A method and system for analyzing seismic toughness of segmental assembled railway high pier nodes

Abstract

The present invention provides a method and system for analyzing the seismic toughness of segmented prefabricated railway high pier nodes, relating to the field of computer technology. The method comprises: obtaining basic parameters of a target segmented prefabricated railway high pier node, including material parameters, pier geometry parameters, seismic motion parameters, and node response parameters; performing multi-physics field coupling tensor decomposition based on the basic parameters to obtain a multi-field coupling characteristic tensor; performing topological modeling based on the multi-field coupling characteristic tensor to obtain a time-varying graphical structural model; performing damage evolution simulation based on the time-varying graphical structural model to obtain a quantitative seismic toughness index; performing modal sensitivity analysis and failure path extraction based on the quantitative seismic toughness index to obtain a failure mode analysis result; and performing an evaluation based on the failure mode analysis results to obtain a node seismic toughness analysis result. By introducing multi-physics field coupling tensor decomposition and a time-varying graphical structural model, the present invention can more accurately simulate the response of nodes in complex seismic environments.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for analyzing the seismic toughness of segment-assembled railway high pier nodes. Background Art

[0002] Segmental prefabricated railways, a new structural form in modern railway construction, have been widely used in recent years. Their key feature is on-site assembly through prefabricated segments, significantly improving construction efficiency and precision while reducing environmental impact during construction. This structural form is particularly suitable for areas with complex terrain and rapid construction. However, due to the structural characteristics and node connection methods of segmental prefabricated railways, they also have relatively high requirements for seismic performance. Especially in earthquake-prone areas, the seismic toughness of high-pier railway nodes becomes a critical factor affecting their safety and stability. Therefore, seismic toughness analysis of nodes is particularly important, as it can effectively assess and improve the load-bearing capacity and durability of structures under earthquake loads.

[0003] Currently, seismic resilience analysis of segmental prefabricated railway pier joints primarily relies on mechanical models and simplified calculation methods. These existing approaches often only consider the elastic properties of the material and the geometry of the joint, failing to fully account for the multi-physics coupling of seismic motion and the nonlinear characteristics of the joint response. Existing technologies typically employ elastic mechanics or linear statics analysis, ignoring the time-varying nature of seismic motion, the nonlinear behavior of the joint, and the cumulative effects of seismic energy. Consequently, they are unable to provide accurate seismic performance assessments in practical applications, and often underestimate the risk of joint damage and failure, particularly under strong earthquakes.

[0004] Based on the above shortcomings of the existing technology, there is an urgent need for a method and system for analyzing the seismic toughness of segmental prefabricated railway high pier nodes. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for analyzing the seismic toughness of segmented prefabricated railway high pier nodes to improve the above-mentioned problems. To achieve the above-mentioned purpose, the technical solutions adopted by the present invention are as follows:

[0006] In a first aspect, the present application provides a method for analyzing the seismic toughness of segmental prefabricated railway high pier nodes, comprising:

[0007] Obtaining basic parameters of the target segment prefabricated railway high pier node, wherein the basic parameters include material parameters, pier body geometry parameters, earthquake motion parameters, and node response parameters;

[0008] Decomposing the multi-field coupling tensor according to the basic parameters to obtain a multi-field coupling characteristic tensor;

[0009] Performing topological modeling based on the multi-field coupling characteristic tensor to obtain a time-varying graph structure model;

[0010] Perform damage evolution simulation based on the time-varying graph structural model to obtain a quantitative index of seismic toughness;

[0011] According to the quantitative index of seismic toughness, the failure mode analysis results are obtained through modal sensitivity analysis and failure path extraction;

[0012] An evaluation is performed based on the failure mode analysis results to obtain node seismic toughness analysis results.

[0013] In a second aspect, the present application also provides a segmental prefabricated railway high pier node seismic toughness analysis system, comprising:

[0014] An acquisition module is used to acquire basic parameters of the target segment prefabricated railway high pier node, wherein the basic parameters include material parameters, pier body geometry parameters, earthquake motion parameters and node response parameters;

[0015] A decomposition module, configured to decompose the multi-field coupling tensor according to the basic parameters to obtain a multi-field coupling characteristic tensor;

[0016] A modeling module, configured to perform topological modeling based on the multi-field coupling characteristic tensor to obtain a time-varying graph structure model;

[0017] A simulation module, configured to simulate damage evolution according to the time-varying graph structural model to obtain a quantitative index of seismic toughness;

[0018] An analysis module is used to obtain failure mode analysis results based on the seismic toughness quantitative index through modal sensitivity analysis and failure path extraction;

[0019] The evaluation module is used to evaluate according to the failure mode analysis results to obtain the node seismic toughness analysis results.

[0020] The beneficial effects of the present invention are:

[0021] By introducing multi-physics field coupling tensor decomposition and time-varying graphical structure models, the present invention can more accurately simulate the response of nodes in complex seismic environments, comprehensively consider the coupling effects of material nonlinearity, geometric deformation, and the short-term impact and long-term cumulative effects of seismic motion, and combine failure mode analysis with damage evolution simulation, significantly improving the accuracy and reliability of seismic toughness analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 A schematic flow chart of a segmental assembled railway high pier node seismic toughness analysis method according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic structural diagram of a seismic toughness analysis system for segmental assembled railway high pier nodes according to an embodiment of the present invention;

[0025] Figure 3 The figure is a schematic structural diagram of a segment-assembly-type railway high pier node seismic toughness analysis device described in an embodiment of the present invention.

