Vulnerability Analysis Method and System of Support Steel Frame Based on Learning Algorithm Analysis

Abstract

The present invention discloses a vulnerability analysis method and system for supporting steel frames based on learning algorithm analysis, which relates to the technical field of supporting steel frame analysis. By obtaining and analyzing the design drawings of supporting steel frames with historical detection records, a structural expression model is constructed, and vulnerable blocks are divided and structural parameters are configured; the correlation between the damage characteristics of the supporting steel frames and time and earthquake intensity is analyzed, and a damage dynamic performance model is established; similarity analysis is carried out on all structural expression models of the supporting steel frames, model sets are classified, and a set of characteristic feature factors is extracted; in actual vulnerability analysis, according to the matching situation between the supporting steel frame and the set of characteristic feature factors, the corresponding set of structural expression models of the supporting steel structure is determined, and the damage dynamic performance model in this model set is used for vulnerability assessment. The above technical solution of the present invention improves the accuracy and efficiency of vulnerability assessment, and provides a scientific and effective technical means for the design, assessment and maintenance of supporting steel frames.

Description

Technical Field

[0001] The present invention relates to the technical field of support steel frame analysis, and in particular to a vulnerability analysis method and system for support steel frames based on learning algorithm analysis. Background Art

[0002] In the field of building structure engineering, as a key component for bearing and transferring loads, the stability and safety of support steel frames are crucial for the durability of the entire structure. However, in practical applications, support steel frames often face various complex external environments and load conditions, such as earthquakes and fatigue effects during long-term use. These factors may lead to a decline in the performance of the steel frame structure or even damage. Most traditional support steel frame design and evaluation methods rely on empirical formulas, static analysis, and simple material mechanics tests. Although these methods can ensure the basic safety of the structure to a certain extent, it is difficult to comprehensively and accurately predict and evaluate the vulnerability of the steel frame under various complex working conditions.

[0003] In particular, for large and complex support steel frame structures, their stress states and damage modes often have high nonlinearity and uncertainty, and traditional analysis methods often have difficulty capturing these subtle and critical changes. In addition, as the service life of the structure increases, factors such as material aging and environmental corrosion will gradually affect the performance of the steel frame, and these long-term effects are often simplified or ignored in traditional evaluation methods. Summary of the Invention

[0004] The purpose of the present invention is to provide an analysis method and system that can accurately analyze the vulnerability of support steel frames.

[0005] The present invention discloses a vulnerability analysis method for support steel frames based on learning algorithm analysis, including:

[0006] Obtain several support steel frame design drawings with historical detection records, analyze each support steel frame design drawing, construct a support steel frame structure expression model, divide the vulnerable areas of the support steel frame structure expression model, and configure structural parameters for each vulnerable area;

[0007] Analyze the historical detection records corresponding to each support steel frame structure expression model, determine the damage characteristics of the support steel frame in different time periods, establish the corresponding relationship between the damage characteristics and the time periods, analyze the historical earthquake data, determine the earthquake intensity parameters in different time periods, and establish the temporal correlation relationship between the damage characteristics and the earthquake intensity parameters based on the equivalent corresponding time periods;

[0008] Analyze the damage characteristics, and based on the analysis results, adjust the damage expression of the corresponding support steel frame structure expression model. Based on the change of the damage characteristics over time, obtain the first dynamic performance of the support steel frame structure expression model. Based on the correlation relationship between the damage characteristics and the earthquake intensity parameters over time, establish the correlation relationship between the change of the earthquake intensity parameters and the first dynamic performance over time, and obtain the damage dynamic performance model of the support steel structure;

[0009] Conduct a structural similarity analysis on all support steel frame structure expression models. Based on the analysis results, classify the support steel frame structure expression models to obtain several support steel frame structure expression model sets. Conduct a characteristic factor analysis on each support steel frame structure expression model set to obtain several characteristic factor groups, and establish the correlation relationship between the characteristic factor groups and the support steel structure expression model sets;

[0010] When conducting a vulnerability analysis on the support steel frame, determine the support steel structure expression model set corresponding to the support steel frame based on the matching situation between different characteristic factor groups and the support steel frame. Based on the damage dynamic performance model of the support steel structure corresponding to the support steel structure expression model set, conduct a vulnerability expression for the support steel structure.

[0011] In some embodiments disclosed by the present invention, the method for dividing the vulnerable area of the support steel frame structure expression model includes:

[0012] Conduct a finite element analysis on the support steel frame structure expression model, including mesh generation, dividing it into several model units, establishing the correlation relationship between adjacent model units, and configuring material properties for the model units, including elastic modulus, Poisson's ratio, and density;

[0013] Apply boundary conditions to the support structure expression model after mesh generation, and apply conventional loads to the support structure expression model after mesh generation;

[0014] Based on historical earthquake data, determine the input parameters of the finite element analysis to obtain the finite element results of the support steel frame structure expression model. Based on the finite element analysis results, determine the area on the support steel frame structure expression model where the stress is greater than or equal to the preset range, record it as the damage - concerned area, and based on the structural type to which the damage - concerned area belongs, determine the defined range of the damage - concerned area to obtain the vulnerable area.

[0015] In some embodiments disclosed by the present invention, the method for determining the damage characteristics of the support steel frame in different time periods includes:

[0016] Analyze historical detection records to determine the damage characteristics of different damage characteristic types, establish damage characteristic comparison rules based on the structural impact of each damage characteristic type on the support steel frame, and based on the damage characteristic comparison rules, determine the negative structural impact parameters of the damage characteristics of different damage characteristic types in different vulnerable blocks;

[0017] Statistically analyze the negative structural impact parameters of different damage characteristic types corresponding to each vulnerable block and configure them in the vulnerable expression components of each vulnerable block.

