Segmental assembly type railway high pier joint seismic toughness analysis method and system

Abstract

The invention provides an anti-seismic toughness analysis method and system for a segment assembly type railway high pier node, and relates to the technical field of computers.The method comprises the steps that basic parameters of a target segment assembly type railway high pier node are obtained, and the basic parameters comprise material parameters, pier body geometric parameters, seismic oscillation parameters and node response parameters; performing multi-physical field coupling tensor decomposition according to the basic parameters to obtain a multi-field coupling feature tensor; performing topological modeling according to the multi-field coupling feature tensor to obtain a time-varying graph structure model; performing damage evolution simulation according to the time-varying graph structure model to obtain an anti-seismic toughness quantitative index; according to the anti-seismic toughness quantitative index, a failure mode analysis result is obtained through modal sensitivity analysis and failure path extraction; and performing evaluation according to the failure mode analysis result to obtain a node anti-seismic toughness analysis result. According to the invention, by introducing the multi-physics field coupling tensor decomposition and time-varying graph structure model, the response of the node in a complex earthquake environment can be simulated more accurately.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method and system for seismic resilience analysis of segmental precast railway high pier nodes. Background Art

[0002] As a new structural form in modern railway construction, segmental precast railways have been widely used in recent years. Its main feature is on-site assembly by prefabricating segments, which significantly improves construction efficiency and accuracy, while reducing the impact on the environment during construction. This structural form is particularly suitable for complex terrain conditions and areas that require rapid construction. However, due to the structural characteristics and node connection methods of segmental precast railways, they have relatively high requirements for seismic performance. Especially in earthquake-prone areas, the seismic resilience of railway high pier nodes becomes an important factor affecting their safety and stability. Therefore, the seismic resilience analysis of nodes is particularly important, which can effectively evaluate and improve the bearing capacity and durability of structures under earthquake actions.

[0003] Currently, the seismic resilience analysis of segmental precast railway high pier nodes mainly relies on mechanical models and simplified calculation methods. These existing methods often only consider the elastic properties of materials and the geometric shapes of nodes, without deeply considering the multi-physical field coupling effect of ground motions and the non-linear characteristics of node responses. Existing technologies usually use elastic mechanics or linear statics methods for analysis, ignoring the time-varying characteristics of ground motions, the non-linear behavior of nodes, and the cumulative effect of vibration energy. In practical applications, they cannot provide accurate seismic performance evaluations. Especially under strong earthquake actions, they often underestimate the damage and failure risks of nodes.

[0004] Based on the above disadvantages of the existing technologies, there is an urgent need for a method and system for seismic resilience analysis of segmental precast railway high pier nodes. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for seismic resilience analysis of segmental precast railway high pier nodes to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows: In a first aspect, the present application provides a method for seismic resilience analysis of segmental precast railway high pier nodes, including: Obtaining the basic parameters of the target segmental precast railway high pier node, where the basic parameters include material parameters, pier geometric parameters, ground motion parameters, and node response parameters; Performing multi-physical field coupling tensor decomposition according to the basic parameters to obtain a multi-field coupling characteristic tensor; Performing topological modeling according to the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; Perform damage evolution simulation according to the time-varying graph structure model to obtain the seismic resilience quantification index; According to the seismic resilience quantification index, through modal sensitivity analysis and failure path extraction, obtain the failure mode analysis result; Evaluate according to the failure mode analysis result to obtain the node seismic resilience analysis result.

[0006] In a second aspect, the present application also provides a seismic resilience analysis system for segmental precast railway high pier nodes, including: An acquisition module, configured to acquire the basic parameters of the target segmental precast railway high pier node, where the basic parameters include material parameters, pier body geometric parameters, ground motion parameters, and node response parameters; A decomposition module, configured to perform multi-physical field coupling tensor decomposition according to the basic parameters to obtain a multi-field coupling characteristic tensor; A modeling module, configured to perform topological modeling according to the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; A simulation module, configured to perform damage evolution simulation according to the time-varying graph structure model to obtain the seismic resilience quantification index; An analysis module, configured to perform modal sensitivity analysis and failure path extraction according to the seismic resilience quantification index to obtain the failure mode analysis result; An evaluation module, configured to evaluate according to the failure mode analysis result to obtain the node seismic resilience analysis result.