[0026] Markings in the figure: 800, a segmental prefabricated railway high pier node seismic toughness analysis device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, decomposition module; 903, modeling module; 904, simulation module; 905, analysis module; 906, evaluation module. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance. Example 1:

[0029] This embodiment provides a method for analyzing the seismic toughness of segmentally assembled railway high pier nodes.

[0030] See also Figure 1 , the figure shows that the method includes steps S100 to S600.

[0031] Step S100: obtaining basic parameters of the target segment prefabricated railway high pier node, the basic parameters including material parameters, pier body geometry parameters, earthquake motion parameters and node response parameters;

[0032] It is understood that material parameters primarily include the mechanical properties of components such as concrete and steel bars, such as elastic modulus, yield strength, compressive strength, and nonlinear behavior. Pier geometry parameters include information such as pier height, pier width, node shape, and assembly method, which directly affect the stiffness and stability of the structure. Seismic motion parameters include the amplitude, frequency, duration, and spectral characteristics of seismic motion. Node response parameters include the displacement, stress distribution, and damage status of nodes under different loads. These parameters can truly reflect the dynamic response of nodes under earthquake action.

[0033] Step S200: Decomposing the multi-field coupling tensor according to the basic parameters to obtain the multi-field coupling characteristic tensor;

[0034] In earthquake engineering, the response of a structure is often influenced not only by a single physical field (such as mechanics), but also by interactions with multiple physical fields, including temperature, strain, and seismic motion. The interactions between these physical fields are complex, and traditional analysis methods often fail to fully account for these multiple coupling effects. However, the multi-physics coupling tensor decomposition method simultaneously addresses the interrelationships between multiple physical fields, synthesizing them into a high-dimensional tensor representation that accurately captures the coupling characteristics between these fields.

[0035] Step S300: topological modeling is performed according to the multi-field coupling characteristic tensor to obtain a time-varying graph structure model;

[0036] It can be understood that topological modeling is to express the physical characteristics, responses and earthquake motion effects of nodes in a graph-structured manner. The resulting time-varying graph structure model can accurately represent the dynamic behavior of nodes under earthquakes. Preferably, by combining graph theory and convolutional networks, the model can not only capture the state of nodes at different time points, but also flexibly simulate the impact of earthquake motion on node connection relationships. The use of time-varying graph structures makes seismic analysis more comprehensive and dynamic, and can effectively reflect the stress and deformation process of nodes under strong earthquakes, providing a reliable foundation for subsequent damage evolution simulation.

[0037] Step S400: performing damage evolution simulation according to the time-varying graph structural model to obtain a quantitative index of seismic toughness;

[0038] It's important to note that damage evolution simulation not only accurately describes the dynamic damage process of a node but also quantifies its seismic resilience from multiple dimensions. Compared to traditional seismic analysis methods, this comprehensive assessment approach, based on a time-varying graphical structural model and damage evolution simulation, more comprehensively reflects the true performance of a node under complex earthquakes. Ultimately, the quantitative seismic resilience index provides solid data support for subsequent failure mode analysis, structural optimization, and seismic design, ensuring that the node maintains a higher level of safety and durability during earthquakes.

[0039] Step S500: Obtain failure mode analysis results through modal sensitivity analysis and failure path extraction based on the seismic toughness quantitative index;

[0040] It's clear that modal sensitivity analysis and failure path extraction can clearly reveal the potential failure modes a node might experience under earthquake conditions. Quantifying these modes allows for the early identification of potential structural weaknesses. Compared to traditional mechanical model-based failure analysis methods, this step, by combining modal analysis with the dynamic evolution of topological graph models, more accurately captures the failure process of nodes in complex earthquake environments.

[0041] Step S600: Evaluate based on the failure mode analysis results to obtain node seismic toughness analysis results.

[0042] It's important to note that this step comprehensively and accurately assesses the seismic performance of nodes under actual earthquake conditions by comprehensively evaluating failure modes and quantitative indicators of seismic resilience. The results are more accurate and timely, providing a scientific basis for subsequent node seismic design, optimization, and maintenance.

[0043] Furthermore, step S200 includes steps S210 to S230.

[0044] Step S210: Perform multimodal fusion based on basic parameters, and obtain a cross-physical field fusion tensor by establishing a derivative correlation between material parameters and geometric parameters;

[0045] The core of this process is to accurately capture the interaction between material properties and geometric forms in different physical fields by establishing derivative correlations between material parameters and geometric parameters. Specifically, material parameters (such as elastic modulus, yield strength, plastic behavior, etc.) and geometric parameters (such as pier shape, size, node connection method, etc.) are fused through derivative correlations, so as to describe how these parameters affect the response of nodes in different physical fields (such as mechanics, thermals, etc.). Through this multimodal fusion, a high-dimensional fusion tensor can be obtained, which contains comprehensive information on material properties, geometric deformation, and the interaction of multiple physical fields. This fusion tensor provides a more accurate physical basis for subsequent analysis and modeling, so that the response of nodes under dynamic loads such as seismic motion can be deeply analyzed through the mutual coupling of multiple physical fields.

