Bridge engineering risk assessment platform based on load-bearing analysis

Abstract

The present invention discloses a bridge engineering risk assessment platform based on load-bearing analysis, which relates to the field of data management technology. The platform includes: a bridge engineering task perception layer, which is used to obtain target bridge engineering tasks; a BIM interaction layer, which is used to obtain target bridge models; a load scenario construction layer, which is used to build load scenarios and generate bridge load scenario particle groups that meet load scenario variation rules; a load-bearing simulation layer, which is used to obtain a multi-scenario bridge load-bearing test vector set; a bridge engineering risk assessment layer, which is used to obtain a bridge engineering risk assessment report; and a bridge engineering optimization layer, which is used to obtain an optimized bridge construction plan. The platform solves the technical problem that it is difficult to fully consider multiple load scenarios in the existing bridge engineering risk assessment, which leads to inaccurate and unreliable risk assessment results of bridge load-bearing performance, and achieves the technical effect of improving the accuracy and reliability of bridge engineering risk assessment.

Description

Technical Field

[0001] The present application relates to the technical field related to data management, and specifically to a bridge engineering risk assessment platform based on load-bearing analysis. Background Art

[0002] With the acceleration of urbanization and the continuous improvement of transportation networks, bridge engineering, as an important link between cities, is particularly critical in terms of safety and reliability. During the design, construction and use of bridge engineering, it often faces various complex environmental and load factors, such as geological conditions, climatic conditions, traffic flow, etc. These factors will have an important impact on the load-bearing performance of bridges. How to accurately evaluate the load-bearing performance of bridge engineering and optimize the bridge construction plan based on the evaluation results has become an urgent problem to be solved in the field of bridge engineering. However, traditional bridge risk assessment is often analyzed and judged in a qualitative way, which is difficult to accurately reflect the actual situation of bridge engineering, and it is difficult to comprehensively evaluate the load-bearing performance of bridges in multiple scenarios, which makes the safety and reliability of bridge engineering have hidden dangers.

[0003] Therefore, in the current bridge engineering risk assessment related technologies, there is a technical problem that it is difficult to fully consider various load scenarios, which leads to the inaccurate and unreliable risk assessment results of bridge bearing performance. Summary of the invention

[0004] This application solves the technical problem that it is difficult to comprehensively consider various load scenarios in existing bridge engineering risk assessments, which leads to inaccurate and unreliable risk assessment results of bridge bearing performance, by providing a bridge engineering risk assessment platform based on load-bearing capacity analysis. This achieves the technical effect of improving the accuracy and reliability of bridge engineering risk assessments.

[0005] The present application provides a bridge engineering risk assessment platform based on load-bearing capacity analysis, the platform comprising: a bridge engineering task perception layer, the bridge engineering task perception layer is used to obtain a target bridge engineering task, wherein the target bridge engineering task includes a bridge engineering construction plan of a target bridge; a BIM interaction layer, the BIM interaction layer is used to model according to the bridge engineering construction plan to obtain a target bridge model; a load scenario building layer, the load scenario building layer is used to build a load scenario for the target bridge, and generate a bridge load scenario particle group that meets the load scenario variation rule; a load-bearing simulation layer, the load-bearing simulation layer is used to perform a confidence load-bearing test on the target bridge model according to the bridge load scenario particle group to obtain a multi-scenario bridge load-bearing test vector set; a bridge engineering risk assessment layer, the bridge engineering risk assessment layer performs risk assessment on the multi-scenario bridge load-bearing test vector set based on an embedded bridge risk assessment channel and a bridge risk assessment combing function to obtain a bridge engineering risk assessment report; a bridge engineering optimization layer, the bridge engineering optimization layer is used to optimize the bridge engineering construction plan according to the bridge engineering risk assessment report to obtain an optimized bridge construction plan.

[0006] In a possible implementation, the load scenario building layer also performs the following processing: obtaining the expected load constraint information of the target bridge; building a bridge load matrix according to the multivariate bridge load index, wherein the multivariate bridge load index includes static load, live load, dynamic load, environmental load and special load; using the expected load constraint information as the load scenario constraint, randomly setting the load scenario parameters according to the bridge load matrix, and obtaining an initial load scenario particle group that meets the load scenario particle capacity; verifying and optimizing the initial load scenario particle group according to the load scenario variation rule, and obtaining the bridge load scenario particle group.

[0007] In a possible implementation, the load scenario building layer also performs the following processing: the load scenario variation rule includes a load scenario variation threshold; a pairwise comparison is performed based on the initial load scenario particle group to generate multiple load scenario variation coefficients; it is determined whether the multiple load scenario variation coefficients are greater than / equal to the load scenario variation threshold; if the multiple load scenario variation coefficients are all greater than / equal to the load scenario variation threshold, the initial load scenario particle group is added to the bridge load scenario particle group.

[0008] In a possible implementation, the load scenario building layer also performs the following processing: if any load scenario variation coefficient among the multiple load scenario variation coefficients is less than the load scenario variation threshold, an identified load scenario particle is generated; the initial load scenario particle group is mutated and optimized according to the load scenario constraints and the identified load scenario particles to generate an optimized load scenario particle group; the optimized load scenario particle group is verified and optimized according to the load scenario variation rules to obtain the bridge load scenario particle group.

[0009] In a possible implementation, the load-bearing simulation layer also performs the following processing: extracting the first bridge load scenario particle according to the bridge load scenario particle group; obtaining the number of confident load-bearing tests; based on the number of confident load-bearing tests, performing multiple load-bearing simulation tests on the target bridge model according to the first bridge load scenario particle to obtain multiple bridge stress-strain simulation data sets; performing stress-strain point feature clustering according to the multiple bridge stress-strain simulation data sets to obtain multiple bridge point stress-strain sample areas; performing centralized value calculation according to the multiple bridge point stress-strain sample areas to obtain the first scenario bridge load-bearing test vector, and adding the first scenario bridge load-bearing test vector to the multi-scenario bridge load-bearing test vector set; based on the number of confident load-bearing tests, continuing to perform confident load-bearing tests on the target bridge model according to the bridge load scenario particle group to generate the multi-scenario bridge load-bearing test vector set.

