Continuous collapse performance detection method for large-span steel structure

Abstract

The invention belongs to the technical field of large-span steel structure index prediction, and relates to a continuous collapse performance detection method for a large-span steel structure, which comprises the following steps: S1, large-span steel structure data collection, field investigation and theoretical structure analysis; s3, large-span steel structure collapse overall numerical simulation is carried out, a continuous collapse path is determined, and key components and weak areas causing large-range damage are preliminarily determined; s4, performing local numerical simulation, and verifying continuous collapse caused by the key component and the weak area; and S5, drawing a load-displacement curve, an internal force curve and a strain curve on the progressive collapse path, and determining an evolution rule of a load transmission path. According to the method, through a refined nonlinear finite element model, mechanical characteristics are taken as a foothold, the continuous collapse-resistant failure mechanism, weak links and sensitive components of the large-span space structure are fully researched from the component level and the overall structure level, and the continuous collapse-resistant failure mechanism of the large-span space structure is revealed.

Description

Technical Field

[0001] The invention relates to the technical field of large-span steel structure index prediction, and in particular to a continuous collapse performance detection method for large-span steel structures. Background Art

[0002] In the context of sustained development of infrastructure construction, spatial structures are in a period of rapid development. Steel has been used in engineering for more than a hundred years. Due to its excellent performance, it has been valued and promoted in the fields of industrial and civil buildings and large-span spatial structures. For example, large-span spatial steel structures such as modern large-scale stadiums, exhibition halls and other public buildings have relatively low redundancy and weak ability to resist continuous collapse. The problem of statically determinate structures such as plane or spatial trusses is particularly prominent. The collapse accidents of such structures caused by factors such as snow load, foundation settlement, strong earthquake and fire at home and abroad have attracted widespread attention. However, existing research has mainly been conducted on structural systems such as frames and load-bearing walls. In recent years, the problem of partial or overall collapse of large public buildings has become increasingly prominent.

[0003] In addition, although large-span steel structures have many spatial structural rod systems and a high degree of static indeterminacy, accidental loads may cause local failure of the structure, which in turn causes continuous damage to components connected to the failed components, and ultimately leads to a larger range of collapse damage than the initial local damage. Therefore, it is necessary to consider taking preventive measures for such structures to prevent continuous collapse of the structure and seek technical data to curb or prevent continuous collapse of the structure. The existing technology does not consider the mechanical properties of the overall large-span steel structure, and the constructed model is relatively limited. It has not fully studied the continuous collapse type failure mechanism, weak links, sensitive components, etc. of large-span spatial structures from the component level and the overall structure level. Summary of the invention

[0004] In order to solve the above existing technical problems, the present invention provides a method for detecting the progressive collapse performance of a large-span steel structure.

[0005] The technical solution of the present invention is achieved through the following scheme: A method for detecting the continuous collapse performance of a large-span steel structure comprises the following steps: S1, data collection and theoretical structural analysis of large-span steel structures; S2, establishing a function model according to the parameter range of the index data and establishing a finite element analysis model; S3, conduct overall numerical simulation of the collapse of large-span steel structures, determine the continuous collapse path, and preliminarily identify the key components and weak areas that cause large-scale damage; S4, local numerical simulation, verifies the progressive collapse caused by key components and weak areas; S5. Draw the load-displacement, internal force, and strain curves on the progressive collapse path to determine the evolution law of the load transfer path. S6. Conduct a systematic parametric analysis of the influencing factors of progressive collapse to determine the design factors of materials and structures, structures, and load effects. S7. Complete the systematic multi-parameter analysis to reveal the failure mechanism. S8. Divide the warning intervals according to the stability state of the long-span steel structure, retain the weak and sensitive points of the long-span steel structure, and strengthen the redundancy of the structures at these points.

