Bridge construction risk assessment method and system, electronic device and storage medium

Abstract

The application provides a bridge construction risk assessment method, system, electronic equipment and storage medium, and belongs to the technical field of bridge construction safety; the method comprises the following steps: confirming a risk analysis total target of a target bridge; decomposing the risk analysis total target to construct a risk evaluation model of a layered structure; determining weight values corresponding to an evaluation set by using an analytic hierarchy process, and determining a risk evaluation grade based on the evaluation set and the weight values; scoring each evaluation factor in the risk evaluation grade to establish a membership matrix; performing operation on the membership matrix by using a comprehensive weighting method to obtain a fuzzy evaluation matrix; calculating a judgment matrix corresponding to the evaluation set according to the fuzzy evaluation matrix, and obtaining the risk grade of the evaluation set based on the maximum membership degree principle. The application determines the evaluation set composed of risk factors and the corresponding weight by using a hierarchical decomposition and analysis method, and quantifies the risk factors by using a fuzzy comprehensive evaluation method of mathematics, so that the comprehensiveness and accuracy of the evaluation result are improved.

Description

Technical Field

[0001] This invention belongs to the technical field of bridge construction safety, and specifically relates to a bridge construction risk assessment method, system, electronic device and storage medium. Background Technology

[0002] In recent years, with the rapid pace of economic development, infrastructure construction projects have increased, including road and bridge construction, which has significantly accelerated the economic development process. Roads and bridges play an increasingly prominent role in connecting cities and regions, gradually becoming an indispensable part of infrastructure construction. The requirements for the form and function of roads and bridges are also becoming higher, thus continuously promoting the development of road and bridge construction technology. At the same time, it is not difficult to find that bridges are prone to various failure risks during construction. For example, bridges are prone to cracking, steel reinforcement corrosion, and loosening and detachment of pavement layers. Other risks include pile foundation failures and cracks appearing at the stirrup locations on piers. Improving the construction capabilities of road and bridge engineering and enhancing construction risk assessment remain urgent technical problems to be solved.

[0003] Construction risks in roads and bridges differ from other risks due to the unique nature of the construction process, the difficulty in predicting these risks, and the resulting losses and consequences. These risk factors have a complex relationship with the structural safety of roads and bridges, which cannot be qualitatively expressed using a clear functional relationship. Therefore, experts have extensive experience in road and bridge risk analysis. Commonly used risk assessment methods in existing technologies include expert scoring, Monte Carlo methods, sensitivity analysis, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation. However, using a single assessment method can only provide a purely qualitative or quantitative analysis of risk. While this method improves risk assessment capabilities to some extent compared to traditional manual statistical methods, empirical values, or bridge construction monitoring methods, the comprehensiveness and accuracy of the evaluation results remain relatively low.

[0004] Therefore, it is particularly important to design an effective risk assessment method to evaluate the safety of road and bridge construction processes. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a bridge construction risk assessment method, system, electronic device, and storage medium. It employs hierarchical decomposition and analysis to determine the evaluation set of risk factors and their corresponding weights, and uses a fuzzy comprehensive evaluation method based on mathematical means to quantify the risk factors, thereby improving the accuracy of the assessment results and providing guidance for actual safe construction.

[0006] In a first aspect, the embodiments of this application provide a bridge construction risk assessment method, which includes:

[0007] Based on the historical risk data collected from similar bridge construction projects, the overall risk analysis objective for the target bridge was determined.

[0008] The overall risk analysis objective is decomposed to construct a layered risk assessment model; wherein, all risk factors in each layer of the risk assessment model constitute the assessment set of that layer;

[0009] The weight values ​​corresponding to the evaluation set are determined by the analytic hierarchy process (AHP), and the risk assessment level is determined based on the evaluation set and the weight values.

[0010] A membership matrix is ​​established by scoring each evaluation factor in the aforementioned risk assessment level.

[0011] A fuzzy evaluation matrix is ​​obtained by performing calculations on the membership matrix using a comprehensive weighting method.

[0012] The evaluation matrix corresponding to the evaluation set is calculated based on the fuzzy evaluation matrix, and the risk level of the evaluation set is obtained based on the principle of maximum membership.