[0010] In a possible implementation, the bridge engineering risk assessment layer also performs the following processing: inputting the multi-scenario bridge load-bearing test vector set into the bridge engineering risk assessment layer to obtain multiple bridge point risk detection coefficients; performing load intensity evaluation based on the bridge load scenario particle group to obtain multiple test scenario load intensity coefficients; performing proportion calculation based on the multiple test scenario load intensity coefficients to output multiple test scenario risk value coefficients; performing weighted calculation on the multiple bridge point risk detection coefficients based on the multiple test scenario risk value coefficients to output the bridge engineering risk assessment coefficient; integrating the bridge load scenario particle group, the multi-scenario bridge load-bearing test vector set, the multiple bridge point risk detection coefficients and the bridge engineering risk assessment coefficient to generate the bridge engineering risk assessment report.

[0011] In a possible implementation, the bridge engineering risk assessment layer also performs the following processing: according to the multi-scenario bridge load-bearing test vector set, extract the nth scenario bridge load-bearing test vector, where n is a positive integer; the bridge risk assessment channel includes R bridge risk assessment branches, where R is a positive integer greater than 1; input the nth scenario bridge load-bearing test vector into the R bridge risk assessment branches to obtain R bridge point risk assessment coefficients; input the R bridge point risk assessment coefficients into the bridge risk assessment combing function to obtain the nth bridge point risk detection coefficient, and add the nth bridge point risk detection coefficient to the multiple bridge point risk detection coefficients; according to the bridge risk assessment channel and the bridge risk assessment combing function, continue to perform risk assessment on the multi-scenario bridge load-bearing test vector set to generate the multiple bridge point risk detection coefficients.

[0012] In a possible implementation, the bridge risk assessment combing function is:

[0013] ;

[0014] Among them, brf represents the risk detection coefficient of bridge points, xbk r Represents the risk assessment coefficient of the rth bridge point, r is a positive integer, 1≤r≤R, bkc r Characterizes the accuracy of the assessment branch output corresponding to the risk assessment coefficient of the r-th bridge point.

[0015] Through the bridge engineering risk assessment platform based on load-bearing analysis proposed in this application, it is planned to obtain the target bridge engineering task, model the bridge engineering construction plan, obtain the target bridge model, build the load scenario for the target bridge, generate the bridge load scenario particle group that meets the load scenario variation rule, conduct the confidence load-bearing test on the target bridge model according to the bridge load scenario particle group, obtain the multi-scenario bridge load-bearing test vector set, conduct the risk assessment on the multi-scenario bridge load-bearing test vector set based on the embedded bridge risk assessment channel and bridge risk assessment combing function, obtain the bridge engineering risk assessment report, optimize the bridge engineering construction plan according to the bridge engineering risk assessment report, and obtain the optimized bridge construction plan. It solves the technical problem that it is difficult to fully consider multiple load scenarios in the existing bridge engineering risk assessment, which leads to the inaccurate and unreliable risk assessment results of the bridge load-bearing performance, and achieves the technical effect of improving the accuracy and reliability of bridge engineering risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0017] Figure 1 A schematic diagram of the structure of a bridge engineering risk assessment platform based on load-bearing capacity analysis provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of the execution process of the load scenario building layer in the bridge engineering risk assessment platform based on load-bearing analysis provided in an embodiment of the present application.

[0019] Explanation of the reference numerals: bridge engineering task perception layer 10, BIM interaction layer 20, load scenario construction layer 30, load-bearing simulation layer 40, bridge engineering risk assessment layer 50, bridge engineering optimization layer 60. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0023] The present application embodiment provides a bridge engineering risk assessment platform based on load-bearing analysis, such as Figure 1 As shown, the platform includes:

[0024] The bridge engineering task perception layer 10 is used to obtain the target bridge engineering task, wherein the target bridge engineering task includes the bridge engineering construction plan of the target bridge. The bridge engineering task perception layer is mainly responsible for obtaining the relevant information of the target bridge engineering task, mainly including the bridge engineering construction plan of the target bridge. Specifically, the bridge engineering construction plan is a detailed guidance document for bridge engineering construction, covering the entire process of construction, including construction methods, construction sequence, construction measures, quality control, safety assurance and other aspects. The construction method includes the specific construction methods of each link such as bridge foundation construction, pier construction, and superstructure construction, such as cast-in-place pile construction, template installation and removal, steel bar processing and installation, concrete pouring, etc. The construction sequence details the sequence and logical relationship of each construction link to ensure the rationality and efficiency of the construction process; construction measures refer to solutions or measures proposed for specific construction links or problems, such as preventing concrete cracking and ensuring bridge alignment; quality control and safety assurance refer to detailed provisions for quality control and safety assurance measures during the construction process to ensure the quality and safety of bridge engineering. The bridge engineering task perception layer collects and analyzes this information to provide basic data and basis for subsequent risk assessment, modeling, load scenario construction, load-bearing simulation and other steps.