[0046] Step S220: Based on the cross-physical field fusion tensor, a time-varying dynamic characteristic matrix is obtained by integrating the short-term impact and long-term cumulative effects of the earthquake motion through linear embedding processing combined with time decay and spectral weighting;

[0047] It can be understood that this step combines the linear embedding method of time attenuation and spectral weighting to integrate the short-term impact of earthquake motion with the long-term cumulative effect. In this process, the application of the time attenuation function can simulate the different effects of earthquake motion on nodes in different time periods. Especially in a complex environment where strong earthquakes and multiple vibrations alternate, the impact of earthquake motion on the structure is not uniform, but gradually decays over time. At the same time, the spectral weighting method can better capture the impact of high-frequency and low-frequency components in earthquake waves on node responses by assigning different importance to different frequency components.

[0048] This combined approach of time attenuation and spectral weighting accurately describes both the short-term impact of earthquake motion (such as intense instantaneous vibrations) and the long-term cumulative effects (such as progressive structural damage caused by repeated vibrations). This comprehensive analysis of these short-term and long-term effects generates a time-varying dynamic characteristic matrix, which reflects the time-varying response characteristics of nodes under different earthquake motion conditions. This matrix not only reveals the response patterns of nodes under dynamic loading but also provides sufficient data support for subsequent dynamic analysis and damage assessment.

[0049] Step S230: Perform tensor decomposition processing according to the time-varying dynamic characteristic matrix to obtain a multi-field coupling characteristic tensor. The multi-field coupling characteristics include the correlation characteristics of tensor material nonlinearity and geometric deformation, and the strain-temperature gradient coupling characteristics.

[0050] It should be noted that tensor decomposition can decompose high-dimensional data representations into lower-dimensional components that are easier to analyze and understand. Specifically, the tensor decomposition process decomposes the multiphysics coupling characteristics in the time-varying dynamic feature matrix into multiple independent coupling features, further extracting important information such as the correlation between material nonlinearity and geometric deformation, and the coupling characteristics of strain-temperature gradients. In this process, tensor decomposition can decompose complex multiphysics interactions into more manageable features, significantly improving data interpretability and model computational efficiency. The correlation between material nonlinearity and geometric deformation reflects the structural response of the node under earthquake, such as crack propagation and node deformation; while the strain-temperature gradient coupling characteristics reveal the complex deformation behavior of the node under the combined action of thermal effects and seismic motion. These coupling features not only reflect the performance of the node under static and dynamic loads, but also provide in-depth insights into the nonlinear relationship between material and geometry, helping designers optimize the seismic performance of the node.

[0051] Furthermore, step S300 includes steps S310 to S330.

[0052] Step S310: Based on the multi-field coupling feature tensor, a vertex dynamic embedding process based on a time convolutional network is performed, and the temporal coupling characteristics of material nonlinearity and geometric deformation are captured by a sliding time window convolution kernel to obtain a time-varying vertex attribute vector;

[0053] In this step, a temporal convolutional network is used to process the time-dependent information in the multi-field coupling feature tensor, especially the temporal coupling characteristics of materials and geometric deformations. By sliding the time window convolution kernel, the nonlinear behavior of the material (such as elastic-plastic deformation) and geometric deformation (such as the shape change of the node) can be dynamically captured. In this way, a dynamically updated vertex attribute vector can be obtained, which contains the physical state of the node at each time step, including displacement, stress, strain, and deformation information. This vector not only reflects the current state of the node, but also reveals how the node changes its response over time under earthquake action.

[0054] Step S320: construct a matrix based on the time-varying vertex attribute vector, calculate the attention weight by introducing an exponential decay function of the earthquake duration, and obtain an adaptive edge weight matrix;

[0055] Specifically, time-varying vertex attribute vectors are constructed through a matrix to form the graph's vertex attribute matrix. Next, by introducing an exponential decay function, the model can simulate the temporal attenuation effect of earthquake motion. The duration of earthquake motion has a significant impact on node connectivity: short-duration shocks may lead to immediate strong coupling between nodes, while long-duration shocks may cause gradual changes in inter-node relationships. The exponential decay function gradually attenuates the intensity and impact of earthquake motion into the model through time weighting, thereby better reflecting the dynamic coupling relationships between nodes over different time periods. Incorporating the aforementioned time decay properties, an attention mechanism is used to calculate the adaptive weight of each edge. This allows the edge weight to dynamically change according to the varying intensity and duration of the earthquake motion, more accurately reflecting the strength and changing trends of interactions between nodes. The adaptive edge weight matrix provides more accurate connectivity information for topological modeling, ensuring that the model truly reflects the actual connectivity and interactions between nodes under earthquake action.

[0056] Step S330: Based on the adaptive edge weight matrix, a topological connection relationship that conforms to the actual seismic response evolution law is constructed by combining the transition probability of the structural damage state to obtain a time-varying graph structure model.