[0018] In some embodiments disclosed by the present invention, the method for adjusting damage expression of the support steel frame structure expression model includes:

[0019] Analyze the support steel frame structure expression model, set parallel central virtual lines for the connecting rods of the support steel frame structure expression model, make several perpendicular parameter expression circles relative to the central virtual lines, and the centers of the parameter expression circles coincide with the central virtual lines. Set parameter expression spheres for the intersection points of the central virtual lines;

[0020] Determine the diameter of the parameter expression circle or the parameter expression sphere based on the negative structural impact parameter of the corresponding vulnerable block: ;

[0021] Wherein, is the diameter of the parameter expression circle or the parameter expression sphere, is the diameter conversion adjustment coefficient, is the negative structural impact parameter, is the negative structural impact parameter influence adjustment coefficient, is the negative structural impact parameter influence adjustment constant.

[0022] In some embodiments disclosed by the present invention, the method for performing structural similarity analysis on all support steel frame structure expression models includes:

[0023] Establish a structural analysis space for the support steel frame structure expression model, uniformly set several space nodes in the structural analysis space, and trigger and mark the space nodes mapped on the support steel frame structure expression model, denoted as trigger marked points;

[0024] Randomly select several trigger marked points and perform random combinations to obtain several trigger marked point groups. Analyze the inter-point distances between the trigger marked points in each trigger marked point group and calculate the average value of the inter-point distances, denoted as the marked point inter-distance;

[0025] Randomly select trigger marker points and denote them as analysis trigger marker points. Determine the inter-point distances between the trigger marker points beside the analysis trigger marker points, calculate the average value of the inter-point distances, denote it as the average inter-point distance, and calculate the ratio of the average inter-point distance to the distance between the marker points, denote it as the inter-point distance reference ratio. If the inter-point distance reference ratio is greater than or equal to the preset value, mark the corresponding analysis trigger marker points, denoted as high-density trigger marker points;

[0026] Combine the interconnected high-density trigger marker points to obtain a high-density trigger marker point group, and denote the space corresponding to the high-density trigger marker point group as the structurally high-dense space;

[0027] Conduct spatial proportion analysis among the structurally high-dense spaces, and based on the order of the spatial proportions, construct spatial vector lines between the structurally high-dense spaces. The method for constructing the spatial vector lines includes determining the center points of the structurally high-dense spaces and connecting the spatial vector lines between the center points;

[0028] Perform alignment adjustments of scaling and rotation on different spatial vector lines, compare the proximity between the spatial vector lines, and screen and classify the support steel frame structure expression models based on the proximity.

[0029] In some embodiments disclosed by the present invention, the method for comparing the proximity between spatial vector lines includes:

[0030] Determine the turning nodes of different spatial vector lines, quantitatively analyze the turning node distances between the corresponding turning nodes of the spatial vector lines, and determine the proximity between the spatial vector lines based on the turning node distances;

[0031] Among them, the method for determining the proximity includes judging the preset turning node distance interval to which each turning node distance belongs, and based on the belonging turning node distance interval, determining the sub-proximity between the corresponding turning nodes. If the sub-proximity is greater than or equal to the preset value, the turning node is identified as a coincident turning node, and based on the node proportion of the coincident turning nodes in all turning nodes, determine the proximity between the spatial vector lines;

[0032] Among them, the expression for calculating the proximity is: ;

[0033] Among them, is the proximity, is the coincidence judgment function between the th turning nodes. If the sub-proximity between the turning nodes is greater than or equal to the preset value, then outputs 1, otherwise outputs 0, is the node proportion influence adjustment constant, is the adjustment coefficient affected by the node ratio.

[0034] In some embodiments disclosed by the present invention, the method for analyzing the characteristic factors of each support steel frame structure expression model set includes:

[0035] Determine the number of high-density spaces in the structural high-density space of the support steel frame expression model in the support steel frame structure expression model set, and recognize it as the first characteristic factor;

[0036] Determine the turning angles of the spatial vector lines of the high-density space, and sort the turning angles based on the order of the turning points to obtain a turning angle sequence, which is recognized as the second characteristic factor.

[0037] In some embodiments disclosed by the present invention, the method for analyzing the characteristic factors of each support steel frame structure expression model set further includes:

[0038] Determine the occupied volume of the model space of the support steel frame expression model in the support steel frame structure expression model set, and recognize it as the third characteristic factor.

[0039] In some embodiments disclosed by the present invention, a support steel frame vulnerability analysis system based on learning algorithm analysis is also disclosed, including:

[0040] The first module is used to obtain several support steel frame design drawings with historical detection records, analyze each support steel frame design drawing, construct a support steel frame structure expression model, divide the vulnerable blocks of the support steel frame structure expression model, and configure structural parameters for each vulnerable block;

[0041] The second module is used to analyze the historical detection records corresponding to each support steel frame structure expression model, determine the damage characteristics of the support steel frame in different time periods, establish the corresponding relationship between the damage characteristics and the time periods, analyze the historical earthquake data, determine the earthquake intensity parameters in different time periods, and establish the temporal correlation relationship between the damage characteristics and the earthquake intensity parameters based on the equivalent corresponding relationship of time periods;

[0042] The third module is used to analyze the damage characteristics, and based on the analysis results, adjust the damage expression of the corresponding support steel frame structure expression model, obtain the first dynamic performance of the support steel frame structure expression model based on the change of the damage characteristics over time, and establish the temporal correlation relationship between the change of the earthquake intensity parameters and the first dynamic performance based on the temporal correlation relationship between the damage characteristics and the earthquake intensity parameters, to obtain the support steel structure damage dynamic performance model;

[0043] The fourth module is used to perform structural similarity analysis on all support steel frame structure expression models, classify the support steel frame structure expression models based on the analysis results to obtain several support steel frame structure expression model sets, perform marker feature factor analysis on each support steel frame structure expression model set to obtain several marker feature factor groups, and establish the association relationship between the marker feature factor groups and the support steel structure expression model sets;

[0044] The fifth module is used to determine the support steel structure expression model set corresponding to the support steel frame based on the matching condition between different marker feature factor groups and the support steel frame when performing vulnerability analysis on the support steel frame, and perform vulnerability expression on the support steel structure based on the support steel structure damage dynamic performance model corresponding to the support steel structure expression model set.