[0006] Preferably, the specific steps of step S2 include: S201. According to the design requirement data, use the Python secondary development script of ABAQUS to achieve large-scale and automated modeling of long-span steel structures and extraction of post-processing results. S202. Use the Explicit dynamic analysis module of the ABAQUS finite element analysis software, consider the modal geometric curve of linear buckling analysis, material nonlinearity, and state nonlinearity reflected in the structural collapse process, and carry out numerical calculations on the collapse failure process of long-span steel structures under dynamic loads.

[0007] Preferably, the overall numerical simulation steps of step S3 include: S301. Apply the normal service load and accidental load to the steel structure data model. S302. Gradually apply the accidental load until the initial collapse of the structure occurs. S303. Continuously apply the accidental load until the remaining structural members are unable to bear the internal force redistribution or impact load, causing a progressive collapse. S304. According to the parts of the long-span steel structure data model that are vulnerable to accidental loads or the failure of which will cause large-scale damage, initially judge and select the key components and weak areas.

[0008] Preferably, the local numerical simulation steps of step S4 include: S401. Apply loads to the key components and weak areas for verification to determine whether the structure will undergo progressive collapse under local damage at this location. S402. Clarify the influence of the weak areas and key components on the structural bearing performance and anti-collapse performance.

[0009] Preferably, in step S3, according to the post-processing function of ABAQUS, carry out numerical calculations on the collapse failure process of long-span steel structures under accidental dynamic loads and extract the key data during the calculation process, including displacement, stress, and strain.

[0010] Preferably, in step S5, a Python script is used to track and extract the result data, and the load-displacement, internal force, and strain curves are plotted using the matplotlib library of Python to observe the evolution laws of load-displacement and strain, the alternative load transfer paths, and determine the evolution law of the load transfer path.

[0011] Preferably, the specific analysis steps of step S6 include: S601, perform a static elastoplastic analysis on the long-span steel structure according to the push-down method to obtain the plastic hinge distribution area of the structure, and determine the material and construction factors among the continuous collapse influencing factors; S602, determine the critical state of collapse failure of the long-span steel structure according to the incremental dynamic method, and determine the structural design factors among the continuous collapse influencing factors; S603, estimate the plastic distribution area and the collapse limit critical displacement of the long-span steel structure under strong earthquake action, and determine the load effect of the continuous collapse influencing factors.

[0012] Preferably, in step S7, by tracking the whole process of continuous collapse of the yield ratio of structural members, the maximum joint displacement, the total strain energy of the structure, and the structural plastic displacement index of the long-span steel structure, the triggering mechanism, failure mode, and its influencing factors and laws of continuous collapse of the long-span steel structure under load are judged comprehensively according to each response index.

[0013] Preferably, the specific steps of step S8 include: S801, analyze the influence of the stress condition, material properties, connection method, and environmental factors of the structure on the structural stability, and record in detail the weak and sensitive points that can cause the decline of the stability of the long-span steel structure; S802, divide the stability states of the weak and sensitive points of the long-span steel structure into different warning intervals, safety intervals, warning intervals, and danger intervals; S803, set alarms and strengthen auxiliary hardware at the weak points of the long-span steel structure. Preferably, the alarm will give an alarm when the structural stability drops to the warning interval according to the threshold set in the warning interval; the strengthened auxiliary hardware includes additional support structures, reinforcement plates, and prestressed cables.

[0014] In summary, the present invention has the following beneficial effects: 1. The present invention pioneeringly proposes a method for detecting the continuous collapse performance of long-span steel structures.

[0015] 2. By carrying out the overall numerical simulation of the collapse of long-span steel structures, determine the continuous collapse path, and preliminarily determine the key components and weak areas that cause large-scale damage; through local numerical simulation, verify the continuous collapse caused by key components and weak areas; draw load-displacement, internal force and strain curves on the continuous collapse path to determine the evolution law of the load transfer path; conduct a systematic parametric analysis of the influencing factors of continuous collapse to determine the design factors of materials and structures, structures, and load effects.