[0057] It can be understood that the adaptive edge weight matrix has already adjusted the connection strength between nodes through the previous steps. This step introduces the transition probability of the damage state into the topological connectivity to reflect the dynamic changes of the structure under earthquake action. Specifically, the damage state of a structure is not static but changes dynamically with the occurrence of an earthquake. During this process, the damage state of a node has a certain transition probability over time, namely the probability of a node transitioning from an undamaged state to a partially damaged, fully damaged, or recovered state. These damage state transition patterns affect the connectivity and topological structure between nodes. In this step, by combining the damage transition probability with the adaptive edge weight matrix, the model can construct a dynamically changing topological structure that accurately simulates the evolution of the structure's response under earthquake action. The construction of the time-varying graph structure model allows each node to not only be spatially connected to other nodes but also adjust its connection strength with other nodes over time as the damage state evolves. This topological model not only reflects the physical state of the node but also simulates its mutual influence with surrounding nodes, revealing the changes in the node's response during an earthquake.

[0058] Furthermore, step S400 includes steps S410 to S430.

[0059] Step S410: Initialize the damage state space according to the time-varying graph structure model, and obtain the initial damage distribution field by establishing a geometric mapping relationship between node displacement and local damage;

[0060] Under the action of an earthquake, the displacement of a node usually exhibits nonlinear characteristics. These displacements not only reflect the degree of deformation of the node, but are also directly related to the damage of the structure. Therefore, through the mapping relationship between displacement and local damage, the damage distribution of each node under the initial earthquake can be clearly described. Specifically, the displacement of the node is the result of the combined action of external seismic motion and internal structural response, and is expressed as time series data. By mapping the node displacement data to the damage state, the initial damage state of each node can be obtained. This initial damage distribution field reflects the possible damage conditions of different parts of the node in the early stage of the earthquake, provides a basis for subsequent damage evolution simulation, and ensures that the damage analysis starts from a reasonable initial state.

[0061] Step S420: Perform stochastic differential equation modeling based on the initial damage distribution field, set the Poisson jump term triggered by the earthquake energy exceeding the limit, couple the gradual damage and sudden damage evolution processes, and obtain a set of damage evolution paths;

[0062] It should be noted that to capture the different damage modes of nodes induced by earthquakes, particularly the interaction between progressive damage and sudden damage, this step introduces a Poisson jump term to set the trigger condition for exceeding the ground motion energy limit, thereby coupling the two. Stochastic differential equations can account for system uncertainty and time dependence. They simulate the behavior of the structure at different earthquake stages by setting a trigger condition (e.g., when the ground motion energy exceeds a certain threshold, the node damage will increase dramatically). In this model, the Poisson jump term simulates the occurrence of sudden damage. For example, under the impact of a strong earthquake, a node may suddenly and violently fail. This sudden damage mechanism is generally not captured by simple continuum models, so the Poisson jump term provides an effective mathematical representation. Meanwhile, progressive damage describes the gradual accumulation of damage at a node under relatively small ground motions. By coupling these two damage modes, the model generates a set of multiple possible damage paths. These paths reflect the different damage evolution processes that a node may undergo under earthquakes of varying intensities. This set of damage evolution paths provides a variety of possible damage modes for subsequent seismic resilience assessments and offers diverse risk analysis perspectives for optimizing structural design.

[0063] Step S430: Perform index fusion according to the damage evolution path set, and obtain a quantitative index of seismic resilience by fusing the energy dissipation spectrum, the recovery capacity gradient, and the topological connectivity change rate into a unified metric.

[0064] It is understandable that the energy dissipation spectrum reflects the energy absorption capacity of the node under the action of an earthquake. It can describe how the node absorbs earthquake energy through plastic deformation and slows down the damage of the structure. The recovery capacity gradient measures the recovery capacity of the node after an earthquake, especially how the structure gradually recovers to its original state or a new equilibrium state after experiencing damage. The topological connectivity change rate reveals the changes in the connection relationship between nodes during an earthquake, especially how the connectivity of the structure changes after the node is damaged, which is closely related to the overall stability of the structure. Combining these indicators into a unified metric allows seismic resilience to be evaluated from multiple dimensions. Through this fusion process, the obtained quantitative index of seismic resilience can comprehensively reflect the dynamic response of the node during an earthquake, including energy absorption, recovery capacity, and structural stability.

[0065] Furthermore, step S500 includes steps S510 to S530.

[0066] Step S510: Based on the seismic toughness quantification index, the modal correlation between the energy dissipation spectrum and the damage rate is quantified by constructing a covariance gradient field on a Riemannian manifold to obtain a master modal sensitivity spectrum;

[0067] The core of this process is to describe the statistical correlation between different damage modes by introducing the manifold covariance matrix to determine which modes have the main impact on the seismic performance of the node. The role of the Riemannian manifold in this step is to represent the damage state as a high-dimensional space, so that the covariance matrix can be used to characterize the relationship between different damage modes in this space. The manifold covariance matrix is defined by the following formula:

[0068] ;

[0069] in, is the manifold covariance matrix, which is used to describe the statistical correlation of damage modes in the differential manifold space; is the damage state manifold; and are the damage mode vectors, representing the 、 Spatial distribution characteristics of the first-order damage mode; is the average damage mode vector; is the volume element of the manifold, which is represented by the manifold metric tensor Defined integral measure; is the manifold metric tensor, a second-order symmetric tensor defining the local geometric properties of the damaged manifold; Represents the transpose of a matrix.

[0070] By calculating the covariance matrix, the dominant modal sensitivity spectrum can be obtained, which quantifies the modal correlation between energy dissipation and damage rate.