[0007] The beneficial effects of the present invention are: By introducing multi-physical field coupling tensor decomposition and time-varying graph structure model, 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, short-term impact and long-term cumulative effects of ground motion, and combine damage evolution simulation for failure mode analysis, significantly improving the accuracy and reliability of seismic resilience analysis. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic flow chart of a seismic resilience analysis method for segmental precast railway high pier nodes described in the embodiments of the present invention; Figure 2Schematic structural diagram of a seismic resilience analysis system for segmental precast railway high pier nodes described in an embodiment of the present invention; Figure 3 Schematic structural diagram of a seismic resilience analysis device for segmental precast railway high pier nodes described in an embodiment of the present invention.

[0010] Reference numerals in the figure: 800, a seismic resilience analysis device for segmental precast railway high pier nodes; 801, a processor; 802, a memory; 803, a multimedia component; 804, an I / O interface; 805, a communication component; 901, an acquisition module; 902, a decomposition module; 903, a modeling module; 904, a simulation module; 905, an analysis module; 906, an evaluation module. Specific implementation manners

[0011] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention described and illustrated herein generally may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but is merely representative of selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0012] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance. Embodiment 1:

[0013] This embodiment provides a method for seismic resilience analysis of segmental precast railway high pier nodes.

[0014] See Figure 1 , which shows that this method includes steps S100 to S600.

[0015] Step S100, obtain the basic parameters of the target segmental precast railway high pier node, where the basic parameters include material parameters, pier geometric parameters, ground motion parameters, and node response parameters; It can be understood that the material parameters mainly include the mechanical properties of components such as the concrete and steel bars used, such as elastic modulus, yield strength, compressive strength, and non-linear behavior, etc. The geometric parameters of the pier body include information such as pier height, pier width, the shape of the joint, and the assembly method, etc. These parameters directly affect the stiffness and stability of the structure. The ground motion parameters include the amplitude, frequency, duration, and spectral characteristics of the ground motion, etc. The joint response parameters include the displacement, stress distribution, damage condition, etc. of the joint under different loads. These parameters can truly reflect the dynamic response of the joint under earthquake action.

[0016] Step S200: Perform multi-physical-field coupling tensor decomposition based on the basic parameters to obtain a multi-field coupling characteristic tensor; In earthquake engineering, the response of a structure is usually affected not only by a single physical field (such as mechanics), but also by the interaction of multiple physical fields such as temperature, strain, and ground motion. The interaction between these physical fields is complex, and traditional analysis methods often cannot fully consider this multiple coupling effect. The multi-physical-field coupling tensor decomposition method can simultaneously handle the interrelationships between multiple physical fields, synthesize them into a high-dimensional tensor representation, and accurately capture the coupling characteristics between each physical field.

[0017] Step S300: Perform topological modeling based on the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; It can be understood that topological modeling expresses the physical characteristics, responses of the joints, and the influence of ground motion in a graph-structured manner. The finally obtained time-varying graph structure model can accurately represent the dynamic behavior of the joints under earthquake action. Preferably, by combining graph theory and convolutional networks, the model can not only capture the state of the joints at different time points, but also flexibly simulate the influence of ground motion on the connection relationship of the joints. The use of the time-varying graph structure makes the seismic analysis more comprehensive and dynamic, and can effectively reflect the force and deformation process of the joints under strong earthquake action, providing a reliable basis for subsequent damage evolution simulation.

[0018] Step S400: Perform damage evolution simulation based on the time-varying graph structure model to obtain a seismic resilience quantification index; It should be noted that the damage evolution simulation can not only accurately describe the dynamic damage process of the joints, but also quantify the seismic resilience of the joints from multiple dimensions. Compared with traditional seismic analysis methods, this comprehensive evaluation method based on the time-varying graph structure model and damage evolution simulation can more comprehensively reflect the true performance of the joints under complex earthquake actions. Finally, the seismic resilience quantification index provides solid data support for subsequent failure mode analysis, structural optimization, and seismic design, ensuring that the joints can maintain higher safety and durability during earthquakes.

[0019] Step S500: According to the seismic resilience quantification index, through modal sensitivity analysis and failure path extraction, obtain the failure mode analysis result; It can be understood that through modal sensitivity analysis and failure path extraction, the failure modes that the node may experience under seismic action can be clearly revealed, and through the quantitative analysis of these modes, the possible weak links of the structure can be identified in advance. Compared with the traditional failure analysis method based on mechanical models, this step can more accurately capture the failure process of the node in a complex seismic environment by combining modal analysis with the dynamic evolution of the topological graph model.

[0020] Step S600: Evaluate according to the failure mode analysis result to obtain the seismic resilience analysis result of the node.