[0045] The present invention discloses a method and system for analyzing the vulnerability of a support steel frame based on learning algorithm analysis, which relates to the technical field of support steel frame analysis. By obtaining and analyzing the design drawings of the support steel frame with historical detection records, constructing a structural expression model, and dividing the vulnerable areas and configuring structural parameters; analyzing the correlation relationship between the damage characteristics of the support steel frame and time and the earthquake intensity, establishing a damage dynamic performance model; performing similarity analysis on all support steel frame structure expression models, classifying to obtain model sets, and extracting marker feature factor groups; in actual vulnerability analysis, determining the corresponding support steel structure expression model set according to the matching condition between the support steel frame and the marker feature factor groups, and using the damage dynamic performance model in the model set for vulnerability assessment. The above technical solution of the present invention improves the accuracy and efficiency of vulnerability assessment, and provides a scientific and effective technical means for the design, assessment and maintenance of the support steel frame.

[0046] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0047] Figure 1 It is a method step diagram of the method for analyzing the vulnerability of a support steel frame based on learning algorithm analysis disclosed in the embodiment of the present invention. Detailed Embodiment

[0048] The technical solution of the present invention will be further described below through the drawings and embodiments.

[0049] The technical solution of the present invention will be clearly and completely described below in combination with the drawings and specific embodiments. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and cannot be understood as a limitation on the protection scope of the present invention. Those skilled in the art can make some non-essential improvements and adjustments according to the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by those skilled in the art of the present invention.

[0050] Embodiment:

[0051] The present invention discloses a vulnerability analysis method for a support steel frame based on learning algorithm analysis. Refer to Figure 1 , including:

[0052] Step S100, obtain several design drawings of support steel frames with historical detection records, analyze each design drawing of the support steel frame, construct a structural expression model of the support steel frame, divide the vulnerable blocks of the structural expression model of the support steel frame, and configure structural parameters for each vulnerable block.

[0053] This step is the starting stage of the method, and the main purpose is to establish a structural expression model of the support steel frame. First, by collecting design drawings of support steel frames with historical detection records, these drawings contain structural information, material information, dimension information, etc. of the steel frame. Then, use professional analysis software or algorithms to deeply analyze these drawings to construct a structural expression model of the support steel frame. Next, according to the structural characteristics and force conditions of the steel frame, divide the vulnerable blocks of the model for more accurate analysis of the vulnerable parts of the steel frame in the future. Finally, configure structural parameters such as strength, stiffness, and toughness for each vulnerable block, and these parameters will be used as the basis for subsequent analysis.

[0054] A vulnerable block refers to an area in the structural expression model of the support steel frame that is determined according to the finite element analysis results and historical earthquake data and is prone to damage under external actions such as earthquakes. Due to stress concentration, weak material properties, or structural design reasons, these areas are more likely to be damaged when subjected to external loads, such as cracks, deformations, or failures. By dividing the vulnerable blocks, the vulnerable parts of the steel frame can be analyzed more specifically, providing important guidance for subsequent structural reinforcement and seismic design.

[0055] In some embodiments disclosed by the present invention, the method for dividing the vulnerable blocks of the structural expression model of the support steel frame includes:

[0056] Step S101, perform finite element analysis on the structural expression model of the support steel frame, including mesh division, dividing it into several model units, establishing the association relationship between adjacent model units, and configuring material properties for the model units, including elastic modulus, Poisson's ratio, and density.

[0057] In the process of dividing the vulnerable areas of the support steel frame structure, step S101 is a crucial step, which involves finite element analysis of the structural expression model. Finite element analysis is a numerical analysis method that simplifies complex problems into a series of simple problems for solution. In this step, first, the support steel frame structure needs to be meshed and subdivided into several small model units. These model units are interconnected to form a whole, enabling the simulation of the mechanical behavior of the entire structure. Subsequently, material properties such as elastic modulus, Poisson's ratio, and density are assigned to each model unit. These properties are key parameters describing the mechanical properties of materials. Through finite element analysis, physical quantities such as stress and strain of each model unit under load can be calculated, providing basic data for subsequent determination of vulnerable areas. The advantage of finite element analysis is that it can adapt to various complex shapes and structures and has high calculation accuracy, so it has been widely used in engineering analysis.

[0058] In finite element analysis, the correlation between adjacent model units is achieved through meshing. First, the support steel frame structure is divided into several small model units, which are physically connected. Then, the correlation between them is established by setting nodes or interfaces between the units. This correlation ensures that when a unit is subjected to an external load, its physical quantities such as stress and strain can be transmitted to adjacent units, thus simulating the mechanical behavior of the entire structure. The establishment of this correlation is the basis of finite element analysis and the key to subsequent determination of vulnerable areas and vulnerability analysis.

[0059] In step S102, boundary conditions are applied to the support structure expression model after meshing, and conventional loads are applied to the support structure expression model after meshing.

[0060] Step S102 is a process of applying boundary conditions and conventional loads to the support steel frame structure expression model after meshing based on finite element analysis. Boundary conditions refer to the setting of external constraint conditions for the structure during the simulation analysis, which determines the displacement and deformation mode of the structure. In the analysis of the support steel frame structure, the setting of boundary conditions needs to accurately reflect the constraint conditions of the structure in the actual working conditions, such as fixed supports, sliding supports, etc. At the same time, conventional loads also need to be applied to the structure to simulate the stress state of the structure during daily use. The application of conventional loads helps to understand the stress distribution and deformation of the structure under normal conditions and provides a reference for subsequent seismic load analysis. By applying boundary conditions and conventional loads, necessary input conditions can be provided for finite element analysis, thus more accurately simulating the mechanical behavior of the structure.

[0061] Boundary conditions refer to the settings of external constraint conditions for a structure during the simulation analysis process, which determine the displacement and deformation modes of the structure. In the analysis of a support steel frame structure, the application of boundary conditions needs to accurately reflect the constraint conditions of the structure in the actual working conditions, such as fixed supports, sliding supports, etc. This is usually achieved by applying constraint conditions at specific positions in the model, such as restricting the displacement or rotation of certain nodes.