[0013] Preferably, the step of confirming the overall risk analysis objective of the target bridge based on collected historical risk data from similar bridge construction projects specifically includes:

[0014] Based on the project information of the target bridge, search and collect historical risk data on the construction of bridges similar to the target bridge;

[0015] A risk source identification table is created based on the historical risk data presented in the statistics;

[0016] Based on the project data and the risk source identification table, the identification results were obtained by using empirical rules.

[0017] The identified results are compiled into a risk source list with sub-projects as summary units and individual projects as identification units, in order to confirm the overall risk analysis objective of the target bridge.

[0018] Preferably, the step of decomposing the overall objective to construct a layered risk assessment model, wherein all risk factors in each layer of the risk assessment model constitute the assessment set for that layer, specifically includes:

[0019] The overall objective is decomposed into several sub-objectives using a hierarchical decomposition method to form an intermediate layer of the hierarchical analysis, and the intermediate layer is further decomposed into a next layer to form the third layer of the hierarchical analysis.

[0020] By analyzing the nature of each risk factor in each layer and the interrelationships between the risk factors, the membership relationships between the risk factors can be identified.

[0021] Based on the aforementioned hierarchical relationships, a risk assessment model corresponding to the overall risk analysis objective is constructed.

[0022] Preferably, the step of determining the weight values ​​corresponding to the evaluation set using the analytic hierarchy process (AHP) and determining the risk assessment level based on the evaluation set and the weight values ​​specifically includes:

[0023] The relative importance of each risk factor in the evaluation set is determined based on a pairwise comparison method in order to construct a judgment matrix;

[0024] The eigenvectors are obtained by solving the judgment matrix using a pre-defined rule.

[0025] Based on the feature vector, the target feature vector corresponding to the largest feature vector is obtained, so as to obtain the weight value corresponding to the evaluation set;

[0026] The risk assessment is divided into several risk levels based on the evaluation set and the weight values ​​to obtain the risk assessment level required for the overall risk analysis objective.

[0027] Preferably, the prefabrication rule is specifically: Ax = λ max x, where A is the judgment matrix, x is the unknown vector, and λ max These are the eigenvectors.

[0028] Preferably, the step of scoring each evaluation factor in the risk assessment level to establish a membership matrix specifically includes:

[0029] An expert scoring table is generated based on each evaluation factor in the aforementioned risk assessment level;

[0030] Based on the data filled in by experts in the collected expert rating table, a mean table of membership degrees given by each expert is calculated to establish a membership function;

[0031] A membership matrix is ​​established for each evaluation factor based on the membership function.

[0032] Preferably, the step of using a comprehensive weighting method to calculate the fuzzy evaluation matrix from the membership matrix specifically includes:

[0033] The comprehensive weighting method based on relevance consistency obtains the comprehensive weight of one of the evaluation sets relative to its superior evaluation set.

[0034] Based on the comprehensive weights, the fuzzy evaluation matrix corresponding to the evaluation set is obtained by using a preset formula; wherein, the preset formula is: P i =W Bi ×R Bi In the formula, P i W represents the fuzzy evaluation matrix. Bi R represents the overall weight.Bi This represents the membership matrix.

[0035] Secondly, this application provides a bridge construction risk assessment method, which includes:

[0036] The confirmation module is used to confirm the overall risk analysis objectives of the target bridge based on historical risk data collected from similar bridge construction projects.

[0037] A construction module is used to decompose the overall risk analysis objective to construct a layered risk assessment model; wherein, all risk factors in each layer of the risk assessment model constitute the assessment set of that layer;

[0038] The determination module is used to determine the weight values ​​corresponding to the evaluation set using the analytic hierarchy process (AHP), and to determine the risk assessment level based on the evaluation set and the weight values.

[0039] The scoring module is used to score each evaluation factor in the risk assessment level to establish a membership matrix;

[0040] The calculation module is used to perform calculations on the membership matrix using a comprehensive weighting method to obtain a fuzzy evaluation matrix;

[0041] The evaluation module is used to calculate the evaluation matrix corresponding to the evaluation set based on the fuzzy evaluation matrix, and to derive the risk level of the evaluation set based on the principle of maximum membership.