[0016] 3. By carrying out the overall numerical simulation of the collapse of long-span steel structures, determine the continuous collapse path, and preliminarily determine the key components and weak areas that cause large-scale damage; through local numerical simulation, verify the continuous collapse caused by key components and weak areas; draw load-displacement, internal force and strain curves on the continuous collapse path to determine the evolution law of the load transfer path; conduct a systematic parametric analysis of the influencing factors of continuous collapse to determine the design factors of materials and structures, structures, and load effects.

[0017] 4. Deduce the plastic distribution area and the collapse limit critical displacement of long-span steel structures under strong earthquake action, and determine the load effect of the influencing factors of continuous collapse; analyze the plastic distribution area of the structure under strong earthquake action, compare it with the results of static pushover analysis, and evaluate the influence of dynamic effects on the structure collapse mechanism; determine the collapse limit critical displacement of the structure under strong earthquake action according to the results of dynamic time history analysis; analyze the relationship between the collapse limit critical displacement and factors such as the intensity of seismic waves, structural characteristics and material properties, provide guidance for the seismic design and reinforcement of the structure, and determine the load effect of the influencing factors of continuous collapse.

[0018] 5. The present invention realizes large-scale and automated modeling and post-processing result extraction through the secondary development of ABAQUS and Python scripts. Through the overall and local numerical simulation of the model, apply the normal service load and accidental load, initially judge the key components and weak areas, and apply the load for verification, improve the accuracy and authenticity of the simulation, accurately evaluate the anti-continuous collapse performance of the structure, improve the efficiency of continuous collapse performance detection, reduce human errors, accurately observe the deformation and damage conditions of the structure at different stages, and identify the parts that are prone to accidental actions and will cause large-scale damage once they fail; and analyze the influence of various factors on the structural stability, divide the stable state of long-span steel structures into a safe interval, a warning interval and a dangerous interval, realizing the precise monitoring of the anti-continuous collapse performance of long-span steel structures, which helps to formulate and implement maintenance plans in a timely manner.

[0019] 6. By means of systematic parameter analysis and multi-parameter research, a finite element conversion program is compiled to quickly establish a refined non-linear finite element model for progressive collapse analysis of structures. By dividing the warning intervals, potential structural safety issues can be promptly detected and warned, providing a valuable time window for taking preventive measures, avoiding or reducing the occurrence of progressive collapse accidents. Based on mechanical characteristics, the anti-progressive collapse failure mechanism, weak links, and sensitive components of long-span spatial structures are fully studied respectively at the component level and the overall structural level, revealing the anti-progressive collapse failure mechanism of long-span steel structures and achieving an overall improvement in the quality and safety of long-span structures.

[0020] 7. Through automatic extraction, the progressive collapse process of long-span steel structures can be analyzed more quickly, providing convenience for subsequent data analysis and processing. By using the Explicit dynamic analysis module of the ABAQUS finite element analysis software, the collapse failure process of long-span steel structures under dynamic loads can be simulated more accurately. The accurate analysis results and revealed failure mechanisms provide theoretical support for formulating effective preventive control methods, helping engineers take targeted measures during the design and optimization of structures to improve the overall safety and stability of the structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described below with reference to the drawings and embodiments.

[0023] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the present invention is not limited by the specific embodiments disclosed in the following specification. The present invention will be further described in detail below with reference to the drawings.

[0024] A method for detecting the progressive collapse performance of long-span steel structures, as Figure 1 shown, includes the following steps: S1, Collection of data on long-span steel structures and theoretical structural analysis; Collect relevant data such as design drawings, construction records, and usage history of the long-span steel structure of the project, conduct on-site investigations, observe the actual condition of the structure, and record possible weak links and sensitive components.