[0021] It should be noted that through the comprehensive evaluation of the failure mode and the seismic resilience quantification index, this step can comprehensively and accurately evaluate the seismic performance of the node under actual seismic conditions. The evaluation result has higher accuracy and real-time performance, thus providing a scientific basis for the subsequent seismic design, optimization and maintenance of the node.

[0022] Furthermore, step S200 includes steps S210 to S230.

[0023] Step S210: Perform multi-modal fusion according to the basic parameters. By establishing the derivative correlation between material parameters and geometric parameters, obtain the cross-physical field fusion tensor; The core of this process is to accurately capture the interaction between material properties and geometric forms in different physical fields by establishing the derivative correlation 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 correlation, so as to describe how these parameters affect the response of the node in different physical fields (such as mechanics, thermotics, etc.). Through this multi-modal fusion, a high-dimensional fusion tensor can be obtained, which contains comprehensive information on material properties, geometric deformation, and multi-physical field interaction. This fusion tensor provides a more accurate physical basis for subsequent analysis and modeling, enabling the response of the node under dynamic loads such as ground motion to be analyzed in depth through the coupling of multi-physical fields.

[0024] Step S220: According to the cross-physical field fusion tensor, through linear embedding processing combining time decay and spectrum weighting, and by fusing the short-time impact and long-time cumulative effects of ground motion, obtain the time-varying dynamic characteristic matrix; It is understandable that this step combines the linear embedding method of time decay and spectral weighting to fuse the short-term impact and long-term cumulative effect of ground motion. In this process, by applying the time decay function, the different impacts of ground motion on nodes at different time periods can be simulated. Especially in a complex environment where strong earthquakes and multiple vibrations alternate, the impact of ground motion on the structure is not uniform but gradually decays over time. At the same time, the spectral weighting method can better capture the influence of high-frequency and low-frequency components in seismic waves on the node response by assigning different importance to different frequency components.

[0025] Through this processing method that combines time decay and spectral weighting, the short-term impact of ground motion (such as strong instantaneous vibrations) and the long-term cumulative effect (such as progressive damage to the structure caused by repeated vibrations) can be accurately described. The comprehensive processing of these short-term and long-term effects generates a time-varying dynamic characteristic matrix, which can reflect the time-varying response characteristics of nodes under different ground motion conditions. The time-varying dynamic characteristic matrix not only reveals the response mode of nodes under dynamic loading but also provides sufficient data support for subsequent dynamic analysis and damage assessment.

[0026] Step S230: Perform tensor decomposition processing based on the time-varying dynamic characteristic matrix to obtain a multi-field coupling characteristic tensor. The multi-field coupling characteristics include the correlation characteristics between tensor material nonlinearity and geometric deformation, and the strain-temperature gradient coupling characteristics.

[0027] It should be noted that tensor decomposition can decompose the high-dimensional data representation into lower-dimensional components that are easier to analyze and understand. Specifically, the tensor decomposition process decomposes the multi-physical field coupling characteristics in the time-varying dynamic characteristic matrix into multiple independent coupling characteristics, and further extracts important information such as the correlation characteristics between material nonlinearity and geometric deformation, and the coupling characteristics of the strain-temperature gradient. In this process, tensor decomposition can decompose the complex multi-physical field interaction into more manageable characteristics, significantly improving the interpretability of the data and the computational efficiency of the model. The correlation characteristics between material nonlinearity and geometric deformation reflect the structural response of nodes under seismic action, such as crack propagation and node deformation; while the strain-temperature gradient coupling characteristics reveal the complex deformation behavior that nodes may undergo under the combined action of thermal effects and ground motion. These coupling characteristics not only reflect the performance of nodes under static and dynamic loads but also can deeply explore the nonlinear relationship between materials and geometric forms, helping designers optimize the seismic performance of nodes.

[0028] Furthermore, step S300 includes steps S310 to S330.

[0029] Step S310: Based on the multi-field coupling feature tensor, through vertex dynamic embedding processing based on a temporal convolutional network, capture the temporal coupling characteristics of material nonlinearity and geometric deformation through a sliding time window convolutional kernel to obtain a time-varying vertex attribute vector; In this step, the temporal convolutional network is used to process the time-dependent information in the multi-field coupling feature tensor, especially the coupling characteristics of materials and geometric deformations over time. Through the sliding time window convolutional kernel, the nonlinear behavior of materials (such as elastoplastic deformation) and geometric deformations (such as the shape changes of nodes) 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 information such as displacement, stress, strain, and deformation. This vector not only reflects the current state of the node but also reveals how the node changes its response over time under seismic action.