[0042] Preferably, the confirmation module includes:

[0043] The collection unit is used to search and collect historical risk data of bridge construction similar to the target bridge based on the project information of the target bridge;

[0044] The production unit is used to create a risk source identification table based on the statistically analyzed historical risk data;

[0045] The identification unit is used to identify the project data and the risk source identification table using empirical rules to obtain the identification result.

[0046] The confirmation unit is used to organize the identification results into a risk source list with sub-projects as summary units and individual projects as identification units, in order to confirm the overall risk analysis objective of the target bridge.

[0047] Preferably, the building module includes:

[0048] The decomposition unit is used to decompose the total objective into several sub-objectives using the hierarchical decomposition method to form an intermediate layer of the hierarchical analysis, and to further decompose the intermediate layer into a next layer to form a third layer of the hierarchical analysis.

[0049] The analysis unit is used to identify the membership relationships between risk factors by analyzing the nature of each risk factor in each layer and the interrelationships between the risk factors.

[0050] The construction unit is used to construct a risk assessment model corresponding to the overall risk analysis objective based on the aforementioned membership relationship.

[0051] Preferably, the determining module includes:

[0052] A construction unit is used to determine the relative importance between each risk factor in the evaluation set based on a pairwise comparison method, so as to construct a judgment matrix;

[0053] The solving unit is used to solve the judgment matrix using a pre-defined rule to obtain the eigenvectors; wherein the pre-defined rule is specifically: Ax = λ max x, where A is the judgment matrix, x is the unknown vector, and λ max For feature vectors;

[0054] The calculation unit is used to calculate the target feature vector corresponding to the largest feature vector based on the feature vector, so as to obtain the weight value corresponding to the evaluation set;

[0055] A partitioning unit is used to divide the risk assessment into several risk levels according to the evaluation set and the weight values, so as to obtain the risk assessment level required for the overall risk analysis objective.

[0056] Preferably, the scoring module includes:

[0057] The generation unit is used to generate an expert scoring table based on each evaluation factor in the risk assessment level.

[0058] The calculation unit is used to calculate the mean table of membership degrees given by each expert based on the data filled in by the experts in the collected expert rating table to establish a membership function;

[0059] Establish a unit for establishing a membership matrix for each evaluation factor based on the membership function.

[0060] Preferably, the computing module includes:

[0061] The acquisition unit is used to acquire the comprehensive weight of one of the evaluation sets relative to its superior evaluation set using a comprehensive weighting method based on relevance consistency.

[0062] The calculation unit is used to calculate the fuzzy evaluation matrix corresponding to the evaluation set based on the comprehensive weights using a preset formula; wherein, the preset formula is: P i =W Bi ×R Bi In the formula, P iW represents the fuzzy evaluation matrix. Bi R represents the overall weight. Bi This represents the membership matrix.

[0063] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the bridge construction risk assessment method as described in the first aspect.

[0064] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the bridge construction risk assessment method as described in the first aspect.

[0065] Compared to existing technologies, this application provides a bridge construction risk assessment method, system, electronic device, and storage medium. By using historical risk data from similar bridges to determine the overall risk analysis objective for the target bridge, the risk identification results are more accurate and comprehensive. The overall risk analysis objective is decomposed using a hierarchical decomposition method, and a risk assessment model is constructed based on the properties and relationships of each risk factor to ensure the comprehensiveness of the risk assessment. The hierarchical analysis method is used to determine the weight values ​​corresponding to the evaluation set and thus determine the risk assessment level required for the overall risk analysis objective, ensuring the rationality of the analysis and assessment. A fuzzy comprehensive evaluation method is used to score each evaluation factor in the risk assessment level, establish a membership matrix, and calculate a fuzzy evaluation matrix to quantify the risk factors. The judgment matrix corresponding to the evaluation set is calculated based on the fuzzy evaluation matrix, and the risk level of the evaluation set is derived based on the principle of maximum membership. Through the above steps, the hierarchical decomposition and analysis method is used to determine the evaluation set composed of risk factors and their corresponding weights, and the fuzzy comprehensive evaluation method is used to quantify the risk factors, thereby improving the comprehensiveness and accuracy of the assessment results and providing guidance for actual safe construction. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 A flowchart of the bridge construction risk assessment method provided in Embodiment 1 of the present invention;

[0068] Figure 2 This is a structural block diagram of the bridge construction risk assessment system corresponding to the method in Embodiment 1 provided in Embodiment 2 of the present invention;

[0069] Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in Embodiment 3 of the present invention.