[0025] S2, Establish a function model based on the range of index data parameters and establish a finite element analysis model; S3. Conduct a holistic numerical simulation of the collapse of long-span steel structures to determine the progressive collapse path and preliminarily identify the key components and weak areas that cause extensive damage. S4. Conduct local numerical simulations to verify the progressive collapse caused by key components and weak areas. S5. Plot load-displacement, internal force, and strain curves along the progressive collapse path to determine the evolution law of the load transfer path. S6. Conduct a systematic parametric analysis of the influencing factors of progressive collapse to determine the design factors of material and structure, structure, and load effects. S7. Complete the systematic multi-parametric analysis to reveal the failure mechanism. S8. Divide the early warning intervals according to the stable state of long-span steel structures, retain the weak and sensitive points of long-span steel structures, and enhance the redundancy of the structures at these points.

[0026] The specific steps of step S2 include: S201. Use the Python secondary development script in ABAQUS to achieve mass and automated modeling of long-span steel structures and extraction of post-processing results. According to the key information such as the geometric dimensions, material properties, boundary conditions, and load data of the long-span steel structures collected and sorted, determine the parameter range of the model, such as node coordinates, component section dimensions, material elastic modulus, and yield strength. Then write a Python script, and use the Python API provided by ABAQUS to write a script to automatically generate a finite element model. The script includes steps such as creating geometric bodies, assigning material properties, applying boundary conditions and loads, dividing meshes, and functions for extracting post-processing results, such as stress, strain, displacement, etc.

[0027] Run the Python script in the ABAQUS environment to generate a finite element model, check whether the generated model meets the expectations, and if necessary, debug and modify the script. Use the script to extract and analyze the post-processing results, generate stress nephograms, strain curves, displacement-time curves, etc., and save the results to files for subsequent analysis and comparison.

[0028] S202. Use the Explicit dynamic analysis module of the ABAQUS finite element analysis software to conduct numerical calculations on the collapse failure process of long-span steel structures under dynamic loads, considering the geometric curve of the linear buckling analysis mode, material nonlinearity, and state nonlinearity reflected in the structure collapse process. Open the Explicit dynamic analysis module in ABAQUS. This module is particularly suitable for handling high-rate events and transient dynamic problems, such as collisions, explosions, impacts, etc. It can capture the response calculations of materials under these extreme conditions. Import the finite element model generated by the Python script, and determine the buckling mode of the structure based on the results of the linear buckling analysis. Add the material's plastic deformation, yield and other curves to the model, as well as the contact and friction curves during the structural collapse process.

[0029] The overall numerical simulation steps of step S3 include: S301, load normal service load and accidental load to the steel structure data model; According to the structural design requirements and usage specifications, the normal service load is calculated and applied to the steel structure data model, including various static and dynamic loads that the structure bears under normal operation, including dead loads (such as structural deadweight), live loads (such as personnel, equipment, etc.) and wind loads, snow loads, etc.

[0030] Next, consider accidental loads, which include dynamic loads generated by sudden events such as earthquakes, explosions, and impacts.

[0031] S302, the accidental load is loaded step by step, and the structure undergoes initial collapse; During the accidental load loading process, the response of the structure, including deformation, stress distribution, displacement, etc., is closely observed. The monitoring function of the finite element analysis software is used to record the displacement, stress and other response data of key nodes.

[0032] When the load continues to be applied, the structure will gradually reach its bearing capacity limit and initial collapse will occur. At this time, the initial collapse point is determined. The initial collapse point usually manifests itself as a sudden destruction or failure of a part of the structure. By observing and analyzing the data, the location and cause of the initial collapse point can be determined.

[0033] S303, continue to load accidental loads until the remaining structural members cannot bear the internal force redistribution or impact loads, causing chain collapse; After the initial collapse occurs, accidental loads continue to be applied to observe the response of the remaining structure and to analyze the bearing capacity of the remaining structural components to see whether they can withstand internal force redistribution or impact loads.

[0034] If the remaining components cannot effectively bear these loads, the structure will experience a chain collapse. The chain collapse process should be observed. Chain collapse is usually the continuous destruction and failure of multiple parts of the structure. The animation function of the finite element analysis software can be used to intuitively observe the process and mode of the chain collapse and record the displacement, stress and other response data of the key nodes during the chain collapse.