[0030] Step S320: Construct a matrix based on the time-varying vertex attribute vector, calculate the attention weights by introducing an exponential decay function of the ground motion duration, and obtain an adaptive edge weight matrix; Specifically, the time-varying vertex attribute vector forms the vertex attribute matrix of the graph through matrix construction. Then, by introducing an exponential decay function, the model can simulate the decay effect of ground motion over time. The duration of ground motion has an important impact on the connection relationship between nodes: short-term shocks may lead to an immediate strong coupling between nodes, while long-term vibrations may lead to a gradual change in the relationship between nodes. The exponential decay function gradually attenuates the intensity and influence of ground motion into the model through time weighting, so as to better reflect the dynamic coupling relationship between nodes in different time periods. Combining the above time decay characteristics, the attention mechanism is used to calculate the adaptive weight of each edge, which enables the edge weight to dynamically change according to the different intensities and durations of ground motion, thus more accurately reflecting the intensity and change trend of the interaction between nodes. The adaptive edge weight matrix provides more accurate connection information for topological structure modeling, ensuring that the model can truly reflect the actual connection and interaction of nodes under seismic action.

[0031] Step S330: Based on the adaptive edge weight matrix, construct a topological connection relationship that conforms to the actual seismic response evolution law by combining the transition probability of the structural damage state, and obtain a time-varying graph structure model.

[0032] It is understandable that the adaptive edge weight matrix has adjusted the connection strength between nodes through the foregoing steps. In this step, the transition probability of the damage state is introduced into the topological connection relationship to reflect the dynamic changes of the structure under earthquake action. Specifically, the damage state of the 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, that is, the probability that the node changes from the undamaged state to the partially damaged, completely damaged, or restored state. The transition laws of these damage states will affect the connection relationship 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 to accurately simulate the response evolution of the structure under earthquake action. The construction of the time-varying graph structure model enables each node to be not only connected to other nodes in space but also, over time, as the damage state evolves, adjust the connection strength with other nodes. Such a topological model not only reflects the physical state of the nodes but also can simulate their mutual influence with surrounding nodes, revealing the response changes of the nodes during the earthquake process.

[0033] Furthermore, step S400 includes steps S410 to S430.

[0034] Step S410: Initialize the damage state space according to the time-varying graph structure model. By establishing the geometric mapping relationship between the node displacement and local damage, obtain the initial damage distribution field. Under earthquake action, the displacement of a node usually exhibits non-linear 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 action can be clearly described. Specifically, the displacement of a node is the result of the combined action of external ground motion and internal structural response and is expressed as time series data. By mapping the displacement data of the node to the damage state, the initial damage state of each node can be obtained. This initial damage distribution field reflects the possible damage conditions in different parts of the node at the initial stage of the earthquake, providing a basis for subsequent damage evolution simulation and ensuring that the damage analysis starts from a reasonable initial state.

[0035] Step S420: Conduct stochastic differential equation modeling based on the initial damage distribution field. By setting the Poisson jump term triggered by the energy exceeding the limit of ground motion, couple the progressive damage and sudden damage evolution processes to obtain a set of damage evolution paths. It should be noted that in order to capture different damage modes of nodes under earthquakes, especially the interaction between progressive damage and sudden damage, a Poisson jump term is introduced in this step to set the triggering condition for the exceeding of ground motion energy, so as to realize the coupling of the two. Stochastic differential equations can take into account the uncertainty and time-dependence of the system. By setting the triggering condition for exceeding the limit (such as when the local ground motion energy exceeds a certain threshold, the damage of the node will increase sharply), it simulates the behavior of the structure in different earthquake stages. In this model, the Poisson jump term is used to simulate the occurrence of sudden damage. For example, under the impact of a strong earthquake, the node may suddenly undergo severe damage. The mechanism of this sudden damage usually cannot be captured by a simple continuous model, so the Poisson jump term provides an effective mathematical representation for it. At the same time, progressive damage describes the process of the node gradually accumulating damage under the action of small ground motions. By coupling these two damage modes, the model can generate a set of possible damage paths. These paths reflect the different damage evolution processes that the node may experience under earthquakes of different intensities. The set of damage evolution paths provides multiple possible damage modes for the subsequent seismic resilience assessment and diverse risk analysis perspectives for the optimization of structural design.