[0070] Explanation of reference numerals in the attached figures:

[0071] 10-Confirmation Module, 11-Collection Unit, 12-Production Unit, 13-Identification Unit, 14-Confirmation Unit;

[0072] 20 - Building module, 21 - Decomposition unit, 22 - Analysis unit, 23 - Building unit;

[0073] 30 - Define the module; 31 - Construct the element; 32 - Solve the element; 33 - Obtain the element; 34 - Divide the element.

[0074] 40-Scoring module, 41-Generation unit, 42-Calculation unit, 43-Establishment unit;

[0075] 50 - Calculation module, 51 - Acquisition unit, 52 - Calculation unit;

[0076] 60 - Evaluation Module;

[0077] 70-Bus, 71-Processor, 72-Memory, 73-Communication interface. Detailed Implementation

[0078] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0079] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0080] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0081] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0082] Example 1

[0083] Specifically, Figure 1 The diagram shown is a flowchart of a bridge construction risk assessment method provided in this embodiment.

[0084] like Figure 1 As shown, the bridge construction risk assessment method in this embodiment includes the following steps:

[0085] S101, Based on the collected historical risk data of similar bridge construction, confirm the overall risk analysis objective of the target bridge.

[0086] Specifically, risk assessment is primarily qualitative, mainly due to a lack of statistical data on risk incidents. The absence of a basic database is the most significant reason why quantitative risk assessment is rarely used; statistical data on risk incidents is the most important and crucial component of such a database. While risk incidents during bridge construction are identified through correspondence and inquiries, the results may be incomplete and inaccurate due to the subjective factors of experts and their limited understanding of risk incidents. This embodiment addresses bridge accident statistics by collecting risk incident data from similar past projects, sub-projects, and sub-items, compiling and statistically analyzing more complete risk incident data. The results are more objective and comprehensive, effectively avoiding the shortcomings of expert survey methods.

[0087] Furthermore, the specific steps of step S101 include:

[0088] S1011, Based on the project information of the target bridge, search and collect historical risk data of bridge construction similar to the target bridge.

[0089] Specifically, due to limited resources and channels, this embodiment obtains information on various safety accidents that occurred during bridge construction by consulting literature, journals, news reports, and government websites. Historical risk data on similar bridges based on the target bridge needs continuous accumulation and improvement to enhance the efficiency and accuracy of project risk identification. It is understood that the more comprehensive the database, the more significant the effect.

[0090] S1012, Create a risk source identification table based on the statistically analyzed historical risk data.

[0091] Specifically, the main task of the risk identification phase is to collect potential risk factors, which typically involves reviewing documents, drawing on experience from similar projects, and consulting experts. The more comprehensive the risk identification and the broader the scope of risk coverage, the more accurate the assessment results will be. This embodiment collects relevant engineering data for the target bridge, including survey reports, hydrogeological materials, design and construction drawings, etc. Based on statistical data of construction safety accidents in similar bridge projects, a risk source identification table is created, focusing on sub-projects.

[0092] S1013, Based on the project data and the risk source identification table, the identification results are obtained by using empirical rules.

[0093] Specifically, industry experts in the field of bridge engineering were invited to conduct analysis based on project data and risk source identification tables, combined with their extensive bridge engineering experience, using sub-projects as identification units. By classifying and summarizing all the analysis results, relevant risk factors were identified and the identification results were obtained.

[0094] S1014, The identification results are sorted to form a risk source list table with sub-projects as summary units and sub-items as identification units, so as to confirm the overall risk analysis target of the target bridge.

[0095] Specifically, this embodiment is based on an improved expert survey method using accident statistics. Statistical data on safety risk accidents can supplement some risk sources that experts may overlook during the evaluation process due to insufficient experience or other reasons. Meanwhile, brainstorming can provide excellent predictions for bridge projects without similar engineering examples for reference. Combining these two methods fully leverages their advantages, resulting in more accurate and comprehensive risk identification.