[0062] Conventional loads refer to the conventional external forces acting on a structure during its daily use, such as gravity, wind force, etc. Applying conventional loads to the expression model of the bracket structure after mesh division is to simulate the stress state of the structure under normal conditions and understand its stress distribution and deformation in daily use. This is usually achieved by applying loads at the corresponding positions in the model, such as applying uniform loads or concentrated loads. By applying boundary conditions and conventional loads, necessary input conditions can be provided for finite element analysis, so as to more accurately simulate the mechanical behavior of the structure.

[0063] Step S103: Based on historical earthquake data, determine the input parameters for finite element analysis, obtain the finite element results of the support steel frame structure expression model, based on the finite element analysis results, determine the blocks on the support steel frame structure expression model where the stress is greater than or equal to the preset range, record them as damage - concerned blocks, and based on the structural type to which the damage - concerned blocks belong, determine the delineation range of the damage - concerned blocks to obtain vulnerable blocks.

[0064] Step S103 is a process of determining the vulnerable blocks on the support steel frame structure expression model based on finite element analysis results and historical earthquake data. In this step, first, the input parameters for finite element analysis need to be determined according to historical earthquake data, such as the acceleration and frequency of seismic waves, to simulate the stress condition of the structure under earthquake action. Then, through finite element analysis, calculate the stress distribution and deformation of the structure under earthquake loads. Next, according to the preset stress range, select the blocks where the stress value is greater than or equal to this range, and these blocks are regarded as damage - concerned blocks. The determination of damage - concerned blocks helps to narrow the scope of subsequent analysis and improve analysis efficiency. Finally, based on the structural type to which the damage - concerned blocks belong (such as beam - column joints, support members, etc.), combined with the mechanical properties of the structure and the stress characteristics under earthquake action, reasonably delineate the range of vulnerable blocks. The determination of vulnerable blocks has important guiding significance for subsequent structural reinforcement and seismic design.

[0065] Step S200: Analyze the historical detection records corresponding to each support steel frame structure expression model, determine the damage characteristics of the support steel frames in different time periods, establish the corresponding relationship between the damage characteristics and time periods, analyze the historical earthquake data, determine the seismic intensity parameters in different time periods, and based on the equivalent corresponding way of time periods, establish the temporal correlation relationship between the damage characteristics and seismic intensity parameters.

[0066] This step aims to establish the correlation between damage characteristics and seismic intensity parameters over time. First, carefully analyze the historical inspection records corresponding to each support steel frame structure expression model to determine the damage characteristics of the support steel frames in different time periods, such as cracks, deformations, rust, etc. Then, establish the corresponding relationship between these damage characteristics and time periods to understand how the damage of the steel frames changes over time. At the same time, analyze the historical earthquake data to determine the seismic intensity parameters in different time periods, such as magnitude, focal depth, epicentral distance, etc. Finally, through the equivalent correspondence of time periods, correlate the damage characteristics with the seismic intensity parameters over time, providing a basis for establishing a dynamic damage performance model in the follow-up.

[0067] In some embodiments disclosed by the present invention, the method for determining the damage characteristics of the support steel frames in different time periods includes:

[0068] Step S201: Analyze the historical inspection records to determine the damage characteristics of different damage characteristic types, and based on the structural impact of each damage characteristic type on the support steel frame, establish a damage characteristic comparison rule, and based on the damage characteristic comparison rule, determine the negative structural impact parameters of the damage characteristics of different damage characteristic types in different vulnerable blocks.

[0069] When evaluating the damage characteristics of the support steel frame structure over time, step S201 first focuses on the analysis of historical inspection records. The core of this step is to identify and summarize different types of damage characteristics, which may include cracks, corrosion, deformations, etc., and these pose potential threats to the integrity and stability of the structure. To deeply understand the specific impact of these damage characteristics on the support steel frame structure, it is necessary to evaluate the degree of weakening of the overall structural performance based on the type of each damage characteristic, and accordingly establish a set of damage characteristic comparison rules. This set of rules not only considers the severity of the damage itself, but also comprehensively takes into account factors such as its location, distribution in the structure, and possible chain reactions. Through this rule, we can quantify the negative structural impacts caused by different damage characteristic types in different vulnerable blocks, forming a series of specific negative structural impact parameters. These parameters provide a key basis for subsequent evaluation of the vulnerability of the structure and formulation of maintenance strategies.

[0070] Step S202: Statistically analyze the negative structural impact parameters of different damage characteristic types corresponding to each vulnerable block and configure them in the vulnerability expression components of each vulnerable block.

[0071] Step S202 further elaborately characterizes the vulnerability of the support steel frame based on Step S201. In this step, we first statistically summarize the negative structural impact parameters of different damage feature types within each vulnerable block. This statistical process not only focuses on the impact of a single damage feature but also considers the cumulative effect of multiple damage features within the same block, thus more comprehensively reflecting the vulnerability status of the block. Subsequently, these statistical results are configured into the vulnerability expression components of each vulnerable block, forming a systematic vulnerability assessment system. As an intuitive display of structural vulnerability, the vulnerability expression components can help engineers and managers quickly identify the weak links of the structure and provide targeted guidance for subsequent maintenance, repair, or reinforcement work. Through the implementation of this step, we can not only more accurately grasp the vulnerability distribution of the support steel frame structure but also provide strong support for the long-term safety management of the structure.

[0072] In Step S300, analyze the damage features, and based on the analysis results, adjust the damage expression of the corresponding support steel frame structure expression model. Based on the changes in the damage features over time, obtain the first dynamic performance of the support steel frame structure expression model. And based on the temporal correlation between the damage features and the seismic intensity parameters, establish the temporal correlation between the changes in the seismic intensity parameters and the first dynamic performance to obtain the damage dynamic performance model of the support steel structure.