[0036] Step S430: According to the set of damage evolution paths, perform index fusion. By fusing the energy dissipation spectrum, the recovery ability gradient, and the change rate of topological connectivity into a unified metric standard, a quantitative index of seismic resilience is obtained.

[0037] It can be understood that the energy dissipation spectrum reflects the energy absorption ability of the node under earthquake action. It can describe how the node absorbs earthquake energy through plastic deformation to slow down the damage of the structure. The recovery ability gradient measures the recovery ability of the node after an earthquake, especially how the structure gradually returns to its original state or a new equilibrium state after experiencing damage. The change rate of topological connectivity reveals the change of the connection relationship between nodes during the 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. Fusing these indicators into a unified metric standard enables the 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 the earthquake, including the performance in aspects such as energy absorption, recovery ability, and structural stability.

[0038] Furthermore, step S500 includes steps S510 to S530.

[0039] Step S510: According to the quantitative index of seismic resilience, by constructing the covariance gradient field on the Riemannian manifold, quantify the modal correlation degree between the energy dissipation spectrum and the damage rate, and obtain the master modal sensitivity spectrum; At the core of this process is the introduction of the manifold covariance matrix to describe the statistical correlation between different damage modes, in order to determine which modes have a major impact on the seismic performance of the nodes. The role of the Riemannian manifold in this step is to represent the damage state as a high-dimensional space, enabling the characterization of the mutual relationship between different damage modes in this space through the covariance matrix. The manifold covariance matrix is defined by the following formula: ; where, is the manifold covariance matrix, used to describe the statistical correlation of damage modes in the space of the differentiable manifold; is the manifold of damage states; and are damage mode vectors, representing the spatial distribution characteristics of the -th and -th order damage modes respectively; is the average damage mode vector; is the manifold volume element, an integral measure defined by the manifold metric tensor ; is the manifold metric tensor, a second-order symmetric tensor defining the local geometric properties of the damage state manifold; represents the transpose of the matrix.

[0040] By calculating the covariance matrix, the master mode sensitivity spectrum can be obtained, which quantifies the modal correlation degree between energy dissipation and damage rate.

[0041] Step S520: Perform path search in the topological graph model according to the master mode sensitivity spectrum to obtain the set of potential failure paths; It can be understood that by analyzing the master mode sensitivity spectrum, it is possible to identify which modes have the greatest impact on the seismic resilience of the structure and provide key clues for path search. During 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 during the path search process, thereby improving the efficiency of path search. The form of the heuristic function is: ; where, is the heuristic function value, used to evaluate the priority of node in the path search; is the node strain energy density gradient, reflecting the spatial change rate of strain energy at this node; is the damage time decay coefficient; is the node time evolution parameter, representing the time accumulation from the initial state to the current node state.

[0042] By introducing a heuristic function, the importance of nodes in path search can be more accurately evaluated, improving the efficiency and accuracy of path search.

[0043] Step S530: Screen according to the set of potential failure paths. By calculating the path Betti number and the Leray - Schauder degree, eliminate the paths with non - closed topological structures or non - integrable mathematics to obtain the failure mode analysis result.

[0044] It can be understood that the Betti number is used to describe the number of connected components in a graph and the complexity of the topological structure, while the Leray - Schauder degree is used to evaluate the topological properties of the path to ensure that it conforms to the actual earthquake response evolution law. By calculating these topological features, the system can eliminate those paths that do not conform to the actual earthquake dynamic response, such as those with non - closed topological structures or those that cannot be realized in physical space. Through this screening process, a set of failure paths that meet physical feasibility is finally obtained.

[0045] Furthermore, step S600 includes steps S610 to S630.

[0046] Step S610: Perform probability density fusion according to the failure mode analysis result. By constructing a time - varying weight function to quantify the entropy increase rate of the main control failure path and fusing the spatio - temporal heterogeneity characteristics of damage accumulation and recovery ability, obtain the comprehensive toughness probability distribution. Specifically, the entropy increase rate reflects the evolution rate of the system from an ordered state to a disordered state. In seismic resilience analysis, the entropy increase rate can describe the damage accumulation and evolution process of nodes or structures under earthquake action. By constructing a time - varying weight function, the system can incorporate the dynamic changes of failure paths into the analysis and at the same time quantify the time - variability of the contribution of these paths to the overall resilience of the structure. The time - varying weight function will adjust the weight of each path according to the earthquake duration and the evolution of the node damage state, so as to more accurately reflect the actual impact of the path.