[0035] The step-by-step loading of accidental loads simulates the response process of the structure under gradually increasing loads, facilitating the observation of the deformation and failure conditions of the structure at different stages, and thus more accurately evaluating the anti-collapse performance of the structure; through local numerical simulation, key components and weak areas are selected for loading tests to accurately identify the parts of the structure that are susceptible to accidental actions and will cause large-scale damage once they fail, providing an important basis for structural design and optimization.

[0036] S304, according to the parts in the large-span steel structure data model that are susceptible to accidental loads or the criterion that the failure of parts will cause large-scale damage, initially judge and select key components and weak areas; Based on the data model of the large-span steel structure, after simulating with the overall data model, identify the parts that may be subjected to accidental actions, or the parts that may cause large-scale damage to the entire structure once they fail, such as edges, corners, connection points, main beams, and columns.

[0037] After determining the key components and weak areas, the next step is to apply simulated loads to these parts to evaluate their performance under local damage.

[0038] The local numerical simulation steps of step S4 include: S401, apply loads to the key components and weak areas for verification to determine whether the structure will undergo progressive collapse under local damage at this location; Through numerical simulation, observe the response of the structure after local damage. If the structure can remain stable after damage and does not undergo further progressive collapse, then its performance in this regard can be considered qualified; conversely, if the structure undergoes progressive collapse, it is necessary to further analyze the reasons and take corresponding strengthening measures.

[0039] S402, clarify the influence of weak areas and key components on the load-bearing performance and anti-collapse performance of the structure; Analyze the stress conditions of key components and weak areas to evaluate whether the load-bearing capacity of the structure under these conditions meets the design requirements; by analyzing the response of the structure under simulated loads, evaluate whether its anti-collapse performance is strong enough to resist potential disaster risks.

[0040] In step S3, according to the post-processing function of ABAQUS, carry out numerical calculations on the collapse failure process of the large-span steel structure under accidental dynamic loads and extract key data during the calculation process, displacement, stress, and strain.

[0041] In ABAQUS, the explicit dynamic analysis module is adopted to capture the dynamic response of the structure. Multiple analysis steps can be set to simulate the load application and structural response in different stages. During the calculation process, the software will automatically solve the dynamic response of the structure and generate the corresponding result files for data analysis and processing.

[0042] Extract key data from the ABAQUS result files, including the displacements, velocities, accelerations of nodes, and the internal forces, stresses, and strains of elements, etc. Use the post-processing tools of ABAQUS to visually display these results for a more intuitive understanding of the dynamic response of the structure. Multiple numerical simulations can quickly give the response results under different conditions, reducing the number of tests on the actual structure and lowering the research cost and time cost.

[0043] In step S5, use a Python script to track and extract the result data, and adopt the matplotlib library of Python to plot the load-displacement, internal force, and strain curves to observe the evolution laws of load-displacement and strain, and the alternative load transfer paths to determine the evolution law of the load transfer path.

[0044] The Python script tracks each step, frame (time step), and field variable in the data model file, extracts the required load, displacement, internal force, and strain data, imports the matplotlib library, and uses the plotting functions of matplotlib (such as plot, scatter, etc.) to plot the load-displacement, internal force, and strain curves (set the titles, axis labels, and legends of the curves to clearly display the data).

[0045] Observe the load-displacement curve to analyze the displacement response and stiffness change of the structure under the load; observe the strain curve to analyze the strain distribution and strain concentration phenomenon of the structure under the load; identify the key stages in the load-displacement and strain curves, such as the initial loading stage, yield stage, failure stage, etc., and analyze the corresponding structural states and possible failure modes in these stages.