[0025] BIM interaction layer 20, the BIM interaction layer 20 is used to model according to the bridge engineering construction plan to obtain the target bridge model. The BIM interaction layer mainly models based on the bridge engineering construction plan to obtain the target bridge model. Specifically, the BIM interaction layer receives the target bridge engineering construction plan from the bridge engineering task perception layer, and uses BIM technology to model according to the detailed information in the construction plan. Among them, BIM technology allows the various parts and components of the bridge to be accurately represented in three-dimensional form, including its geometric shape, structural parameters, material properties, etc., including terrain geological modeling, bridge structure modeling, and adding attribute and parameter information. Before modeling, the BIM interaction layer may need to further collect data related to the bridge, such as terrain point cloud data, geographic information, soil data, etc., and then use the terrain point cloud data to pass terrain modeling software (such as ArcGIS, Civil 3D, etc.) to process and optimize the terrain and generate a three-dimensional terrain entity model. Then, according to the design drawings and construction plans, use BIM software (such as AutoCAD, Tekla, etc.) to create a 3D model of the bridge. Specifically, determine the reference point and coordinate system of the bridge so that it can be accurately positioned during the modeling process. Draw a sketch of the bridge according to the design drawings to determine the basic shape and size of the bridge. Use the modeling function of the BIM software to create a three-dimensional model of the bridge according to the sketch. During the modeling process, it is necessary to add details such as piers, bridge decks, and approach bridges, and consider factors such as the geometric shape, structural parameters, and material properties of the bridge. Finally, through the parametric design function, that is, add attributes to the bridge model, such as the material, size, and structure of the components, connect the bridge model with the analysis software, perform statics, dynamics, and other analyses, and evaluate the structural safety and stability of the bridge. During the analysis process, it is necessary to consider the stress conditions under different load conditions and the deformation and stress distribution of the bridge. Compare and verify the analysis results with the design specifications to ensure that the bridge model meets the design requirements. According to the analysis results and verification results, further adjust the model parameters to complete the modeling and finally obtain the target bridge model. The modeling data is as follows:

[0026]

[0027] Among them, the X-axis of the coordinates (X, Y, Z) represents the length of the bridge, the Y-axis represents the width of the bridge, and the Z-axis represents the height of the bridge, all in meters; the piers are located at the key bearing points of the bridge and support the bridge deck; the bridgehead is the two ends of the bridge and is used to connect the roads; the beam connects the two piers, supports the bridge deck, and bears the load transmitted from the bridge deck; the support is located under the beam to provide additional stability and support.

[0028] The load scenario building layer 30 is used to build load scenarios for the target bridge and generate a bridge load scenario particle swarm that meets the load scenario variation rules. The load scenario building layer is mainly responsible for simulating and constructing various load scenarios that the target bridge may encounter to generate a bridge load scenario particle swarm that meets the load scenario variation rules. Specifically, according to the design requirements and use conditions of the bridge, the variation rules of the load scenarios are defined, and then the particle swarm is initialized based on the particle swarm optimization algorithm, wherein each particle represents a possible load scenario. During initialization, a certain number of particles are randomly generated and assigned initial positions and velocities. The load scenario represented by each particle is applied to the bridge model, and the finite element analysis software is used to calculate the response of the bridge under the load scenario. The fitness of the particles is evaluated according to the response results and the variation rules, and the position and velocity of each particle are updated according to the fitness of the particles. A better load scenario is found through continuous iterative updates. When the convergence conditions are met (such as the number of iterations reaches the upper limit), the iteration is stopped and the optimal load scenario particle swarm is output, representing the optimal or optimal load scenario that meets the variation rules. Close to the optimal load scenario, load scenario refers to the various external forces and influences that a bridge may encounter during its life cycle, such as static loads (such as vehicles, people, deadweight, etc.), dynamic loads (such as earthquakes, wind, etc.), temperature effects, etc.; load scenario variation rules refer to the changes and combinations between different load scenarios, including changes in factors such as load size, direction, and action time. They are formulated based on bridge design specifications, historical data, etc., and are intended to simulate various complex situations that bridges may encounter in actual use. Using computer simulation technology, according to the load scenario variation rules, a bridge load scenario particle group that meets the requirements is generated. Each particle represents a specific load scenario, including parameters such as load type, size, direction, and action time. Through a large number of particle simulations, various load conditions that a bridge may encounter can be fully covered, providing data support for subsequent load-bearing simulations and risk assessments. The specific load scenario particle optimization parameter table is as follows:

[0029]

[0030] Among them, the number of particles and the maximum number of iterations determine the coverage of the search space and the convergence speed of the algorithm; the inertia weight, individual learning coefficient, and social learning coefficient control the dynamic characteristics of the particle search behavior. The inertia weight determines the tendency of the particles to maintain the current speed, while the learning coefficient determines the tendency of the particles to move toward the individual or group optimal solution; the speed limit and position limit ensure that the particles will not leave the search space restricted by physical meaning or design requirements; the load model takes into account the actual load conditions that the bridge design must withstand, such as vehicle load, pedestrian load, wind load, etc.

[0031] The load-bearing simulation layer 40 is used to perform a confidence load-bearing test on the target bridge model according to the bridge load scenario particle swarm to obtain a multi-scenario bridge load-bearing test vector set. The load-bearing simulation layer is mainly responsible for the confidence load-bearing test of the target bridge model, that is, using the bridge load scenario particle group to simulate the load-bearing test of the target bridge model, and evaluate the load-bearing capacity and structural response of the bridge under different load scenarios. Specifically, the load-bearing simulation layer first receives the bridge load scenario particle group from the load scenario construction layer. For each load scenario particle, the load-bearing simulation layer will apply it to the target bridge model for a confidence load-bearing test. During the test, the stress, strain and displacement responses of the bridge model at various key parts will be monitored. At the same time, the load-bearing simulation layer will collect and record a large amount of data, including stress distribution, maximum stress value, displacement, etc. of various parts of the bridge. The load-bearing simulation layer will integrate the data collected under all test scenarios into a multi-scenario bridge load-bearing test vector set. This vector set contains the structural response information of the bridge under various load scenarios, which is an important basis for evaluating the load-bearing performance of the bridge. Through this vector set, we can fully understand the performance of the bridge under different load scenarios, so as to more accurately evaluate its safety and reliability.