[0073] The core of this step is to establish the damage dynamic performance model of the support steel structure. First, deeply analyze the damage features obtained in Step S200 to understand their impact on the performance of the steel frame structure. Then, adjust the damage expression of the support steel frame structure expression model according to the analysis results to make it more realistically reflect the damage situation of the steel frame in actual use. Next, based on the changes in the damage features over time, obtain the first dynamic performance of the support steel frame structure expression model, that is, the change of the steel frame damage over time. Finally, combine the temporal correlation between the damage features and the seismic intensity parameters to establish the temporal correlation between the changes in the seismic intensity parameters and the first dynamic performance, thereby obtaining the damage dynamic performance model of the support steel structure.

[0074] In some embodiments disclosed by the present invention, the method for adjusting the damage expression of the support steel frame structure expression model includes:

[0075] In Step S301, analyze the support steel frame structure expression model. Set parallel central virtual lines for the connecting rods of the support steel frame structure expression model, and make several perpendicular parameter expression circles relative to the central virtual line, and the centers of the parameter expression circles coincide with the central virtual line. Set parameter expression spheres for the intersection points of the central virtual line.

[0076] Step S302: Determine the diameter of the parameter expression circle or the parameter expression solid based on the negative structure influence parameter corresponding to the vulnerable block. 。

[0077] Wherein, is the diameter of the parameter expression circle or the parameter expression solid, is the diameter conversion adjustment coefficient, is the negative structure influence parameter, is the negative structure influence parameter adjustment coefficient, is the negative structure influence parameter influence adjustment constant.

[0078] Diameter conversion adjustment coefficient: This is a coefficient used to convert the negative structure influence parameter into the diameter of the parameter expression circle or the parameter expression solid. Ensure that the converted diameter can accurately reflect the magnitude of the negative structure influence parameter.

[0079] Negative structure influence parameter adjustment coefficient: This is a coefficient used to adjust the magnitude of the negative structure influence parameter. It may consider various factors, such as the severity, location, distribution of damage characteristics, and the degree of influence of these factors on the overall performance of the structure. By adjusting this coefficient, the influence of different damage characteristics on the vulnerability of the structure can be evaluated more accurately.

[0080] Negative structure influence parameter influence adjustment constant: This is a constant used in calculating the negative structure influence parameter. It may be used to ensure the accuracy and consistency of the calculation results, or to adjust the calculation results to conform to the actual situation. The specific value of this constant may be determined based on experimental data, experience, or theoretical analysis.

[0081] Step S400: Conduct a structural similarity analysis on all support steel frame structure expression models, and based on the analysis results, classify the support steel frame structure expression models to obtain several support steel frame structure expression model sets, conduct a characteristic factor analysis on each support steel frame structure expression model set to obtain several characteristic factor groups, and establish an association relationship between the characteristic factor groups and the support steel structure expression model sets.

[0082] This step aims to classify the support steel frame structure expression models and extract the characteristic factor groups of signs. First, conduct a structural similarity analysis on all support steel frame structure expression models, and classify them into several support steel frame structure expression model sets according to characteristics such as the geometric shape, material type, and connection method of the models. Then, conduct a characteristic factor analysis of signs for each model set, and extract several characteristic factor groups of signs that can represent the characteristics of the model set. These characteristic factor groups of signs will serve as an important basis for subsequent vulnerability analysis. Finally, establish the association relationship between the characteristic factor groups of signs and the support steel structure expression model sets, so as to quickly find the model set matching a specific characteristic factor group of signs in subsequent analysis.

[0083] In some embodiments disclosed by the present invention, the method for conducting a structural similarity analysis on all support steel frame structure expression models includes:

[0084] Step S401: Establish a structural analysis space for the support steel frame structure expression model, and uniformly set several space nodes in the structural analysis space. Trigger marking is performed on the space nodes mapped to the support steel frame structure expression model, and they are denoted as trigger marking points.

[0085] Step S402: Randomly select several trigger marking points and perform random combinations to obtain several trigger marking point groups. Analyze the inter-point distances between the trigger marking points in each trigger marking point group, and calculate the average value of the inter-point distances, which is denoted as the sign inter-point distance.

[0086] Step S403: Randomly select a trigger marking point and denote it as the analysis trigger marking point. Determine the inter-point distances between the trigger marking points beside the analysis trigger marking point, and calculate the average value of the inter-point distances, which is denoted as the average inter-point distance. Calculate the ratio of the average inter-point distance to the sign inter-point distance, which is denoted as the inter-point distance reference ratio. If the inter-point distance reference ratio is greater than or equal to the preset value, mark the corresponding analysis trigger marking point, which is denoted as a high-density trigger marking point.

[0087] Step S404: Combine the mutually connected high-density trigger marking points to obtain a high-density trigger marking point group, and denote the space corresponding to the high-density trigger marking point group as the structural high-density space.

[0088] Step S405: Conduct a spatial proportion analysis among the structural high-density spaces, and based on the order of the spatial proportions, construct spatial vector lines between the structural high-density spaces. The method for constructing spatial vector lines includes determining the center points of the structural high-density spaces and connecting the spatial vector lines between the center points.

[0089] The structurally high-density space refers to the area formed by the combination of high-density trigger marker points in the support steel frame structure expression model. These areas have a relatively high density and complexity in terms of structure. Conducting a spatial proportion analysis among structurally high-density spaces aims to compare the morphological, dimensional, and proportional relationships among different high-density spaces. Such analysis helps to understand the overall layout and characteristics of the structure, as well as the interactions and influences among different parts. Through the spatial proportion analysis, the characteristic factors of the structure's markers can be further extracted, providing an important basis for subsequent vulnerability analysis.

[0090] In step S406, perform scaling and rotational alignment adjustments on different spatial vector lines, compare the proximity between spatial vector lines, and based on the proximity, screen and classify the support steel frame structure expression model.

[0091] In some embodiments disclosed by the present invention, the method for comparing the proximity between spatial vector lines includes:

[0092] In step S4061, determine the turning nodes of different spatial vector lines, quantitatively analyze the distance between corresponding turning nodes among spatial vector lines, and based on the distance between turning nodes, determine the proximity between spatial vector lines.