[0096] S102, decompose the overall risk analysis objective to construct a layered risk assessment model; wherein, all risk factors in each layer of the risk assessment model constitute the assessment set of that layer;

[0097] Specifically, bridge construction risk analysis is a highly complex systems engineering project, involving numerous targets, bridge components, complex construction procedures, and many related intersections. Risk identification methods each have their own applicable scope and advantages and disadvantages. Using a single identification method is unlikely to completely and accurately identify bridge construction risks. Therefore, when selecting a risk identification method, a case-by-case analysis should be conducted, and the selected method must be adapted to the model and environment in which it is used. This embodiment adopts a comprehensive identification perspective based on relevance consistency for the preliminary identification work.

[0098] Furthermore, the specific steps of step S102 include:

[0099] S1021, The overall objective is decomposed into several sub-objectives using the hierarchical decomposition method to form the intermediate layer of the hierarchical analysis, and the intermediate layer is further decomposed into the next layer to form the third layer of the hierarchical analysis.

[0100] S1022, By analyzing the nature of each risk factor in each layer and the interrelationships between the risk factors, the membership relationships between the risk factors are identified;

[0101] S1023, Construct a risk assessment model corresponding to the overall risk analysis objective based on the aforementioned affiliation.

[0102] Specifically, the risk assessment model in this embodiment comprises three levels from top to bottom: Level 1, Level 2, and Level 3. Risk factors within the same level can influence risk factors in the next level, and are also influenced by risk factors in the previous level. Typically, Level 1 is the top level of the model structure, usually containing only one risk factor. The set of all risk factors in each level constitutes the risk factor evaluation set for that level. For example, in the process of bridge construction, bridge construction is considered Level 1 of the model structure. Due to the varying influencing conditions, these can be broadly categorized into four factors: construction technology, construction site management, natural disasters, and the quality of construction personnel. These four factors constitute Level 2. Natural disasters can be further divided into influencing factors such as fire, earthquake, and water, which constitute Level 3 of the structural hierarchy.

[0103] S103, the weight values ​​corresponding to the evaluation set are determined by the analytic hierarchy process, and the risk assessment level is determined based on the evaluation set and the weight values.

[0104] Specifically, the Analytic Hierarchy Process (AHP) can effectively solve the problem of multiple risk factors in bridge engineering systems. It breaks down risk factors into different levels and establishes a risk assessment model based on the interrelationships between risk factors to obtain the weight values ​​corresponding to each evaluation set. Then, based on the evaluation sets and their corresponding weight values, it classifies and determines the risk levels in the construction process of the target bridge.

[0105] Furthermore, the specific steps of step S103 include:

[0106] S1031, determine the relative importance of each risk factor in the evaluation set based on the pairwise comparison method, so as to construct a judgment matrix.

[0107] Specifically, the importance of this embodiment is mainly determined by pairwise comparisons using the quantitative method shown in the table below.

[0108]

[0109] Based on the above principles, by comparing each pair of n factors, we can obtain the comparison judgment matrix A, which is as follows:

[0110] .

[0111] S1032, the eigenvectors are obtained by solving the judgment matrix using a pre-defined rule;

[0112] Specifically, the prefabrication rule is: Ax = λ max x, where A is the judgment matrix, x is the unknown vector, and λ max These are the eigenvectors.

[0113] S1033, Based on the feature vector, the target feature vector corresponding to the largest feature vector is obtained, so as to obtain the weight value corresponding to the evaluation set.

[0114] Specifically, for the judgment matrix A, according to the formula Ax=λ max x, solve for the eigenvector λ max The eigenvector corresponding to the largest eigenvalue is then calculated, and this eigenvector is the weight value for judging the relative importance of matrix A. It should be noted that experts construct the judgment matrix based on their experience and the above principles, so their scoring results will be influenced by subjective factors, and the consistency of the judgment matrix cannot be guaranteed; therefore, further consistency checks on the judgment matrix are necessary.

[0115] S1034, Based on the evaluation set and the weight values, the risk assessment is divided into several risk levels to obtain the risk assessment level required for the overall risk analysis objective.