[0071] Step S520: performing a path search in the topology model according to the master control modal sensitivity spectrum to obtain a set of potential failure paths;

[0072] It is understandable that by analyzing the main modal sensitivity spectrum, it is possible to identify which modes have the greatest impact on the seismic toughness of the structure and provide key clues for path search. In the path search process, the strain energy density gradient is introduced as a heuristic function. The heuristic function is used to evaluate the priority of nodes in the path search process, thereby improving the efficiency of the path search. The form of the heuristic function is:

[0073] ;

[0074] in, is the heuristic function value used to evaluate the node Priority in path search; is the node strain energy density gradient, reflecting the spatial variation rate of strain energy at the node; is the damage time attenuation coefficient; is the node time evolution parameter, which represents the cumulative time from the initial state to the current node state.

[0075] By introducing the heuristic function, the importance of nodes in path search can be evaluated more accurately, thereby improving the efficiency and accuracy of path search.

[0076] Step S530 : Screening is performed based on the set of potential failure paths. By calculating the Betti number and the Leray-Sauder degree of the path, paths with incomplete topological structures or mathematically integrable structures are eliminated to obtain failure mode analysis results.

[0077] As you can understand, the Betti number describes the number of connected components and the complexity of the topological structure in a graph, while the Leray-Sauder degree is used to evaluate the topological properties of the paths, ensuring that they conform to the evolution of actual earthquake responses. By calculating these topological characteristics, the system can eliminate paths that do not conform to the actual earthquake dynamic response, such as those with incomplete topology or paths that cannot be realized in physical space. Through this screening process, a set of physically feasible failure paths is ultimately obtained.

[0078] Furthermore, step S600 includes steps S610 to S630.

[0079] Step S610: Perform probability density fusion based on the failure mode analysis results, quantify the entropy growth rate of the master failure path by constructing a time-varying weight function, and integrate the spatiotemporal heterogeneity characteristics of damage accumulation and recovery capability to obtain a comprehensive toughness probability distribution;

[0080] Specifically, the entropy growth rate reflects the rate at which a system evolves from an ordered to a disordered state. In seismic resilience analysis, the entropy growth rate can describe the damage accumulation and evolution process of a node or structure under earthquake action. By constructing a time-varying weight function, the system can incorporate the dynamic changes of failure paths into the analysis and quantify the time-varying contribution of these paths to the overall resilience of the structure. The time-varying weight function adjusts the weight of each path based on the duration of the earthquake and the evolution of the node damage state, thereby more accurately reflecting the actual impact of the path.

[0081] Furthermore, the spatial and temporal heterogeneity of damage accumulation and recovery capacity is incorporated into this process. These characteristics include differences in damage development and recovery processes at nodes at different temporal and spatial locations. By integrating these temporal and spatial heterogeneity features, the inhomogeneous response of nodes to earthquakes can be more accurately captured, leading to a comprehensive, integrated resilience probability distribution.

[0082] Step S620: Simulate according to the comprehensive toughness probability distribution to obtain a node toughness association map;

[0083] It's important to note that the comprehensive resilience probability distribution incorporates information such as the damage evolution, recovery capacity, and energy dissipation of nodes under different earthquake conditions. Next, by simulating this data, a node resilience correlation map is generated. This map visually illustrates the damage and recovery process of nodes under earthquakes, as well as their time-varying behavior under different vibration conditions. The node resilience state is represented in the map using different colors, sizes, or shapes to facilitate identification of the node's seismic resistance and vulnerable locations.

[0084] By simulating the performance of nodes in various earthquake scenarios, the node resilience correlation map not only demonstrates the seismic performance of the nodes in the current earthquake event, but also reflects the damage evolution they may experience in future earthquakes. This map provides a visual basis for subsequent structural design and optimization of seismic resistance strategies.

[0085] Step S630: Evaluate the node toughness correlation map, construct a joint order parameter space of strain field gradient and energy dissipation rate, and divide the phase transition boundary of the structure from elastic response to failure to obtain the node seismic toughness analysis result.

[0086] It can be understood that the strain field gradient reflects the local deformation rate of the node during an earthquake, while the energy dissipation rate describes how the node absorbs and dissipates seismic energy. A joint analysis of these two can reveal the deformation and energy dissipation capacity of the node under earthquake action and construct a multidimensional evaluation standard through the joint order parameter space. Through this space, the different states of the node under earthquake action, such as the elastic response zone, plastic deformation zone, and damage zone, can be clearly identified. Next, a phase transition boundary is constructed to demarcate the transition zone from elastic response to failure and damage of the node. This process determines the turning point from the recoverable state to the irrecoverable damage state of the node by defining the critical values of the strain field and energy dissipation rate. The delineation of the phase transition boundary is crucial for understanding the failure mode of the node. It can help identify the vulnerable areas of the node during earthquakes and provide a basis for structural optimization and seismic design.

[0087] Ultimately, a joint analysis of strain field gradients and energy dissipation rates yields a seismic toughness analysis of the node. This result not only includes the node's elastic response but also predicts its failure path and vulnerable points under strong earthquakes, providing crucial decision-making support for subsequent design improvements.

[0088] Furthermore, step S620 includes steps S621 to S623.