[0032] The bridge engineering risk assessment layer 50 performs risk assessment on the multi-scenario bridge load-bearing test vector set based on the embedded bridge risk assessment channel and bridge risk assessment combing function to obtain a bridge engineering risk assessment report. The bridge engineering risk assessment layer is the core component of the bridge engineering risk assessment system. Based on the embedded bridge risk assessment channel and bridge risk assessment combing function, the multi-scenario bridge load-bearing test vector set is deeply analyzed and evaluated to generate a comprehensive bridge engineering risk assessment report. Among them, the bridge risk assessment channel is a systematic assessment framework that covers all aspects of bridge engineering risk assessment to ensure the comprehensiveness and accuracy of the assessment. Specifically, the assessment layer will receive the multi-scenario bridge load-bearing test vector set output from the load-bearing simulation layer. The bridge risk assessment channel determines the appropriate risk assessment indicators based on the characteristics and assessment requirements of the bridge, such as the structural safety, durability, and economy of the bridge, and then conducts risk assessment based on risk assessment methods such as the hierarchical analysis method and the risk assessment matrix method. The multi-scenario bridge load-bearing test vector set is calculated and analyzed to obtain the risk level of the bridge in various aspects to improve the accuracy and reliability of the assessment; the bridge risk assessment combing function is a mathematical model used to analyze the data and The core purpose is to organize, analyze and summarize the information. The risk assessment process is transformed into an output result that is easy to understand and apply. Specifically, the collected multi-scenario bridge load-bearing test vector set is standardized to eliminate the dimensional differences between different data and improve data comparability. According to the risk assessment results, the risk level of the bridge is divided into different levels, such as low risk, medium risk, high risk, etc. Then, based on the risk assessment results and the level division, a detailed bridge engineering risk assessment report is generated, which may include the purpose of the assessment, the process of the assessment, the method of assessment, the results of the assessment, and risk management recommendations. The bridge engineering risk assessment report is the final output of the assessment layer and is a summary of the comprehensive risk assessment of the bridge project. For example, it includes the basic information such as the purpose, background, scope and method of the assessment, and lists in detail the specific assessment results such as the risk level and level division of the bridge in various aspects (such as structural safety, durability, etc.), as well as targeted risk management measures and recommendations based on the assessment results to reduce the risk level of the bridge.

[0033] The bridge engineering optimization layer 60 is used to optimize the bridge engineering construction plan according to the bridge engineering risk assessment report to obtain an optimized bridge construction plan. The bridge engineering optimization layer is a key stage in the design and construction process of bridge engineering. Based on the bridge engineering risk assessment report, the existing bridge engineering construction plan is optimized to obtain a more reasonable, efficient and safe optimized bridge construction plan. Specifically, the bridge engineering optimization layer will carefully analyze the risk assessment report, identify the risk level of the bridge in different construction stages and load scenarios, and determine the key risk points based on the risk level, such as structural design defects, improper material selection, construction process problems, etc. Then, for the identified key risk points, the bridge engineering optimization layer will take a series of measures to optimize the construction plan, such as improving the structural design of the bridge, introducing new materials or new technologies, etc.; selecting more suitable building materials to ensure that the quality, strength and durability of the materials meet the requirements of the project; optimizing the construction process such as optimizing the construction sequence and process, introducing advanced construction technology and equipment; optimizing the construction environment by considering the geological and climatic factors of the construction site and strengthening the coordination with the surrounding environment; integrating the above optimization measures into the original construction plan, providing a more reasonable, efficient and safe construction plan, and finally forming a new optimized bridge construction plan.

[0034] The bridge engineering risk assessment platform based on load-bearing analysis according to the embodiment of the present invention is used to solve the technical problem that it is difficult to fully consider multiple load scenarios in the existing bridge engineering risk assessment, which leads to inaccurate and unreliable risk assessment results of bridge load-bearing performance, and achieves the technical effect of improving the accuracy and reliability of bridge engineering risk assessment. The bridge engineering risk assessment platform based on load-bearing analysis includes: a bridge engineering task perception layer 10, a BIM interaction layer 20, a load scenario construction layer 30, a load-bearing simulation layer 40, a bridge engineering risk assessment layer 50, and a bridge engineering optimization layer 60.

[0035] The specific configuration of the load scene building layer 30 will be described in detail below. Figure 2As shown, the load scenario building layer 30 may further include: obtaining the expected load constraint information of the target bridge. In the process of bridge engineering risk assessment, the load constraint conditions expected or specified for the target bridge are obtained. Specifically, the load constraint describes the constraint conditions of the external forces (such as gravity, wind force, vehicle load, etc.) on the object (here the bridge) to ensure that the structural response (such as stress, displacement, etc.) of the bridge under various possible loads does not exceed the predetermined safety threshold. The specific content of the expected load constraint information may include load type and classification, load limit value, load combination and variation rules, etc., to determine the main load types that the bridge needs to bear, such as dead load (such as bridge deadweight, bridge deck pavement, etc.), live load (such as vehicles, crowds, etc.), Wind loads, earthquake loads, etc. are classified according to the duration and nature of the loads, such as permanent loads, variable loads, accidental loads, etc.; load limit values ​​are usually calculated and determined based on factors such as the structural strength, stiffness and stability of the bridge. For example, for vehicle loads, it may be necessary to set restrictions such as the maximum allowable vehicle weight and vehicle speed; consider the interaction and combined effects between different load types, and formulate reasonable load combination schemes. Consider the interaction and combined effects between different load types, formulate reasonable load combination schemes, and formulate load variation rules to simulate the structural response of the bridge under different load scenarios.