[0093] Among them, the method for determining the proximity includes: judging the preset distance interval between turning nodes to which each distance between turning nodes belongs, and based on the distance interval between turning nodes to which it belongs, determining the sub-proximity between corresponding turning nodes. If the sub-proximity is greater than or equal to the preset value, the turning nodes are identified as coincident turning nodes, and based on the node proportion of coincident turning nodes in all turning nodes, determine the proximity between spatial vector lines.

[0094] The distance between turning nodes refers to the distance between adjacent turning nodes in a spatial vector line. In the structural similarity analysis of the support steel frame structure expression model, by determining the turning nodes of the spatial vector line and calculating the distance between them, the similarity degree between different models can be quantified. The distance between turning nodes is an important indicator for evaluating model similarity, which reflects the detailed differences and changes in the structure of the model. By comparing the distances between turning nodes of different models, the similarity and differences between them can be judged more accurately.

[0095] The sub - proximity degree is a quantitative evaluation of the distance between each turning node when comparing the proximity degree between spatial vector lines. It reflects the similarity degree of two spatial vector lines at specific turning nodes. By calculating the sub - proximity degree, the similarities and differences between different spatial vector lines can be analyzed more meticulously. In the structural similarity analysis of the support steel frame structure expression model, the sub - proximity degree is an important part of evaluating the model similarity, which helps to more accurately judge the coincidence situation between different models.

[0096] Among them, the expression for calculating the proximity degree is: .

[0097] Among them, is the proximity degree, is the coincidence judgment function between the th turning nodes. If the sub - proximity degree between the turning nodes is greater than or equal to the preset value, then outputs 1, otherwise outputs 0. is the node proportion influence adjustment constant, is the node proportion influence adjustment coefficient.

[0098] In some embodiments disclosed by the present invention, the method for analyzing the signature feature factors for each support steel frame structure expression model set includes:

[0099] Step S406: Determine the number of structure - high - density spaces of the support steel frame expression models in the support steel frame structure expression model set, which is recognized as the first signature feature factor.

[0100] Step S407: Determine the turning angles of the spatial vector lines of the structure - high - density spaces for several times, and sort the turning angles based on the turning point order to obtain a turning angle sequence, which is recognized as the second signature feature factor.

[0101] In some embodiments disclosed by the present invention, the method for analyzing the signature feature factors for each support steel frame structure expression model set further includes:

[0102] Step S408: Determine the model space occupied volume of the support steel frame expression models in the support steel frame structure expression model set, which is recognized as the third signature feature factor.

[0103] The model space occupied volume refers to the volume size occupied by the support steel frame structure expression model in three-dimensional space. This volume can be obtained by calculating the geometric dimensions and shape of the model. The model space occupied volume is an important indicator for evaluating the size and complexity of the model, and it reflects the occupancy of the model in physical space. In the structural similarity analysis of the support steel frame structure expression model, the model space occupied volume can be used as one of the signature feature factors to compare the similarities and differences between different models. At the same time, it can also provide important information about the size and shape of the model for subsequent vulnerability analysis.

[0104] Step S500, when performing vulnerability analysis on the support steel frame, based on the matching situation between different signature feature factor groups and the support steel frame, determine the set of support steel structure expression models corresponding to the support steel frame, and based on the support steel structure damage dynamic performance model corresponding to the set of support steel structure expression models, perform vulnerability expression on the support steel structure.

[0105] This step is the final application stage of the method, that is, to perform vulnerability analysis on the support steel frame. First, analyze the matching situation between the signature feature factor group of the support steel frame to be analyzed and the established signature feature factor group. Then, determine the set of support steel structure expression models corresponding to the support steel frame to be analyzed according to the degree of matching. Next, use the support steel structure damage dynamic performance model corresponding to this model set to perform vulnerability expression on the support steel frame to be analyzed, that is, predict its damage situation and performance changes under different seismic intensity parameters. Finally, put forward corresponding maintenance, repair or reinforcement suggestions according to the vulnerability analysis results to ensure the safety and stability of the support steel frame.

[0106] For the support steel frame that needs to be subjected to vulnerability analysis, we first need to re-express its structural characteristics according to the established analysis process (including steps such as establishing a structural analysis space, marking trigger points, determining a high-density trigger marker point group, constructing a structural high-density space, drawing spatial vector lines, and screening and classifying the model). Subsequently, we comprehensively compare this new expression with the existing set of support steel structure expression models. The comparison process involves multiple dimensions, such as the distribution characteristics of the high-density trigger marker point group, the morphological ratio of the structural high-density space, and the specific parameters of the spatial vector lines, and uses algorithms such as similarity calculation or pattern matching to quantify the similarity between the two. Finally, based on the preset matching degree threshold or standard, we can determine which model or models in the model set have a high matching situation with the new support steel frame, thus providing an accurate model basis for subsequent vulnerability analysis. This process not only ensures the pertinence and accuracy of the vulnerability analysis, but also realizes the effective combination of the theoretical model and the actual engineering practice.

[0107] In some embodiments disclosed by the present invention, a support steel frame vulnerability analysis system based on learning algorithm analysis is also disclosed, including:

[0108] The first module is used to obtain several design drawings of support steel frames with historical detection records, analyze each design drawing of the support steel frame, construct a support steel frame structure expression model, divide the vulnerable blocks of the support steel frame structure expression model, and configure structural parameters for each vulnerable block;

[0109] The second module is used to analyze the historical detection records corresponding to each support steel frame structure expression model, determine the damage characteristics of the support steel frame in different time periods, establish the corresponding relationship between the damage characteristics and the time periods, analyze the historical earthquake data, determine the earthquake intensity parameters in different time periods, and establish the temporal correlation relationship between the damage characteristics and the earthquake intensity parameters based on the equivalent time period correspondence;