[0047] In addition, the spatio - temporal heterogeneity characteristics of damage accumulation and recovery ability are also fused into this process. These characteristics include the differences in damage development and recovery processes of nodes at different times and spatial positions. By fusing these spatio - temporal heterogeneity characteristics, the non - uniform response of nodes during an earthquake can be more accurately captured, and then a comprehensive comprehensive toughness probability distribution can be obtained.

[0048] Step S620: Simulate according to the comprehensive toughness probability distribution to obtain the node toughness correlation map. It should be noted that the comprehensive toughness probability distribution has comprehensively considered information such as the damage evolution, recovery ability, and energy dissipation of nodes under different seismic conditions. Next, by simulating these data, a node toughness correlation map is generated. This map can intuitively display the damage and recovery processes of nodes under seismic action, as well as their time-varying behavior under different vibration conditions. The toughness state of nodes will be represented in the map by different colors, sizes, or shapes, etc., in order to facilitate the identification of the seismic resistance ability and vulnerable parts of nodes.

[0049] By simulating the performance of nodes in a variety of seismic scenarios, the node toughness correlation map can not only display the seismic resistance performance of nodes in the current seismic event, but also reflect the damage evolution that nodes may experience in future possible earthquakes. This map provides a visual basis for subsequent structural design and seismic strategy optimization.

[0050] Step S630: Evaluate according to the node toughness correlation map. By constructing a joint order parameter space of the strain field gradient and the energy dissipation rate, and dividing the phase transition boundary of the structure from elastic response to failure, the seismic toughness analysis result of the node is obtained.

[0051] 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. The joint analysis of these two can reveal the deformation and energy dissipation capabilities of the node under seismic action, and construct a multi-dimensional evaluation criterion through the joint order parameter space. Through this space, different states such as the elastic response area, plastic deformation area, and failure area of the node under seismic action can be clearly identified. Next, a phase transition boundary is constructed to divide the transition region of the node from elastic response to failure. This process determines the turning point of the node from a recoverable state to an irrecoverable damage state by defining the critical values of the strain field and the 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 an earthquake and provide a basis for structural optimization and seismic design.

[0052] Finally, based on the joint analysis results of the strain field gradient and the energy dissipation rate, the seismic toughness analysis result of the node can be obtained. This result not only includes the elastic response ability of the node, but also can predict the failure path and vulnerable points of the node under strong earthquakes, providing important decision-making support for subsequent design improvements.

[0053] Furthermore, step S620 includes steps S621 to S623.

[0054] Step S621: Conduct a non-linear regression analysis according to the comprehensive toughness probability distribution. By extracting the node failure sensitivity coefficient and the energy transfer path, the key propagation threshold parameter is obtained; In this step, the node failure sensitivity coefficient reflects the response ability of a node to ground motions of different intensities during an earthquake, and can reveal the vulnerability of the node at different damage levels. By analyzing the 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, especially how the earthquake input affects the energy dissipation and damage evolution of the node. Based on these data, key propagation threshold parameters can be extracted. These parameters represent the critical conditions (such as the threshold of energy dissipation, the limit of node force, etc.) at which damage or failure begins to occur under earthquake action, and provide important criteria for subsequent modeling and path analysis.

[0055] Step S622: Perform modeling based on the key propagation threshold parameters. By establishing a dynamic coupling mechanism for damage diffusion and energy redistribution among adjacent nodes, an initial cascade network model is obtained. 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, leading to further damage. The energy redistribution among adjacent nodes means that when a node is damaged, it will redistribute the energy of surrounding nodes, and this energy transfer will affect the stability of the entire system. By coupling these effects, the initial cascade network model can simulate the complex dynamic relationships between nodes and connections, and reveal the paths of node damage diffusion and the flow direction of energy transfer. In this process, the construction of the network model not only considers the connection relationships between nodes, but also incorporates the dynamic changes of energy and damage, enabling the model to update the state of nodes at each time step.

[0056] Step S623: Based on the initial cascade network model, by modifying the topological connectivity of the propagation path, a network map reflecting the correlation characteristics of ground motion input - local damage - global topological degradation is generated to obtain the node toughness correlation map.