[0036] The load scenario construction layer 30 also includes building a bridge load matrix based on multivariate bridge load indicators, wherein the multivariate bridge load indicators include static loads, live loads, dynamic loads, environmental loads and special loads. The bridge load matrix is ​​a matrix used to systematically organize and manage various types of loads that a bridge may bear. Static loads refer to loads that do not change with time or external conditions, mainly including the deadweight of the bridge structure, prestressing, the effects of shrinkage and creep of concrete, vertical and horizontal pressures generated by the deadweight of the soil, hydrostatic pressure and buoyancy, etc., such as the deadweight of various parts of the bridge, the tension of prestressed steel bars, the pressure of the foundation soil, etc.; live loads refer to loads that change with time or external conditions during the service life, such as vehicle loads, crowd loads, etc.; dynamic loads refer to loads that change with time, and this change is not necessarily periodic. Dynamic loads include vibration loads, impact loads, etc., such as earthquake force, wind load (dynamic effect of wind on bridge structure), vibration caused by vehicle driving, etc.; environmental loads are loads caused by natural environmental factors, such as temperature, humidity, precipitation, wind, waves, etc., such as thermal stress of bridge structure caused by temperature changes, lateral pressure and lift of wind on bridge, impact force of waves on bridge substructure, etc.; special loads refer to loads that are unlikely to occur during the design service life, but once they occur, they will have a greater impact on the structure, such as the impact force of ships on bridge structures, the scouring force of floods on bridges, and loads of special vehicles (such as military vehicles).

[0037] The load scenario building layer 30 also includes, taking the expected load constraint information as the load scenario constraint, randomly setting the load scenario parameters according to the bridge load matrix, and obtaining an initial load scenario particle group that meets the load scenario particle capacity. After determining the expected load constraint information and the bridge load matrix, the load scenario parameters are randomly set according to these information, including selecting the load type, determining the load range, and randomly generating the load value. By randomly setting the load scenario parameters, multiple different load scenarios can be generated, each load scenario can be regarded as a particle, and these particles together constitute the initial load scenario particle group. Each particle (i.e., load scenario) in the particle group meets the requirements of the load scenario particle capacity, that is, the number of particles should be sufficient to fully reflect the various load conditions that the bridge may encounter during actual use.

[0038] The load scenario building layer 30 also includes verifying and optimizing the initial load scenario particle group according to the load scenario variation rule to obtain the bridge load scenario particle group. Each particle (i.e., load scenario) in the initial load scenario particle group is compared and verified with the defined load scenario variation rule to check whether each particle meets the requirements of the variation rule, including whether the load size, action position, action time, etc. change within the specified range. If a particle does not meet the variation rule, it needs to be adjusted or regenerated to ensure that each particle in the particle group meets the load variation in the actual project. On the basis of verification, the initial load scenario particle group is optimized to make the particle group closer to the load distribution in the actual project and meet the requirements of engineering design and analysis. After verification and optimization, the final bridge load scenario particle group is obtained, which includes multiple load scenarios that meet the actual engineering conditions.

[0039] The specific configuration of the load scenario building layer 30 will be described in detail below. The load scenario building layer 30 may further include: the load scenario variation rule includes a load scenario variation threshold. The load scenario variation threshold refers to the maximum allowable range or limit value of the load parameter change set when simulating the stress conditions of the bridge under different load scenarios, which may include the load size variation threshold (the upper and lower limits of the load size change, such as the maximum and minimum weight of the vehicle load, the maximum and minimum wind speed of the wind load, etc.), the load action position variation threshold (the range of changes in the load action position, such as the driving route of the vehicle load on the bridge deck, the specific area where the wind load acts on the bridge, etc.), the load action time variation threshold (the length or frequency of the load action time, such as the driving speed of the vehicle load or the duration of the wind load) and the combined load variation threshold, etc.

[0040] The load scenario building layer 30 also includes generating multiple load scenario variation coefficients by performing pairwise comparisons based on the initial load scenario particle group. Any two particles (i.e., two load scenarios) are selected from the initial load scenario particle group for comparison, and the difference between the two load scenarios is calculated. Based on the calculated difference value, a load scenario variation coefficient can be further generated to describe the discrete degree of the two load scenario particles. Each pair of particles in the initial load scenario particle group is compared, and a corresponding load scenario variation coefficient is generated to obtain a set consisting of multiple variation coefficients, which respectively represent the degree of difference between different load scenarios.

[0041] The load scenario building layer 30 also includes judging whether the coefficients of variation of the multiple load scenarios are greater than / equal to the load scenario variation threshold. The load scenario variation threshold is a preset value used to quantify the degree of difference between load scenarios. Specifically, each load scenario variation coefficient is compared with the load scenario variation threshold. If the coefficient of variation of a load scenario is greater than or equal to the load scenario variation threshold, it is considered that the degree of difference between the load scenario and other load scenarios is large. It also includes adding the initial load scenario particle group to the bridge load scenario particle group if the coefficients of variation of the multiple load scenarios are greater than / equal to the load scenario variation threshold. If the coefficients of variation of the multiple load scenarios are greater than or equal to the preset load scenario variation threshold, it means that there are sufficient differences and diversity between the load scenarios in the initial load scenario particle group, and it can more comprehensively reflect the various complex and uncertain load conditions that the bridge may encounter during actual use. The initial load scenario particle group is added to the bridge load scenario particle group.

[0042] The specific configuration of the load scenario building layer 30 will be described in detail below. The load scenario building layer 30 may further include: if any one of the multiple load scenario variation coefficients is less than the load scenario variation threshold, generating an identified load scenario particle. Among the multiple calculated load scenario variation coefficients, if there is any one variation coefficient that is less than the preset load scenario variation threshold, for these load scenarios with insignificant differences, mark or indicate those load scenarios with smaller changes compared to other scenarios, and generate an identified load scenario particle. It also includes performing variation optimization on the initial load scenario particle group according to the load scenario constraints and the identified load scenario particles to generate an optimized load scenario particle group. A group of initial particles are randomly generated, each particle represents a possible load scenario solution. According to the load scenario constraints and a specific fitness function, the fitness value of each particle is calculated. The fitness value reflects the quality of the solution represented by the particle. According to the information identifying the load scenario particle and the current particle (such as the historical best position and the historical best position of the population), the particle speed and position are updated. The above steps are repeated until the termination condition is met (such as reaching the maximum number of iterations or finding a solution that meets the requirements). During the iteration process, the particle swarm gradually converges to the vicinity of the optimal solution to form an optimized load scenario particle swarm.