[0116] Specifically, in this embodiment, the risk assessment of bridges is divided into five levels: high risk, relatively high risk, medium risk, relatively low risk, and low risk. The corresponding assessment levels can be represented by V=(9, 7, 5, 3, 1), and levels between two levels are represented by 8, 6, 4, and 2. Specifically: Level 1 risk represents a high probability of risk and potential loss, requiring the highest level of attention and efforts to avoid its occurrence; Level 2 risk represents a moderate probability of risk but a relatively high potential loss, also requiring measures to reduce the risk; Level 3 risk represents a moderate probability of risk and potential loss, which can be mitigated through appropriate measures; Level 4 risk represents a low probability of risk and potential loss, requiring only enhanced prevention; and Level 5 risk represents a low probability of risk and potential loss, requiring only general inspection and protection.

[0117] S104, score each evaluation factor in the risk assessment level to establish a membership matrix.

[0118] Furthermore, the specific steps of step S104 include:

[0119] S1041, Generate an expert scoring table based on each evaluation factor in the risk assessment level.

[0120] Specifically, this embodiment formulates the "Expert Scoring Table" as shown in the table below based on each evaluation factor in the set risk assessment level.

[0121]

[0122] S1042, Based on the data filled in by experts in the collected expert rating table, calculate the mean table of membership degrees given by each expert to establish a membership function.

[0123] Specifically, the expert rating sheets collected in this embodiment are as shown in the table below.

[0124]

[0125] The mean values ​​of membership degrees given by each expert, calculated based on the table above, are shown in the table below.

[0126]

[0127] S1043, Establish a membership matrix for each evaluation factor based on the membership function.

[0128] In this embodiment, the membership matrix R Bi as follows:

[0129] .

[0130] Specifically, the membership matrix of the primary risk factors is obtained as follows:

[0131] The membership matrix for material and equipment risk B1 is as follows: ;

[0132] The membership matrix of natural disaster risk B2 is as follows: ;

[0133] The membership matrix for personnel risk B3 is as follows: ;

[0134] The membership matrix of design technology risk B4 is as follows: .

[0135] S105, the fuzzy evaluation matrix is ​​obtained by performing a comprehensive weighting method on the membership matrix.

[0136] Furthermore, the specific steps of step S105 include:

[0137] S1051, based on the comprehensive weighting method of relevance consistency, obtain the comprehensive weight of the evaluation set relative to its superior evaluation set.

[0138] Specifically, in this embodiment, the comprehensive weights are as follows:

[0139] W B1-C =(0.583,0.417) T W B2-C =(0.495,0.505) T W B3-C =(0.712,0.288) T ,

[0140] W B4-C =(0.682,0.318) T .

[0141] S1052, Based on the comprehensive weights, the fuzzy evaluation matrix corresponding to the evaluation set is obtained by calculating using a preset formula; wherein, the preset formula is: P i =W Bi ×R Bi In the formula, P i W represents the fuzzy evaluation matrix. Bi R represents the overall weight. Bi This represents the membership matrix.

[0142] S106, calculate the evaluation matrix corresponding to the evaluation set based on the fuzzy evaluation matrix, and obtain the risk level of the evaluation set based on the principle of maximum membership.

[0143] Specifically, based on the above calculations and the principle of maximum membership, it can be seen that: material and equipment risks are classified as "low risk"; natural disaster risks are classified as "low risk"; personnel risks are classified as "low risk"; design technology risks are classified as "medium risk"; and construction technology risks are classified as "relatively low risk".

[0144] In summary, determining the overall risk analysis objective of the target bridge based on historical risk data of similar bridges makes the risk identification results more accurate and comprehensive. The overall risk analysis objective is decomposed using the hierarchical decomposition method, and a risk assessment model is constructed based on the nature and correlation of each risk factor to ensure the comprehensiveness of the risk assessment. The analytic hierarchy process (AHP) is used to determine the weight values ​​corresponding to the evaluation set and thus determine the risk assessment level required for the overall risk analysis objective, ensuring the rationality of the analysis and evaluation. The fuzzy comprehensive evaluation method, employing mathematical means, scores each evaluation factor in the risk assessment level to establish a membership matrix, and calculates the fuzzy evaluation matrix to quantify the risk factors. Based on the fuzzy evaluation matrix, the judgment matrix corresponding to the evaluation set is calculated, and the risk level of the evaluation set is derived based on the principle of maximum membership. This achieves the goal of improving the comprehensiveness and accuracy of the assessment results.

