure into multiple regions, establishes an equivalent model of each region, obtains multiple units and multiple nodes, sets a constitutive model of each unit, establishes a control equation of each node by using a vector finite element method, and simulates the opening and closing behavior of the self-resetting structure by using a vector finite element linking unit based on the constitutive model of each unit and the control equation of each node. The application applies the vector finite element linking unit to describe the node opening and closing behavior of the beam-column connecting region, can efficiently simulate the node opening and closing behavior of the self-resetting structure, significantly improves the calculation precision and stability, and is suitable for the design and analysis of the self-resetting structure in seismic engineering.

The invention discloses a tunnel disaster monitoring method and blocking method based on earthquake damage-flood cooperation, and relates to the field of earthquake engineering and intelligent disaster prevention and control, and the method comprises the steps: carrying out the decoupling based on the DOFS data of a section, and obtaining a damage feature, a seepage feature and an aftershock feature; simulating to obtain water pressure data according to the multi-source sensing data in the section; based on each feature and historical data, determining a current damage variable, a damage-seepage coupling index and a fracture hydraulic opening degree; according to the water pressure value of each section, the seepage pressure gradient between the sections is determined, and the seepage velocity vector between the sections is determined by combining the fracture hydraulic opening degree and the damage-seepage coupling index of the sections; determining a system cascade failure risk index according to the damage variables of the sections and the dynamic risk propagation weight between the sections; and determining a disaster chain propagation intensity index of the current tunnel by using an entropy weight method. According to the invention, real-time monitoring and risk assessment of secondary flood disasters after tunnel earthquakes can be realized.

The invention discloses a large-displacement large-energy-dissipation-capacity large-tonnage sliding type steel plate damper, and relates to the technical application field of civil engineering and earthquake engineering. The device comprises a top plate, a bottom plate, two groups of upper energy dissipation steel plates, two groups of lower energy dissipation steel plates and pin shafts, two groups of upper energy dissipation steel plates are arranged at the bottom of the top plate at intervals; the two lower energy dissipation steel plates are attached to the top of the bottom plate. The pin shaft penetrates through the upper energy dissipation steel plate sliding groove and the lower energy dissipation steel plate sliding groove and is connected with the upper energy dissipation steel plate sliding groove and the lower energy dissipation steel plate sliding groove in a sliding mode, and force application and transmission rings are arranged at the two ends of the pin shaft. The force application and transmission ring is attached to the upper energy dissipation steel plate. The sliding steel plate damper is designed according to the characteristic that the bending moment of the inflection point of the frame structure is zero, the inflection point is designed to be in sliding connection with the energy dissipation steel plate, and the large displacement capacity is achieved on the basis of smooth connection and force transmission driving of the upper steel plate and the lower steel plate.