[0043] The load scenario building layer 30 also includes verifying and optimizing the optimized load scenario particle group according to the load scenario mutation rule to obtain the bridge load scenario particle group. The optimized load scenario particle group obtained through preliminary optimization is used as input, and mutation operations are performed on these particles according to the load scenario mutation rule, for example, certain properties of the particles (such as load value, position of the point of action, etc.) are changed, or new particles are generated according to the rule. For the mutated particle group, simulation analysis and structural calculation are used to verify whether it meets the requirements and constraints of the bridge engineering load scenario. If the verification result shows that the particle group does not meet the requirements, the mutation rule or parameter setting is adjusted according to the verification result, and mutation and optimization are performed again. Through multiple iterations, the optimization solution that meets the requirements of the bridge engineering load scenario is gradually approached. When the convergence condition is met (such as reaching the maximum number of iterations, the optimization solution is stable and no longer changes, etc.), the iteration is stopped and the final result is output. The particle group that meets the requirements of the bridge engineering load scenario is finally obtained, that is, the bridge load scenario particle group. Each particle in this particle group represents a possible bridge load scenario.

[0044] The specific configuration of the load-bearing simulation layer 40 will be described in detail below. The load-bearing simulation layer 40 may further include: extracting a first bridge load scenario particle according to the bridge load scenario particle group. The first bridge load scenario particle refers to any bridge load scenario particle in the bridge load scenario particle group. It also includes obtaining a confident load-bearing test number. The confident load-bearing test number is used to ensure the accuracy and reliability of the simulation test. Usually, the required number of tests is calculated based on the required confidence level and the allowable error range, combined with historical data or expert experience. For example, if a 95% confidence level is required and the allowable error does not exceed 5%, the required number of tests can be obtained through a confidence calculation formula or a table lookup. It also includes, based on the confident load-bearing test number, performing multiple load-bearing simulation tests on the target bridge model according to the first bridge load scenario particle to obtain multiple bridge stress-strain simulation data sets. Using the first bridge load scenario particle as input, multiple load-bearing simulation tests are performed on the target bridge model. The number of simulation tests should be equal to or greater than the number of confidence load-bearing tests obtained previously to ensure the reliability of the test results. In each simulation test, the stress and strain data of the bridge are recorded to form a simulation data set. For example, if the number of confidence load-bearing tests is 5 times, 5 or more load-bearing simulation tests are repeated to obtain 5 or corresponding times of bridge stress and strain simulation data sets.

[0045] The load-bearing simulation layer 40 also includes clustering stress and strain point features according to the multiple bridge stress and strain simulation data sets to obtain multiple bridge point stress and strain sample areas. The multiple bridge stress and strain simulation data sets obtained are processed and analyzed, and based on the stress and strain data of different points on the bridge, features useful for clustering analysis are extracted from the original data, such as stress and strain values, temperature changes, load magnitudes, etc., to reflect the mechanical behavior characteristics of bridge points under different working conditions. Clustering algorithms (such as K-means, hierarchical clustering, etc.) are then applied to perform feature clustering, and the pre-processed data is input into the algorithm, and appropriate clustering parameters (such as cluster number K, distance method, etc.) are set. The algorithm will automatically cluster the data with similar stress and strain characteristics according to the characteristics of the data. The points with similar characteristics are grouped into one category to form multiple bridge point stress and strain sample areas. Each sample area represents a group of bridge points with similar stress and strain characteristics. The bridge point stress and strain sample areas obtained by clustering are mapped to the bridge model to form an intuitive visual representation. Then, according to the clustering results, the parameters of the bridge load-bearing simulation are set, such as material properties, boundary conditions, load distribution, etc., to reflect the mechanical behavior characteristics of different stress and strain sample areas, ensure the accuracy and reliability of the simulation results, and use finite element analysis software to perform load-bearing simulation calculations on the bridge. The specific bridge point stress and strain data table is as follows:

[0046]

[0047] Among them, the X-coordinate and Y-coordinate describe the position of the point on the bridge; stress is the stress value measured at each point; strain is the strain percentage measured at each point.

[0048] It also includes calculating the concentrated value according to the stress-strain sample areas of the multiple bridge points, obtaining the first scenario bridge load-bearing test vector, and adding the first scenario bridge load-bearing test vector to the multi-scenario bridge load-bearing test vector set. In each sample area, the concentrated value of stress and strain is calculated, that is, the stress and strain data of each point are counted, and the average value, median or other statistics are calculated as the concentrated value, and the multiple point data in each sample area are simplified into a representative value, and a bridge load-bearing test vector representing a specific load scenario (i.e., the first scenario) is constructed, which includes the concentrated values ​​of stress and strain at each key point on the bridge under the load scenario, and is used to describe the overall response of the bridge under the scenario, and the first scenario bridge load-bearing test vector is added to the multi-scenario bridge load-bearing test vector set, and the multi-scenario bridge load-bearing test vector set is used to store the bridge load-bearing test vectors under each load scenario.

[0049] The load-bearing simulation layer 40 also includes, based on the number of confident load-bearing tests, continuing to perform confident load-bearing tests on the target bridge model according to the bridge load scenario particle group, and generating the multi-scenario bridge load-bearing test vector set. For each load scenario particle in the bridge load scenario particle group, multiple simulation tests are performed on the target bridge model according to the determined number of confident load-bearing tests, and in each load scenario, the data of key points on the bridge are concentrated and calculated to obtain representative stress-strain values, and the representative stress-strain values ​​of the key points are combined into a bridge load-bearing test vector, and the bridge load-bearing test vectors of all scenarios are summarized to form a set containing multiple scenario test vectors, that is, a multi-scenario bridge load-bearing test vector set.