[0145] Example 2

[0146] This embodiment provides a structural block diagram of a system corresponding to the method described in Embodiment 1. Figure 2 This is a structural block diagram of the bridge construction risk assessment system according to this embodiment, such as... Figure 2 As shown, the system includes:

[0147] The confirmation module 10 is used to confirm the overall risk analysis objective of the target bridge based on the historical risk data of similar bridge construction collected.

[0148] The construction module 20 is used to decompose the overall risk analysis objective to construct a layered risk assessment model; wherein, all risk factors in each layer of the risk assessment model constitute the assessment set of that layer;

[0149] The determination module 30 is used to determine the weight values ​​corresponding to the evaluation set using the analytic hierarchy process, and to determine the risk assessment level based on the evaluation set and the weight values.

[0150] The scoring module 40 is used to score each evaluation factor in the risk assessment level to establish a membership matrix;

[0151] The calculation module 50 is used to perform calculations on the membership matrix using a comprehensive weighting method to obtain a fuzzy evaluation matrix;

[0152] The evaluation module 60 is used to calculate the evaluation matrix corresponding to the evaluation set based on the fuzzy evaluation matrix, and to derive the risk level of the evaluation set based on the maximum membership principle.

[0153] Furthermore, the confirmation module 10 includes:

[0154] Collection unit 11 is used to search and collect historical risk data of bridge construction similar to the target bridge based on the project information of the target bridge;

[0155] Production unit 12 is used to produce a risk source identification table based on the statistically analyzed historical risk data;

[0156] Identification unit 13 is used to identify the project data and the risk source identification table using empirical rules to obtain the identification result;

[0157] The confirmation unit 14 is used to organize the identification results to form a risk source list with sub-projects as summary units and sub-items as identification units, so as to confirm the overall risk analysis target of the target bridge.

[0158] Furthermore, the building module 20 includes:

[0159] Decomposition unit 21 is used to decompose the total objective into several sub-objectives using a hierarchical decomposition method to form an intermediate layer of hierarchical analysis, and to further decompose the intermediate layer into a next layer to form a third layer of hierarchical analysis.

[0160] Analysis unit 22 is used to identify the membership relationships between risk factors by analyzing the nature of each risk factor in each layer and the interrelationships between the risk factors;

[0161] Construction unit 23 is used to construct a risk assessment model corresponding to the overall risk analysis objective based on the membership relationship.

[0162] Furthermore, the determining module 30 includes:

[0163] Construction unit 31 is used to determine the relative importance between each risk factor in the evaluation set based on the pairwise comparison method, so as to construct a judgment matrix;

[0164] Solving unit 32 is used to solve the judgment matrix using a pre-defined rule to obtain the eigenvector; wherein, the pre-defined rule is specifically: Ax = λ max x, where A is the judgment matrix, x is the unknown vector, and λ max For feature vectors;

[0165] The calculation unit 33 is used to calculate the target feature vector corresponding to the largest feature vector based on the feature vector, so as to obtain the weight value corresponding to the evaluation set;

[0166] The partitioning unit 34 is used to partition the risk assessment into several risk levels according to the evaluation set and the weight values, so as to obtain the risk assessment level required for the overall risk analysis objective.

[0167] Furthermore, the scoring module 40 includes:

[0168] Generation unit 41 is used to generate an expert scoring table based on each evaluation factor in the risk assessment level;

[0169] Calculation unit 42 is used to calculate the mean table of membership degrees given by each expert based on the data filled in by the experts in the collected expert rating table to establish a membership function;

[0170] Establishment unit 43 is used to establish a membership matrix for each evaluation factor based on the membership function.

[0171] Furthermore, the computing module 50 includes:

[0172] The acquisition unit 51 is used to acquire the comprehensive weight of one of the evaluation sets relative to its superior evaluation set using a comprehensive weighting method based on relevance consistency.

[0173] The calculation unit 52 is used to calculate the fuzzy evaluation matrix corresponding to the evaluation set based on the comprehensive weights using a preset formula; wherein, the preset formula is: P i =W Bi ×R Bi In the formula, P i W represents the fuzzy evaluation matrix. Bi R represents the overall weight. Bi This represents the membership matrix.