[0089] Step S621: Perform nonlinear regression analysis based on the comprehensive toughness probability distribution, and obtain key propagation threshold parameters by extracting node failure sensitivity coefficients and energy transfer paths;

[0090] In this step, the node failure sensitivity coefficient reflects the node's ability to respond to ground motions of varying intensities during an earthquake, revealing the node's vulnerability to varying levels of damage. By analyzing energy transfer paths, the system can track the energy transfer process between nodes and identify which paths are most likely to cause node failure. These energy transfer paths provide important information for subsequent failure mode prediction, particularly how earthquake input affects node energy dissipation and damage evolution. Based on this data, key propagation threshold parameters can be extracted. These parameters represent the critical conditions (such as the energy dissipation threshold and the node's stress limit) at which damage or failure begins to occur under earthquake conditions, providing important criteria for subsequent modeling and path analysis.

[0091] Step S622: Modeling is performed based on key propagation threshold parameters, and an initial cascade network model is obtained by establishing a dynamic coupling mechanism between damage diffusion and energy redistribution between adjacent nodes.

[0092] It can be understood that the damage diffusion model describes how nodes experience progressive damage accumulation during an earthquake, and the occurrence of damage will affect adjacent nodes, causing further damage to them. Energy redistribution of adjacent nodes means that when a node is damaged, it will redistribute the energy of surrounding nodes. This energy transfer will affect the stability of the entire system. By coupling these effects, the initial cascade network model is able to simulate the complex dynamic relationship between nodes and connections, revealing the path of node damage diffusion and the direction of energy transfer. In this process, the construction of the network model not only takes into account the connection relationship between nodes, but also incorporates the dynamic changes of energy and damage, allowing the model to update the state of the node at each time step.

[0093] Step S623: Based on the initial cascade network model, by correcting the topological connectivity of the propagation path, a network graph reflecting the correlation characteristics of earthquake input-local damage-global topological degradation is generated to obtain a node resilience correlation graph.

[0094] It should be noted that topological connectivity is a key indicator that describes the strength of connections between nodes in a network and determines the efficiency of information or energy transfer between nodes. Under the action of an earthquake, damage to nodes will lead to changes in the topological relationship of the structure. In particular, after a node fails, the connection relationship may be broken, which in turn affects the stability of the entire structure. By correcting the topological connectivity of the propagation path, these changes can be dynamically reflected, thereby accurately simulating the performance of nodes in an earthquake. The generated network map reflects how seismic input causes local damage and affects changes in the global topological structure through energy redistribution between nodes. Through this map, it is possible to clearly identify which nodes are the vulnerable points of the system during an earthquake and which paths may lead to global failure. Example 2:

[0095] like Figure 2 As shown, this embodiment provides a seismic toughness analysis system for segmental prefabricated railway high pier nodes, the system comprising:

[0096] An acquisition module 901 is used to acquire basic parameters of the target segment prefabricated railway high pier node, the basic parameters including material parameters, pier body geometry parameters, earthquake motion parameters and node response parameters;

[0097] A decomposition module 902 is used to decompose the multi-field coupling tensor according to the basic parameters to obtain a multi-field coupling characteristic tensor;

[0098] Modeling module 903, used for performing topological modeling based on multi-field coupling characteristic tensors to obtain a time-varying graph structure model;

[0099] The simulation module 904 is used to simulate the damage evolution according to the time-varying graph structural model to obtain a quantitative index of seismic toughness;

[0100] Analysis module 905, used to obtain failure mode analysis results based on the seismic toughness quantitative index through modal sensitivity analysis and failure path extraction;

[0101] The evaluation module 906 is used to perform an evaluation based on the failure mode analysis results to obtain a node seismic toughness analysis result.

[0102] In a specific embodiment of the present invention, the decomposition module 902 includes:

[0103] The first decomposition unit is used to perform multimodal fusion according to basic parameters, and obtain a cross-physical field fusion tensor by establishing the derivative correlation between material parameters and geometric parameters;

[0104] The second decomposition unit is used to obtain the time-varying dynamic characteristic matrix by fusing the short-term impact and long-term cumulative effects of the earthquake motion based on the cross-physical field fusion tensor through linear embedding processing combined with time decay and spectral weighting;

[0105] The third decomposition unit is used to perform tensor decomposition processing according to the time-varying dynamic characteristic matrix to obtain a multi-field coupling characteristic tensor. The multi-field coupling characteristics include the correlation characteristics of tensor material nonlinearity and geometric deformation, and the strain-temperature gradient coupling characteristics.

[0106] In a specific embodiment of the present invention, the modeling module 903 includes:

[0107] The first modeling unit is used to obtain a time-varying vertex attribute vector based on the multi-field coupling feature tensor after vertex dynamic embedding processing based on a time convolutional network, and to capture the temporal coupling characteristics of material nonlinearity and geometric deformation through a sliding time window convolution kernel;

[0108] The second modeling unit is used to construct a matrix based on the time-varying vertex attribute vector, calculate the attention weight by introducing the exponential decay function of the earthquake duration, and obtain the adaptive edge weight matrix;

[0109] The third modeling unit is used to construct a topological connection relationship that conforms to the actual seismic response evolution law based on the adaptive edge weight matrix and by combining the transition probability of the structural damage state to obtain a time-varying graph structure model. Example 3:

[0110] Corresponding to the above method embodiment, this embodiment also provides a segment-assembly railway high pier node seismic toughness analysis device. The segment-assembly railway high pier node seismic toughness analysis device described below and the segment-assembly railway high pier node seismic toughness analysis method described above can be referenced to each other.