[0050] The specific configuration of the bridge engineering risk assessment layer 50 will be described in detail below. The bridge engineering risk assessment layer 50 may further include: inputting the multi-scenario bridge load-bearing test vector set into the bridge engineering risk assessment layer to obtain risk detection coefficients for multiple bridge points. The generated multi-scenario bridge load-bearing test vector set (including stress and strain data of key bridge points under different load scenarios) is input into the bridge engineering risk assessment layer, and the risk assessment layer performs risk detection on each key point on the bridge, such as comparing the actual stress and strain data with the design threshold and historical data to determine the risk level of each point, and generating a risk detection coefficient for each point, which represents the risk size of the point. It also includes, according to the bridge load scenario particle group, performing load intensity evaluation to obtain multiple test scenario load intensity coefficients. Performing strength evaluation on each load scenario in the bridge load scenario particle group may include analyzing factors such as the size, distribution, and frequency of the load to determine the strength or importance of each load scenario, and generating a load intensity coefficient for each load scenario, that is, the test scenario load intensity coefficient, which represents the degree of influence of the scenario on the bridge risk.

[0051] The bridge engineering risk assessment layer 50 also includes: performing a proportion calculation according to the load intensity coefficients of the multiple test scenarios, and outputting multiple test scenario risk value coefficients. Performing a proportion calculation according to the load intensity coefficient of each test scenario, that is, calculating their risk value according to the importance or weight of each scenario, and generating a risk value coefficient for each test scenario, which comprehensively considers the load intensity and the importance of the scenario. It also includes: performing a weighted calculation on the risk detection coefficients of the multiple bridge points according to the risk value coefficients of the multiple test scenarios, and outputting a bridge engineering risk assessment coefficient. Using the risk value coefficients of the test scenarios to perform a weighted calculation on the risk detection coefficients of the bridge points, comprehensively considering the impact of different load scenarios on the risks of each bridge point, thereby obtaining a comprehensive bridge engineering risk assessment coefficient, which represents the overall risk level of the entire bridge project under different load scenarios. It also includes integrating the bridge load scenario particle group, the multi-scenario bridge load-bearing test vector set, the multiple bridge point risk detection coefficients and the bridge engineering risk assessment coefficient to generate the bridge engineering risk assessment report. The bridge load scenario particle swarm, multi-scenario bridge load-bearing test vector set, multiple bridge point risk detection coefficients and bridge engineering risk assessment coefficients are integrated to form a complete bridge engineering risk assessment report, which may include detailed test data, charts, risk analysis results and recommendations.

[0052] The specific configuration of the bridge engineering risk assessment layer 50 will be described in detail below. The bridge engineering risk assessment layer 50 may further include: extracting the nth scenario bridge bearing test vector according to the multi-scenario bridge bearing test vector set, where n is a positive integer. Selecting and extracting the bridge bearing test vector corresponding to the nth scenario from the multi-scenario bridge bearing test vector set, where n is a positive integer representing a specific scenario in the test vector set. It also includes that the bridge risk assessment channel includes R bridge risk assessment branches, where R is a positive integer greater than 1. The bridge risk assessment channel is an assessment model including R bridge risk assessment branches, and the R bridge risk assessment branches are used to comprehensively assess the risk of the bridge from different angles. For example, the structural safety assessment branch focuses on the response analysis of the bridge structure under static and dynamic loads, including stress, strain, displacement, etc., to assess the overall safety performance of the bridge structure; the durability assessment branch considers factors such as aging, corrosion, fatigue, etc. of the bridge material, and assesses the service life and durability of the bridge; the stability assessment branch analyzes the stability of the bridge under various extreme conditions, such as strong winds, earthquakes, floods, etc. The method further includes inputting the load-bearing test vector of the bridge in the nth scenario into the R bridge risk assessment branches to obtain risk assessment coefficients of R bridge points. The extracted load-bearing test vector of the bridge in the nth scenario is input into the R bridge risk assessment branches, and each assessment branch calculates and analyzes the input test vector to obtain the risk assessment coefficient of the corresponding bridge point. These risk assessment coefficients represent the risk level or potential problems of each point of the bridge in a specific scenario.

[0053] The bridge engineering risk assessment layer 50 also includes: inputting the R bridge point risk assessment coefficients into the bridge risk assessment combing function, obtaining the nth bridge point risk detection coefficient, and adding the nth bridge point risk detection coefficient to the multiple bridge point risk detection coefficients. The R bridge point risk assessment coefficients are input into the bridge risk assessment combing function, and the bridge risk assessment combing function comprehensively analyzes and processes the risk assessment coefficients from different assessment branches to obtain a more comprehensive and accurate bridge point risk detection coefficient, outputs the nth bridge point risk detection coefficient, and adds this coefficient to the set of multiple bridge point risk detection coefficients. It also includes: continuing to perform risk assessment on the multi-scenario bridge load-bearing test vector set according to the bridge risk assessment channel and the bridge risk assessment combing function, and generating the multiple bridge point risk detection coefficients. The above risk assessment is repeated for each scenario bridge load-bearing test vector in the multi-scenario bridge load-bearing test vector set, and a corresponding bridge point risk detection coefficient is generated for each scenario.

[0054] The specific configuration of the bridge engineering risk assessment layer 50 will be described in detail below. The bridge engineering risk assessment layer 50 may further include: the bridge risk assessment combing function is:

[0055] ;

[0056] Among them, brf represents the risk detection coefficient of bridge points, xbk r Represents the risk assessment coefficient of the rth bridge point, r is a positive integer, 1≤r≤R, bkc r Characterizes the accuracy of the assessment branch output corresponding to the risk assessment coefficient of the r-th bridge point.