[0110] The third module is used to analyze the damage characteristics, and based on the analysis results, adjust the damage expression of the corresponding support steel frame structure expression model, obtain the first dynamic performance of the support steel frame structure expression model based on the temporal change of the damage characteristics, and establish the temporal correlation relationship between the change of the earthquake intensity parameters and the first dynamic performance based on the temporal correlation relationship between the damage characteristics and the earthquake intensity parameters, so as to obtain the support steel structure damage dynamic performance model;

[0111] The fourth module is used to conduct a structural similarity analysis on all support steel frame structure expression models, classify the support steel frame structure expression models based on the analysis results, obtain several support steel frame structure expression model sets, conduct a marker feature factor analysis on each support steel frame structure expression model set, obtain several marker feature factor groups, and establish the correlation relationship between the marker feature factor groups and the support steel structure expression model sets;

[0112] The fifth module is used to determine the support steel structure expression model set corresponding to the support steel frame based on the matching situation between different marker feature factor groups and the support steel frame when conducting the vulnerability analysis of the support steel frame, and conduct the vulnerability expression of the support steel structure based on the support steel structure damage dynamic performance model corresponding to the support steel structure expression model set.

[0113] The present invention discloses a vulnerability analysis method and system for support steel frames based on learning algorithm analysis, which relates to the technical field of support steel frame analysis. By obtaining and analyzing the design drawings of support steel frames with historical detection records, a structural expression model is constructed, and vulnerable blocks are divided and structural parameters are configured; the correlation between the damage characteristics of the support steel frames and time and earthquake intensity is analyzed, and a damage dynamic performance model is established; similarity analysis is carried out on all structural expression models of the support steel frames, a model set is classified, and a set of characteristic feature factors is extracted; in actual vulnerability analysis, according to the matching situation between the support steel frame and the set of characteristic feature factors, the corresponding support steel structure expression model set is determined, and the damage dynamic performance model in this model set is used for vulnerability assessment. The above technical solution of the present invention improves the accuracy and efficiency of vulnerability assessment, and provides a scientific and effective technical means for the design, assessment and maintenance of support steel frames.

[0114] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for analyzing the vulnerability of supporting steel frames based on learning algorithm analysis, characterized in that: include: Obtain several supporting steel frame design drawings with historical inspection records, analyze each supporting steel frame design drawing, construct a supporting steel frame structure expression model, divide the supporting steel frame structure expression model into vulnerable blocks, and configure structural parameters for each vulnerable block; Analyze the historical detection records corresponding to each supporting steel frame structure expression model, determine the damage characteristics of the supporting steel frame in different time segments, and establish the corresponding relationship between the damage characteristics and the time segments; analyze the historical earthquake data, determine the earthquake severity parameters in different time segments, and establish the temporal correlation between the damage characteristics and the earthquake severity parameters based on the equivalent correspondence of the time segments; Analyze the damage characteristics, and based on the analysis results, adjust the damage expression of the corresponding supporting steel frame structure expression model, and based on the change of the damage characteristics over time, obtain the first dynamic expression of the supporting steel frame structure expression model, and based on the temporal correlation between the damage characteristics and the earthquake severity parameter, establish the temporal correlation between the earthquake severity parameter change and the first dynamic expression, and obtain the supporting steel structure damage dynamic expression model; Structural similarity analysis is performed on all supporting steel frame structure expression models, and based on the analysis results, the supporting steel frame structure expression models are classified to obtain several supporting steel frame structure expression model sets, and a signature characteristic factor analysis is performed on each supporting steel frame structure expression model set to obtain several signature characteristic factor groups, and an association relationship between the signature characteristic factor group and the supporting steel structure expression model set is established, wherein the signature characteristic factor is a structural feature that plays a signature role in the supporting steel frame structure expression model; When conducting vulnerability analysis on the supporting steel frame, the supporting steel structure expression model set corresponding to the supporting steel frame is determined based on the matching of different sign characteristic factor groups with the supporting steel frame, and the vulnerability of the supporting steel structure is expressed based on the supporting steel structure damage dynamic expression model corresponding to the supporting steel structure expression model set.

2. The method for analyzing the vulnerability of a supporting steel frame based on learning algorithm analysis according to claim 1 is characterized in that: The method of dividing the vulnerable blocks of the supporting steel frame structure expression model includes: Finite element analysis is performed on the supporting steel frame structure expression model, including meshing, dividing it into several model units, establishing the association relationship between adjacent model units, and configuring material properties for the model units, including elastic modulus, Poisson's ratio and density; Apply boundary conditions to the support structure expression model after meshing, and apply conventional loads to the support structure expression model after meshing; Based on historical earthquake data, the input parameters of the finite element analysis are determined, and the finite element results of the supporting steel frame structure expression model are obtained. Based on the finite element analysis results, the blocks on the supporting steel frame structure expression model where the stress is greater than or equal to the preset range are determined and recorded as damage concern blocks. Based on the type of structure of the damage concern blocks, the scope of the damage concern blocks is determined to obtain the vulnerable blocks.

3. The method for vulnerability analysis of supporting steel frame based on learning algorithm analysis according to claim 1 is characterized in that: Methods for determining damage characteristics of supporting steel frames at different time periods include: Analyze historical inspection records to determine the damage characteristics of different damage characteristic types, and establish damage characteristic comparison rules based on the structural impact of each damage characteristic type on the supporting steel frame. Based on the damage characteristic comparison rules, determine the negative structural impact parameters of the damage characteristics of different damage characteristic types in different vulnerable blocks; The negative structural impact parameters of different damage feature types corresponding to each vulnerable block are counted and configured in the vulnerable expression component of each vulnerable block.

4. The method for analyzing the vulnerability of a supporting steel frame based on learning algorithm analysis according to claim 3 is characterized in that: The methods for adjusting the damage expression of the supporting steel frame structure expression model include: The supporting steel frame structure expression model is analyzed, and a parallel central virtual line is set for the connecting rod of the supporting steel frame structure expression model, and a number of perpendicular parametric expression circles are made relative to the central virtual line, and the center of the parametric expression circle coincides with the central virtual line, and a parametric expression circle is set for the intersection of the central virtual line; Based on the negative structural impact parameters of the corresponding vulnerable block, the diameter of the parametric expression circle or parametric expression body is determined: ; in, is the diameter of a circle or a body expressed parametrically, is the diameter conversion adjustment factor, is the negative structural influence parameter, is the adjustment coefficient of the negative structural impact parameter, Adjustment constant for negative structural influence parameters.