[0111] Figure 3FIG. 8 is a block diagram of a segment-assembly railway high pier node seismic toughness analysis device 800 according to an exemplary embodiment. Figure 3 As shown, the segment-assembly railway high pier node seismic toughness analysis device 800 may include: a processor 801, a memory 802. The segment-assembly railway high pier node seismic toughness analysis device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0112] The processor 801 is used to control the overall operation of the segment-assembly railway high pier node seismic resilience analysis device 800 to complete all or part of the steps in the above-mentioned segment-assembly railway high pier node seismic resilience analysis method. The memory 802 is used to store various types of data to support the operation of the segment-assembly railway high pier node seismic resilience analysis device 800. This data may include, for example, instructions for any application or method operating on the segment-assembly railway high pier node seismic resilience analysis device 800, as well as application-related data such as contact information, sent and received messages, images, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the segment-assembly-type railway high pier node seismic toughness analysis device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0113] In an exemplary embodiment, a segment-assembly railway high pier node seismic toughness analysis device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned segment-assembly railway high pier node seismic toughness analysis method.

[0114] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the aforementioned method for analyzing the seismic toughness of segmentally prefabricated railway high pier joints. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the device 800 for analyzing the seismic toughness of segmentally prefabricated railway high pier joints to implement the aforementioned method for analyzing the seismic toughness of segmentally prefabricated railway high pier joints.

[0115] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for analyzing the seismic toughness of segmental prefabricated railway high pier nodes, characterized in that: include: Obtaining basic parameters of the target segment prefabricated railway high pier node, wherein the basic parameters include material parameters, pier body geometry parameters, earthquake motion parameters, and node response parameters; Decomposing the multi-field coupling tensor according to the basic parameters to obtain a multi-field coupling characteristic tensor; Performing topological modeling based on the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; Perform damage evolution simulation based on the time-varying graph structural model to obtain a quantitative index of seismic toughness; According to the quantitative index of seismic toughness, the failure mode analysis results are obtained through modal sensitivity analysis and failure path extraction; Perform an evaluation based on the failure mode analysis results to obtain a node seismic toughness analysis result; The damage evolution simulation is performed according to the time-varying graphical structure model, including: Initializing the damage state space according to the time-varying graphical structure model, and obtaining the initial damage distribution field by establishing a geometric mapping relationship between node displacement and local damage; Stochastic differential equation modeling is performed based on the initial damage distribution field. By setting the Poisson jump term triggered by the earthquake energy exceeding the limit, the gradual damage and sudden damage evolution processes are coupled to obtain a set of damage evolution paths. According to the damage evolution path set, the index fusion is performed to obtain the quantitative index of seismic toughness by integrating the energy dissipation spectrum, the recovery capacity gradient and the topological connectivity change rate into a unified metric standard; Among them, according to the quantitative index of seismic toughness, after modal sensitivity analysis and failure path extraction, it includes: According to the quantitative index of seismic toughness, the modal correlation between the energy dissipation spectrum and the damage rate is quantified by constructing a covariance gradient field on a Riemannian manifold, thereby obtaining a master modal sensitivity spectrum; Performing a path search in a topology graph model according to the master control modal sensitivity spectrum to obtain a set of potential failure paths; The potential failure path set is screened, and paths with incomplete topological structures or mathematically integrable paths are eliminated by calculating the Betti number and the Leray-Schödel degree of the paths to obtain failure mode analysis results.

2. The method for analyzing the seismic toughness of segmented prefabricated railway high pier nodes according to claim 1 is characterized in that: Performing multi-physics field coupling tensor decomposition according to the basic parameters includes: Performing multimodal fusion according to the basic parameters, and obtaining a cross-physical field fusion tensor by establishing a derivative correlation between material parameters and geometric parameters; According to the cross-physical field fusion tensor, a time-varying dynamic characteristic matrix is obtained by integrating the short-term impact and long-term cumulative effects of the earthquake motion through linear embedding processing combined with time decay and spectral weighting; A tensor decomposition process is performed according to the time-varying dynamic characteristic matrix to obtain a multi-field coupling characteristic tensor, wherein the multi-field coupling characteristics include correlation characteristics of tensor material nonlinearity and geometric deformation, and strain-temperature gradient coupling characteristics.

3. The method for analyzing the seismic toughness of segmented prefabricated railway high pier nodes according to claim 1 is characterized in that: Topological modeling is performed according to the multi-field coupling characteristic tensor, including: According to the multi-field coupling characteristic tensor, after vertex dynamic embedding processing based on a time convolutional network, the time-varying vertex attribute vector is obtained by capturing the temporal coupling characteristics of material nonlinearity and geometric deformation through a sliding time window convolution kernel; A matrix is constructed according to the time-varying vertex attribute vector, and an attention weight is calculated by introducing an exponential decay function of the earthquake duration to obtain an adaptive edge weight matrix; According to the adaptive edge weight matrix, a topological connection relationship that conforms to the actual seismic response evolution law is constructed by combining the transition probability of the structural damage state to obtain a time-varying graph structure model.