[0046] The specific analysis steps of step S6 include: S601, conduct a static pushover analysis on the long-span steel structure according to the push-down method to obtain the plastic hinge distribution area of the structure and determine the material and construction factors among the progressive collapse influencing factors; By gradually increasing the horizontal or vertical load, the elastoplastic behavior of the structure under static action is simulated; the deformation and internal force changes of the structure during the loading process are monitored, especially the formation and development of plastic hinges, and the regions where plastic hinges appear in the structure are determined, so as to obtain the distribution region of plastic hinges in the structure. And according to the continuous monitoring and tracking of the program, the subsequent analysis is carried out on the reasons for the formation of plastic hinges, such as insufficient material strength, failure of joint connections or geometric nonlinear effects, etc. If the material strength is insufficient, subsequent material replacement is carried out (using low-grade steel, resulting in the premature formation of plastic hinges in some components under static load, excessive plastic deformation, and ultimately the overall instability of the structure). If the joint connection fails, the connection method is changed (at the welded or bolted joints of the nodes, due to the stress problem of the nodes, slippage or fracture occurs at the connection part under load, resulting in a decrease in the overall stiffness of the structure, and plastic hinges quickly form and expand in this area, ultimately leading to the collapse of the structure), so as to determine the material and construction factors in the influencing factors of progressive collapse.

[0047] S602. Determine the critical state of collapse failure of long-span steel structures according to the incremental dynamic method, and determine the structural design factors in the influencing factors of progressive collapse; On the basis of the static elastoplastic analysis, a series of gradually increasing dynamic load conditions are further set. During the process, the response indexes such as the displacement, velocity, acceleration and internal force of the structure are continuously monitored to determine the critical state of collapse failure of the structure, such as the maximum displacement, maximum acceleration of the structural design or the failure of specific plastic hinges, etc. (in the gradually increasing dynamic load conditions, the displacement and acceleration responses of the structure far exceed the design expectations, and plastic hinges form simultaneously at multiple key parts, and finally the structure cannot bear and collapses), so as to determine the structural design factors in the influencing factors of progressive collapse.

[0048] S603. Deduce the plastic distribution region and the collapse limit critical displacement of long-span steel structures under strong earthquake action, and determine the load effects in the influencing factors of progressive collapse; Analyze the plastic distribution region of the structure under strong earthquake action, compare it with the results of static elastoplastic analysis, and evaluate the influence of dynamic effects on the collapse mechanism of the structure; according to the results of dynamic time history analysis, determine the collapse limit critical displacement of the structure under strong earthquake action; analyze the relationship between the collapse limit critical displacement and factors such as the intensity of seismic waves, structural characteristics and material properties, provide guidance for the seismic design and reinforcement of the structure, and determine the load effects in the influencing factors of progressive collapse. Under strong earthquake action, the plastic distribution region is too concentrated, resulting in excessive plastic deformation in these regions and unable to effectively disperse seismic energy, ultimately leading to the overall instability of the structure.

[0049] Under extreme load conditions such as earthquakes and explosions, long-span steel structures may face serious risks of damage and collapse. By using the push-down method and incremental dynamic analysis to evaluate the anti-collapse ability of structures under these conditions, and taking corresponding strengthening measures to improve their safety, it is possible to timely identify the weak links and potential risks in the structural design; this helps engineers take corresponding optimization measures during the design stage, such as strengthening the construction of key parts and adjusting the structural layout, thereby improving the overall stability and anti-collapse ability of the structure.

[0050] Based on the extracted data, evaluate the anti-collapse performance of the structure under accidental dynamic loads, analyze the overall stability of the structure, including whether obvious plastic deformation, buckling or fracture occurs, evaluate the remaining load-bearing capacity of the structure after local damage, and whether progressive collapse will occur.

[0051] Analyze the response of the structure under different load conditions, identify potential weak areas and key components, evaluate the possibility of failure of these areas and components under extreme conditions, and their impact on the safety of the overall structure.

[0052] Compare the numerical simulation results with the existing experimental results or theoretical predictions to verify the accuracy and reliability of the simulation. If significant differences are found, the model, load application, and analysis step settings should be rechecked to ensure the correctness and accuracy of the simulation process.

[0053] In step S7, by tracking the entire process of progressive collapse of long-span steel structures through indicators such as the yield ratio of structural members, the maximum joint displacement, the total strain energy of the structure, and the plastic displacement index of the structure, comprehensively judge the triggering mechanism, failure mode, and its influencing factors and laws of progressive collapse of long-span steel structures under load.