[0057] It should be noted that the topological connectivity is a key index describing the connection strength between nodes in the network, which determines the efficiency of information or energy transfer between nodes. Under earthquake action, the damage of nodes will lead to changes in the structural topological relationship. Especially after a node fails, the connection relationship may break, thereby affecting the stability of the entire structure. By modifying the topological connectivity of the propagation path, these changes can be dynamically reflected, so as to accurately simulate the performance of nodes during an earthquake. The generated network map reflects how ground motion input triggers local damage and affects the change of the global topological structure through energy redistribution among nodes. Through this map, it is possible to clearly identify which nodes are the weak points of the system and which paths may lead to global failure during an earthquake. Embodiment 2:

[0058] As shown in Figure 2 the figure, this embodiment provides a seismic resilience analysis system for segmental prefabricated railway high pier joints, and the system includes: An acquisition module 901, configured to acquire the basic parameters of the target segmental prefabricated railway high pier joint, where the basic parameters include material parameters, pier geometric parameters, ground motion parameters, and joint response parameters; A decomposition module 902, configured to perform multi-physical field coupling tensor decomposition according to the basic parameters to obtain a multi-field coupling characteristic tensor; A modeling module 903, configured to perform topological modeling according to the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; A simulation module 904, configured to perform damage evolution simulation according to the time-varying graph structure model to obtain a seismic resilience quantification index; An analysis module 905, configured to perform modal sensitivity analysis and failure path extraction according to the seismic resilience quantification index to obtain a failure mode analysis result; An evaluation module 906, configured to perform evaluation according to the failure mode analysis result to obtain a joint seismic resilience analysis result.

[0059] In a specific implementation manner of the present invention, the decomposition module 902 includes: A first decomposition unit, configured to perform multi-modal fusion according to the basic parameters, and obtain a cross-physical field fusion tensor by establishing the derivative correlation between the material parameters and the geometric parameters; A second decomposition unit, configured to perform linear embedding processing combining time decay and spectrum weighting according to the cross-physical field fusion tensor, and obtain a time-varying dynamic characteristic matrix by fusing the short-time impact and long-time cumulative effects of ground motion; A third decomposition unit, configured to perform tensor decomposition processing according to the time-varying dynamic characteristic matrix to obtain a multi-field coupling characteristic tensor, and the multi-field coupling characteristics include the correlation characteristics of tensor material nonlinearity and geometric deformation, and the strain-temperature gradient coupling characteristics.

[0060] In a specific implementation manner of the present invention, the modeling module 903 includes: A first modeling unit, configured to perform vertex dynamic embedding processing based on a time convolutional network according to the multi-field coupling characteristic tensor, and obtain a time-varying vertex attribute vector by capturing the time sequence coupling characteristics of material nonlinearity and geometric deformation through a sliding time window convolutional kernel; A second modeling unit, configured to perform matrix construction according to the time-varying vertex attribute vector, and obtain an adaptive edge weight matrix by introducing an exponential decay function of ground motion duration to calculate attention weights; A third modeling unit, configured to construct a topological connection relationship that conforms to the actual seismic response evolution law by combining the transition probability of the structural damage state according to the adaptive edge weight matrix to obtain a time-varying graph structure model. Embodiment 3:

[0061] Corresponding to the above method embodiment, in this embodiment, an anti-seismic toughness analysis device for segmental prefabricated railway high piers is also provided. An anti-seismic toughness analysis device for segmental prefabricated railway high piers described below can be correspondingly referred to with an anti-seismic toughness analysis method for segmental prefabricated railway high piers described above.

[0062] Figure 3 It is a block diagram of an anti-seismic toughness analysis device 800 for segmental prefabricated railway high piers shown according to an exemplary embodiment. As Figure 3 shown, the anti-seismic toughness analysis device 800 for segmental prefabricated railway high piers may include: a processor 801, a memory 802. The anti-seismic toughness analysis device 800 for segmental prefabricated railway high piers may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0063] Among them, the processor 801 is used to control the overall operation of the segment prefabricated high pier node seismic resilience analysis device 800 to complete all or part of the steps in the above-mentioned segment prefabricated 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 prefabricated high pier node seismic resilience analysis device 800. These data may include, for example, instructions for any application program or method operating on the segment prefabricated high pier node seismic resilience analysis device 800, as well as application program-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. 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 disc. The multimedia component 803 may include a screen and an audio component. Among them, the screen can 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, and the microphone is used to receive external audio signals. The received audio signals can be further stored in the memory 802 or sent through 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 the segment prefabricated high pier node seismic resilience analysis device 800 to communicate with other devices in a wired or wireless manner. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0064] In an exemplary embodiment, a seismic resilience analysis device 800 for segmental prefabricated railway high pier joints 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, and is used to execute the above-mentioned seismic resilience analysis method for segmental prefabricated railway high pier joints.