4. The method for analyzing seismic toughness of segmented prefabricated railway high pier nodes according to claim 1 is characterized in that: Evaluation is performed based on the failure mode analysis results, including: Based on the failure mode analysis results, probability density fusion is performed, and the entropy growth rate of the main control failure path is quantified by constructing a time-varying weight function. The spatiotemporal heterogeneity characteristics of damage accumulation and recovery capability are integrated to obtain a comprehensive toughness probability distribution. Perform simulation based on the comprehensive toughness probability distribution to obtain a node toughness correlation map; An evaluation is performed based on the node toughness correlation map. By constructing a joint order parameter space of strain field gradient and energy dissipation rate and dividing the phase transition boundary of the structure from elastic response to failure, the node seismic toughness analysis results are obtained.

5. The method for analyzing seismic toughness of segmental prefabricated railway high pier nodes according to claim 4 is characterized in that: A simulation is performed based on the comprehensive toughness probability distribution to obtain a node toughness association map, including: Nonlinear regression analysis is performed based on the comprehensive toughness probability distribution to obtain key propagation threshold parameters by extracting node failure sensitivity coefficients and energy transfer paths; Modeling is performed based on the key propagation threshold parameters, and an initial cascade network model is obtained by establishing a dynamic coupling mechanism between damage diffusion and energy redistribution between adjacent nodes; According to the initial cascade network model, by correcting the topological connectivity of the propagation path, a network graph reflecting the correlation characteristics of earthquake input-local damage-global topological degradation is generated, and a node toughness correlation graph is obtained.

6. A segmental assembly railway high pier node seismic toughness analysis system, characterized by: include: An acquisition module is used to acquire basic parameters of the target segment prefabricated railway high pier node, wherein the basic parameters include material parameters, pier body geometry parameters, earthquake motion parameters and node response parameters; A decomposition module, configured to decompose the multi-field coupling tensor according to the basic parameters to obtain a multi-field coupling characteristic tensor; A modeling module, configured to perform topological modeling based on the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; A simulation module, configured to simulate damage evolution according to the time-varying graph structural model to obtain a quantitative index of seismic toughness; An analysis module is used to obtain failure mode analysis results based on the seismic toughness quantitative index through modal sensitivity analysis and failure path extraction; An evaluation module, configured to evaluate the failure mode analysis results to obtain a node seismic toughness analysis result; The damage evolution simulation is performed according to the time-varying graphical structure model, including: Initializing the damage state space according to the time-varying graphical structure model, and obtaining the initial damage distribution field by establishing a geometric mapping relationship between node displacement and local damage; Stochastic differential equation modeling is performed based on the initial damage distribution field. By setting the Poisson jump term triggered by the earthquake energy exceeding the limit, the gradual damage and sudden damage evolution processes are coupled to obtain a set of damage evolution paths. According to the damage evolution path set, the index fusion is performed to obtain the quantitative index of seismic toughness by integrating the energy dissipation spectrum, the recovery capacity gradient and the topological connectivity change rate into a unified metric standard; Among them, according to the quantitative index of seismic toughness, after modal sensitivity analysis and failure path extraction, it includes: According to the quantitative index of seismic toughness, the modal correlation between the energy dissipation spectrum and the damage rate is quantified by constructing a covariance gradient field on a Riemannian manifold, thereby obtaining a master modal sensitivity spectrum; Performing a path search in a topology graph model according to the master control modal sensitivity spectrum to obtain a set of potential failure paths; The potential failure path set is screened, and paths with incomplete topological structures or mathematically integrable paths are eliminated by calculating the Betti number and the Leray-Schödel degree of the paths to obtain failure mode analysis results.

7. The segmental assembled railway high pier node seismic toughness analysis system according to claim 6 is characterized in that: The decomposition module includes: A first decomposition unit is configured to perform multimodal fusion according to the basic parameters, and obtain a cross-physical field fusion tensor by establishing a derivative correlation between material parameters and geometric parameters; The second decomposition unit is used to obtain a time-varying dynamic characteristic matrix by fusing the short-term impact and long-term cumulative effects of the earthquake motion according to the cross-physical field fusion tensor through linear embedding processing combined with time decay and spectral weighting; The third decomposition unit is used to perform tensor decomposition processing according to the time-varying dynamic characteristic matrix to obtain a multi-field coupling characteristic tensor, wherein the multi-field coupling characteristics include the correlation characteristics of tensor material nonlinearity and geometric deformation, and the strain-temperature gradient coupling characteristics.

8. The segment-assembly railway high pier node seismic toughness analysis system according to claim 6 is characterized in that: The modeling module includes: A first modeling unit is configured to obtain a time-varying vertex attribute vector by capturing the temporal coupling characteristics of material nonlinearity and geometric deformation through a sliding time window convolution kernel according to the multi-field coupling characteristic tensor and performing vertex dynamic embedding processing based on a time convolution network; A second modeling unit is configured to construct a matrix based on the time-varying vertex attribute vector, calculate attention weights by introducing an exponential decay function of earthquake duration, and obtain an adaptive edge weight matrix; The third modeling unit is used to construct a topological connection relationship that conforms to the actual earthquake response evolution law based on the adaptive edge weight matrix and in combination with the transition probability of the structural damage state to obtain a time-varying graph structure model.

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