[0057] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0058] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. Bridge engineering risk assessment platform based on load-bearing analysis, characterized by: The platform includes: A bridge engineering task perception layer, the bridge engineering task perception layer is used to obtain a target bridge engineering task, wherein the target bridge engineering task includes a bridge engineering construction plan of a target bridge; A BIM interaction layer, wherein the BIM interaction layer is used to perform modeling according to the bridge engineering construction plan to obtain a target bridge model; A load scenario building layer, wherein the load scenario building layer is used to build a load scenario for the target bridge and generate a bridge load scenario particle swarm that satisfies the load scenario variation rule; A load-bearing simulation layer, wherein the load-bearing simulation layer is used to perform a confidence load-bearing test on the target bridge model according to the bridge load scenario particle swarm to obtain a multi-scenario bridge load-bearing test vector set; A bridge engineering risk assessment layer, wherein the bridge engineering risk assessment layer performs risk assessment on the multi-scenario bridge load-bearing test vector set based on an embedded bridge risk assessment channel and a bridge risk assessment combing function to obtain a bridge engineering risk assessment report; A bridge engineering optimization layer, the bridge engineering optimization layer is used to optimize the bridge engineering construction plan according to the bridge engineering risk assessment report to obtain an optimized bridge construction plan; The load-bearing simulation layer performs the following steps: Extracting a first bridge load scenario particle according to the bridge load scenario particle group; Get the number of confident load-bearing tests; Based on the number of confident load-bearing tests, performing multiple load-bearing simulation tests on the target bridge model according to the first bridge load scenario particles to obtain multiple bridge stress-strain simulation data sets; Perform stress and strain point feature clustering according to the multiple bridge stress and strain simulation data sets to obtain multiple bridge point stress and strain sample areas; Performing concentrated value calculation according to the stress-strain sample areas of the multiple bridge points to obtain a first-scenario bridge load-bearing test vector, and adding the first-scenario bridge load-bearing test vector to the multi-scenario bridge load-bearing test vector set; Based on the number of confidence load-bearing tests, the target bridge model is continuously subjected to confidence load-bearing tests according to the bridge load scenario particle swarm to generate the multi-scenario bridge load-bearing test vector set.

2. The platform according to claim 1, characterized in that The load scenario building layer includes the following steps: Obtaining expected load constraint information of the target bridge; According to the multivariate bridge load index, a bridge load matrix is ​​constructed, wherein the multivariate bridge load index includes static load, live load, dynamic load, environmental load and special load. Static load refers to the load that does not change with time or external conditions. Live load refers to the load that changes with time or external conditions during the service life. Dynamic load refers to the load that changes with time and the change is not necessarily periodic. Environmental load is the load caused by natural environmental factors. Special load refers to the load that will have a greater impact on the structure. Taking the expected load constraint information as the load scenario constraint, randomly setting the load scenario parameters according to the bridge load matrix, and obtaining an initial load scenario particle group that meets the load scenario particle capacity; The initial load scenario particle swarm is verified and optimized according to the load scenario variation rule to obtain the bridge load scenario particle swarm.

3. The platform according to claim 2, characterized in that The load scenario building layer includes the following steps: The load scenario variation rule includes a load scenario variation threshold; Performing pairwise comparisons on the initial load scenario particle groups to generate multiple load scenario variation coefficients; Determining whether the multiple load scenario variation coefficients are greater than / equal to the load scenario variation threshold; If the variation coefficients of the multiple load scenarios are all greater than / equal to the load scenario variation threshold, the initial load scenario particle group is added to the bridge load scenario particle group.

4. The platform according to claim 3, characterized in that The load scenario building layer includes the following steps: If any load scenario variation coefficient among the multiple load scenario variation coefficients is less than the load scenario variation threshold, generating an identification load scenario particle; performing mutation optimization on the initial load scenario particle group according to the load scenario constraints and the identified load scenario particles to generate an optimized load scenario particle group; The optimized load scenario particle swarm is verified and optimized according to the load scenario variation rule to obtain the bridge load scenario particle swarm.

5. The platform according to claim 1, characterized in that The steps of the bridge engineering risk assessment layer include: Inputting the multi-scenario bridge load-bearing test vector set into the bridge engineering risk assessment layer to obtain risk detection coefficients of multiple bridge points; Performing load intensity evaluation according to the bridge load scenario particle swarm to obtain multiple test scenario load intensity coefficients; Performing a proportion calculation based on the load intensity coefficients of the multiple test scenarios, and outputting risk value coefficients of the multiple test scenarios; Performing weighted calculation on the risk detection coefficients of the plurality of bridge points according to the risk value coefficients of the plurality of test scenarios, and outputting a risk assessment coefficient for the bridge project; The bridge load scenario particle swarm, the multi-scenario bridge load-bearing test vector set, the multiple bridge point risk detection coefficients and the bridge engineering risk assessment coefficients are integrated to generate the bridge engineering risk assessment report.

6. The platform according to claim 5, characterized in that The steps of the bridge engineering risk assessment layer include: Extracting the nth scenario bridge load-bearing test vector according to the multi-scenario bridge load-bearing test vector set, where n is a positive integer; The bridge risk assessment channel includes R bridge risk assessment branches, where R is a positive integer greater than 1; Inputting the n-th scenario bridge load-bearing test vector into the R bridge risk assessment branches to obtain R bridge point risk assessment coefficients; Inputting the R bridge point risk assessment coefficients into the bridge risk assessment combing function to obtain the nth bridge point risk detection coefficient, and adding the nth bridge point risk detection coefficient to the multiple bridge point risk detection coefficients; According to the bridge risk assessment channel and the bridge risk assessment combing function, the risk assessment of the multi-scenario bridge load-bearing test vector set is continued to be performed to generate the risk detection coefficients of the multiple bridge points.

7. The platform according to claim 1, characterized in that The bridge risk assessment combing function is: Among them, brf represents the risk detection coefficient of bridge points, xbk r Represents the risk assessment coefficient of the rth bridge point, r is a positive integer, 1≤r≤R, bkc r Characterizes the accuracy of the assessment branch output corresponding to the risk assessment coefficient of the r-th bridge point.

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