[0065] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned seismic resilience analysis method for segmental prefabricated railway high pier joints are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above program instructions can be executed by the processor 801 of a seismic resilience analysis device 800 for segmental prefabricated railway high pier joints to complete the above-mentioned seismic resilience analysis method for segmental prefabricated railway high pier joints.

[0066] As mentioned above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A method for analyzing the seismic toughness of segmental assembled 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, ground 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; Perform topological modeling according to the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; Perform damage evolution simulation according to 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 result is obtained through modal sensitivity analysis and failure path extraction; An evaluation is performed based on the failure mode analysis results to obtain node seismic toughness analysis results.

2. The method for analyzing seismic toughness of segmental assembled 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 attenuation and spectral weighting; Tensor decomposition 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 the correlation characteristics of tensor material nonlinearity and geometric deformation, and the strain-temperature gradient coupling characteristics.

3. The method for analyzing seismic toughness of segmental assembled 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 feature tensor, after vertex dynamic embedding processing based on a time convolution 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, and a time-varying graph structure model is obtained.

4. The method for analyzing seismic toughness of segmental assembled railway high pier nodes according to claim 1 is characterized in that: 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 an initial damage distribution field by establishing a geometric mapping relationship between node displacement and local damage; A stochastic differential equation modeling is performed according to the initial damage distribution field, and a Poisson jump term is triggered by setting the seismic energy exceeding the limit, and the gradual damage and sudden damage evolution processes are coupled to obtain a set of damage evolution paths; The indicators are fused according to the damage evolution path set, and the quantitative indicator of seismic toughness is obtained by fusing the energy dissipation spectrum, the recovery capacity gradient and the topological connectivity change rate into a unified metric.

5. The method for analyzing seismic toughness of segmental assembled railway high pier nodes according to claim 1 is characterized in that: According to the quantitative index of seismic toughness, modal sensitivity analysis and failure path extraction are carried out, including: According to the quantitative index of seismic toughness, by constructing a covariance gradient field on a Riemann manifold, the modal correlation between the energy dissipation spectrum and the damage rate is quantified, and the main control modal sensitivity spectrum is obtained; Performing 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 are eliminated by calculating the path Betti number and the Leray-Sauder degree to obtain a failure mode analysis result.

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

7. The method for analyzing seismic toughness of segmental assembled railway high pier nodes according to claim 6 is characterized in that: A simulation is performed according to the comprehensive toughness probability distribution to obtain a node toughness association map, including: According to the comprehensive toughness probability distribution, nonlinear regression analysis is performed to obtain key propagation threshold parameters by extracting node failure sensitivity coefficients and energy transfer paths; Modeling is performed according to 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 of 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.

8. A segmental assembly railway high pier node seismic toughness analysis system, characterized in that: include: An acquisition module is used to acquire basic parameters of the target segment prefabricated railway high pier nodes, wherein the basic parameters include material parameters, pier body geometry parameters, ground motion parameters and node response parameters; A decomposition module, used for decomposing the multi-physical field coupling tensor according to the basic parameters to obtain a multi-field coupling characteristic tensor; A modeling module, used for performing topological modeling according to the multi-field coupling characteristic tensor to obtain a time-varying graph structure model; A simulation module, used to simulate the damage evolution according to the time-varying graph structural model to obtain a quantitative index of seismic toughness; An analysis module, used to obtain failure mode analysis results through modal sensitivity analysis and failure path extraction according to the seismic toughness quantitative index; The evaluation module is used to evaluate according to the failure mode analysis results to obtain node seismic toughness analysis results.

9. The segmental assembled railway high pier node seismic toughness analysis system according to claim 8 is characterized in that: The decomposition module comprises: A first decomposition unit is used to perform multi-modal 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 attenuation 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.

10. The segmental assembled railway high pier node seismic toughness analysis system according to claim 8, characterized in that: The modeling module includes: A first modeling unit is used 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 feature tensor through vertex dynamic embedding processing based on a time convolution network; A second modeling unit is used to construct a matrix according to the time-varying vertex attribute vector, and calculate the attention weight by introducing an exponential decay function of the earthquake duration to obtain an adaptive edge weight matrix; The third modeling unit is used to construct a topological connection relationship that conforms to the actual seismic response evolution law according to the adaptive edge weight matrix by combining the transition probability of the structural damage state to obtain a time-varying graph structure model.

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