[0054] According to the observation path curve and the systematic parameters of the influencing factors of progressive collapse, analyze the change of the yield ratio of structural members to judge the degree of plastic deformation of the structure under load, so as to determine the triggering mechanism of progressive collapse (when the yield ratio reaches or exceeds a certain critical value, the structure may enter the plastic deformation stage, thus triggering progressive collapse).

[0055] Observe the changes in the maximum joint displacement and the plastic displacement of the structure to judge the failure mode of the structure (when the joint displacement or plastic displacement reaches or exceeds a certain critical value, the structure may undergo local or overall failure). At the same time, combined with the change of the total strain energy of the structure, further evaluate the energy absorption and dissipation capacity of the structure, so as to more accurately judge the failure mode.

[0056] At the same time, it is also possible to provide guidance for the anti-collapse design and strengthening of the structure according to the influence degree of different factors on the collapse performance of the structure.

[0057] Based on the analysis results of the recorded data, propose targeted strengthening measures or improvement suggestions to improve the anti-collapse performance and overall safety of the structure. Detail the weak and sensitive points that can lead to the decline of the stability of the long-span steel structure, and focus on these areas in subsequent monitoring to improve the pertinence and efficiency of monitoring.

[0058] The specific steps of step S8 include: S801, analyze the influence of the force condition, material properties, connection method and environmental factors of the structure on the structural stability, and detail the weak and sensitive points that can lead to the decline of the stability of the long-span steel structure; Through structural analysis, find out the parts of the structure with stress concentration, large deformation or poor material properties, and establish a point information database for subsequent management and maintenance.

[0059] S802, divide the stable states of the weak and sensitive points of the long-span steel structure into different warning intervals, safety intervals, warning intervals and danger intervals; Safety interval: The steel structure is in a good stable state and does not require special attention.

[0060] Warning interval: The stability of the steel structure begins to decline, but it has not reached a dangerous level and needs to be closely monitored.

[0061] Danger interval: The stability of the steel structure is seriously insufficient, and there is a risk of collapse or serious damage.

[0062] S803, set up alarms and strengthen auxiliary hardware at the weak points of the long-span steel structure.

[0063] According to the threshold values set for the warning interval, when the structural stability drops to the warning interval, the alarm will sound to remind the management personnel to take corresponding measures.

[0064] The strengthened auxiliary hardware includes additional support structures, reinforcement plates and prestressed cables. Set additional support structures such as steel columns and steel beams at the weak points to improve the load-bearing capacity of the structure; use reinforcement plates to locally reinforce the structure and enhance the overall stability of the structure; introduce active reinforcement measures such as prestressed cables to improve the stiffness and stability of the structure by applying prestress.

[0065] The above is only a preferred embodiment of the present invention, and it is not a limitation to the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification and equivalent change made to the above embodiments according to the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for detecting the progressive collapse performance of a long-span steel structure, characterized in that: It includes the following steps: S1, collecting data of long-span steel structures and conducting theoretical structural analysis; S2, establishing a function model based on the range of index data parameters and establishing a finite element analysis model; S3, conducting an overall numerical simulation of the collapse of long-span steel structures, determining the continuous collapse path, and preliminarily determining the key components and weak areas that cause large-scale damage; S4, local numerical simulation to verify the continuous collapse caused by key components and weak areas; S5, drawing load-displacement, internal force, and strain curves on the continuous collapse path to determine the evolution law of the load transfer path; S6, systematic parametric analysis of the influencing factors of continuous collapse to determine the design factors of materials and structures, structures, and load effects; S7, completing the systematic multi-parameter analysis to reveal the failure mechanism; S8, dividing the warning interval according to the stable state of long-span steel structures, retaining the weak and sensitive points of long-span steel structures, and strengthening the redundancy of the structures at these points.