5. The method for analyzing the vulnerability of a supporting steel frame based on learning algorithm analysis according to claim 1 is characterized in that: The methods for structural similarity analysis of all supporting steel frame structure expression models include: A structural analysis space is established for the supporting steel frame structure expression model, and a number of spatial nodes are evenly set in the structural analysis space, and the spatial nodes mapped on the supporting steel frame structure expression model are triggered and marked as trigger mark points; Randomly select a number of trigger mark points and randomly combine them to obtain a number of trigger mark point groups, analyze the distances between the trigger mark points in each trigger mark point group, calculate the average value of the distances between the points, and record it as the distance between the mark points; A trigger mark point is randomly selected and recorded as an analysis trigger mark point. The distance between the trigger mark points next to the analysis trigger mark point is determined, and the average value of the distance between the points is calculated, which is recorded as the average distance between the points. The ratio of the average distance between the points to the distance between the marking points is calculated, which is recorded as the reference ratio of the distance between the points. If the reference ratio of the distance between the points is greater than or equal to the preset value, the corresponding analysis trigger mark point is marked and recorded as a high-density trigger mark point. The interconnected high-density trigger mark points are combined to obtain a high-density trigger mark point group, and the space corresponding to the high-density trigger mark point group is recorded as a structural high-density space; Performing spatial ratio analysis on the high-density structural spaces, and constructing spatial vector lines between the high-density structural spaces based on the order of the spatial ratios. The method for constructing the spatial vector lines includes determining the center points of the high-density structural spaces, and connecting the spatial vector lines between the center points. The different space vector lines are scaled and rotated for alignment adjustment, and the proximity between the space vector lines is compared. Based on the proximity, the supporting steel frame structure expression model is screened and classified.

6. The method for analyzing the vulnerability of a supporting steel frame based on learning algorithm analysis according to claim 5 is characterized in that: Methods for comparing the proximity between space vector lines include: Determine the turning nodes of different space vector lines, quantitatively analyze the turning node distances between the turning nodes corresponding to the space vector lines, and determine the proximity between the space vector lines based on the turning node distances; The method for determining the degree of proximity includes determining the preset interval of distances between turning nodes to which the distance between each turning node belongs, and determining the sub-degree of proximity between the corresponding turning nodes based on the interval of distances between the turning nodes to which they belong, and if the sub-degree of proximity is greater than or equal to the preset value, the turning node is identified as a coincident turning node, and the degree of proximity between the space vector lines is determined based on the node ratio of the coincident turning nodes to all turning nodes; Among them, the expression for calculating the degree of proximity is: ; in, For the degree of proximity, For the The coincidence judgment function between the turning nodes, if the sub-closeness between the turning nodes is greater than or equal to the preset value, then Output 1, otherwise output 0. is the node ratio impact adjustment constant, It is the node proportion impact adjustment coefficient.

7. The method for analyzing the vulnerability of a supporting steel frame based on learning algorithm analysis according to claim 5 is characterized in that: The method of performing characteristic factor analysis on each supporting steel frame structure expression model set includes: Determine the number of high-density structural spaces in the supporting steel frame structure expression model, and identify it as the first characteristic factor; The turning angles of several spatial vector lines in the high-density structured space are determined, and based on the order of turning points, the turning angles are sorted to obtain a turning angle sequence, which is identified as the second signature characteristic factor.

8. The method for analyzing the vulnerability of a supporting steel frame based on learning algorithm analysis according to claim 7 is characterized in that: The method of performing a signature factor analysis on each supporting steel frame structure expression model set also includes: The model space occupied volume of the supporting steel frame expression model and the concentrated supporting steel frame expression model is determined and identified as the third characteristic factor.

9. The support steel frame vulnerability analysis system based on learning algorithm analysis is characterized by: The method for performing the vulnerability analysis of a supporting steel frame according to any one of claims 1 to 8 comprises: The first module is used to obtain a number of supporting steel frame design drawings with historical inspection records, analyze each supporting steel frame design drawing, construct a supporting steel frame structure expression model, divide the supporting steel frame structure expression model into vulnerable blocks, and configure structural parameters for each vulnerable block; The second module is used to analyze the historical detection records corresponding to each supporting steel frame structure expression model, determine the damage characteristics of the supporting steel frame in different time segments, and establish the corresponding relationship between the damage characteristics and the time segments, analyze the historical earthquake data, determine the earthquake severity parameters in different time segments, and establish the temporal correlation between the damage characteristics and the earthquake severity parameters based on the equivalent correspondence of the time segments; The third module is used to analyze the damage characteristics, and based on the analysis results, adjust the damage expression of the corresponding supporting steel frame structure expression model, and based on the change of the damage characteristics over time, obtain the first dynamic expression of the supporting steel frame structure expression model, and based on the temporal correlation between the damage characteristics and the earthquake severity parameter, establish the temporal correlation between the earthquake severity parameter change and the first dynamic expression, and obtain the supporting steel structure damage dynamic expression model; The fourth module is used to perform structural similarity analysis on all supporting steel frame structure expression models, and based on the analysis results, classify the supporting steel frame structure expression models to obtain several supporting steel frame structure expression model sets, and perform a signature feature factor analysis on each supporting steel frame structure expression model set to obtain several signature feature factor groups, and establish an association relationship between the signature feature factor group and the supporting steel structure expression model set; The fifth module is used to perform vulnerability analysis on the supporting steel frame. Based on the matching of different sign characteristic factor groups with the supporting steel frame, the supporting steel structure expression model set corresponding to the supporting steel frame is determined, and based on the supporting steel structure damage dynamic expression model corresponding to the supporting steel structure expression model set, the vulnerability of the supporting steel structure is expressed.

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