2. The continuous collapse performance detection method for a long-span steel structure according to claim 1, wherein: The specific steps of step S2 include: S201, using the Python secondary development script of ABAQUS to achieve large-scale and automated modeling of long-span steel structures and extraction of post-processing results; S202, using the Explicit explicit dynamic analysis module of ABAQUS finite element analysis software, considering the linear buckling analysis modal geometric curve, material nonlinearity, and state nonlinearity reflected in the structural collapse process, to conduct numerical calculations on the collapse failure process of long-span steel structures under dynamic loads.

3. The continuous collapse performance detection method for a long-span steel structure according to claim 1, characterized in that: The overall numerical simulation steps of step S3 include: S301, loading the normal service load and accidental load on the steel structure data model; S302, gradually loading the accidental load until the initial collapse of the structure occurs; S303, continuously loading the accidental load until the remaining structural components are unable to bear the internal force redistribution or impact load, causing a chain collapse; S304, preliminarily determining and selecting key components and weak areas according to the standard that the parts of the long-span steel structure data model that are vulnerable to accidental loads or the failure of which will cause large-scale damage.

4. A method for detecting the progressive collapse performance of a long-span steel structure according to claim 3, characterized in that: The local numerical simulation steps of step S4 include: S401, applying loads to key components and weak areas for verification to determine whether the structure will undergo continuous collapse under local damage at this place; S402, clarifying the influence of weak areas and key components on the structural bearing performance and anti-collapse performance.

5. The method for detecting the progressive collapse performance of a long-span steel structure according to claim 3, characterized in that: In step S3, according to the post-processing function of ABAQUS, conduct numerical calculations on the collapse failure process of long-span steel structures under accidental dynamic loads and extract the key data, displacement, stress, and strain during the calculation process.

6. The continuous collapse performance detection method for a long-span steel structure according to claim 1, characterized in that: In step S5, use the Python script to track and extract the result data, use the matplotlib library of Python to draw load-displacement, internal force, and strain curves, observe the evolution law of load-displacement and strain, and the alternate load transfer path, and determine the evolution law of the load transfer path.

7. The method for detecting the progressive collapse performance of a long-span steel structure according to claim 1, characterized in that: The specific analysis steps of step S6 include: S601, conduct a static elastoplastic analysis of long-span steel structures according to the push-down method to obtain the plastic hinge distribution area of the structure and determine the material and structure factors among the influencing factors of continuous collapse; S602. Determine the critical state of the collapse failure of the long-span steel structure according to the incremental dynamic method, and determine the structural design factors among the factors affecting progressive collapse. S603. Deduce the plastic distribution area and the collapse limit critical displacement of the long-span steel structure under the action of strong earthquakes, and determine the load effects among the factors affecting progressive collapse.

8. The continuity collapse performance detection method for a long-span steel structure according to claim 1, characterized in that: In the step S7, by tracking the whole process of progressive collapse of the yield ratio of the structural members, the maximum joint displacement, the total structural strain energy and the structural plastic displacement index of the long-span steel structure, comprehensively judge the triggering mechanism, failure mode, its influencing factors and laws of the progressive collapse of the long-span steel structure under the action of loads.

9. The continuity collapse performance detection method for long-span steel structures according to claim 1, characterized in that: The specific steps of step S8 include: S801. Analyze the influence of the structural force condition, material properties, connection mode and environmental factors on the structural stability, and record in detail the weak and sensitive points that can lead to the decrease of the stability of the long-span steel structure. S802. Divide the stability states of the weak and sensitive points of the long-span steel structure into different warning intervals, safety intervals, warning intervals and danger intervals. S803. Set alarms and strengthen auxiliary hardware at the weak points of the long-span steel structure.

10. The method for detecting the progressive collapse performance of a long-span steel structure according to claim 9, characterized in that: The alarm will give an alarm according to the threshold set in the warning interval when the structural stability drops to the warning interval; the strengthening auxiliary hardware includes additional support structures, reinforcement plates and prestressed cables.

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