[0174] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0175] Example 3

[0176] Combination Figure 1 The bridge construction risk assessment method described can be implemented using electronic devices. Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to this embodiment.

[0177] The electronic device may include a processor 71 and a memory 72 storing computer program instructions.

[0178] Specifically, the processor 71 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0179] The memory 72 may include a large-capacity memory for data or instructions. For example, and not limitingly, the memory 72 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 72 may include removable or non-removable (or fixed) media. Where appropriate, the memory 72 may be internal or external to a data processing device. In a particular embodiment, the memory 72 is non-volatile memory. In a particular embodiment, the memory 72 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only ROM (PROM), an erasable read-only ROM (EPROM), an electrically erasable read-only ROM (EEPROM), an electrically alterable read-only ROM (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random Access Memory (FPMDRAM), Extended Data Out Dynamic Random Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0180] The memory 72 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 71.

[0181] The processor 71 reads and executes the computer program instructions stored in the memory 72 to implement the bridge construction risk assessment method of Embodiment 1 described above.

[0182] In some embodiments, the electronic device may further include a communication interface 73 and a bus 70. For example, Figure 3 As shown, the processor 71, memory 72, and communication interface 73 are connected through bus 70 and complete communication with each other.

[0183] The communication interface 73 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The communication interface 73 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0184] Bus 70 includes hardware, software, or both, that couples components of a device together. Bus 70 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 70 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 70 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0185] The electronic device can access the bridge construction risk assessment system and execute the bridge construction risk assessment method of this embodiment 1.

[0186] Furthermore, in conjunction with the bridge construction risk assessment method in Embodiment 1 above, this application embodiment can provide a storage medium for implementation. This storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement the bridge construction risk assessment method of Embodiment 1 above.

[0187] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing bridge construction risks, characterized in that, include: Based on the historical risk data collected from similar bridge construction projects, the overall risk analysis objective for the target bridge was determined. The overall risk analysis objective is decomposed to construct a layered risk assessment model; wherein, all risk factors in each layer of the risk assessment model constitute the assessment set of that layer; The weight values ​​corresponding to the evaluation set are determined by the analytic hierarchy process (AHP), and the risk assessment level is determined based on the evaluation set and the weight values. A membership matrix is ​​established by scoring each evaluation factor in the aforementioned risk assessment level. A fuzzy evaluation matrix is ​​obtained by performing calculations on the membership matrix using a comprehensive weighting method. The evaluation matrix corresponding to the evaluation set is calculated based on the fuzzy evaluation matrix, and the risk level of the evaluation set is obtained based on the maximum membership principle; The step of confirming the overall risk analysis objective of the target bridge based on collected historical risk data from similar bridge construction projects specifically includes: Based on the project information of the target bridge, search and collect historical risk data on the construction of bridges similar to the target bridge; A risk source identification table is created based on the historical risk data presented in the statistics; Based on the project data and the risk source identification table, the identification results were obtained by using empirical rules. The identification results are compiled into a risk source list with sub-projects as summary units and individual projects as identification units, in order to confirm the overall risk analysis objective of the target bridge; The step of decomposing the overall objective to construct a layered risk assessment model, wherein all risk factors in each layer of the risk assessment model constitute the assessment set of that layer, specifically includes: The overall objective is decomposed into several sub-objectives using a hierarchical decomposition method to form an intermediate layer of the hierarchical analysis, and the intermediate layer is further decomposed into a next layer to form the third layer of the hierarchical analysis. By analyzing the nature of each risk factor in each layer and the interrelationships between the risk factors, the membership relationships between the risk factors can be identified. Based on the aforementioned affiliation, a risk assessment model corresponding to the overall risk analysis objective is constructed. The specific steps of using the comprehensive weighting method to calculate the fuzzy evaluation matrix from the membership matrix include: The comprehensive weighting method based on relevance consistency obtains the comprehensive weight of one of the evaluation sets relative to its superior evaluation set. Based on the comprehensive weights, the fuzzy evaluation matrix corresponding to the evaluation set is obtained by using a preset formula; wherein, the preset formula is: P i =W Bi ×R Bi In the formula, P i W represents the fuzzy evaluation matrix. Bi R represents the overall weight. Bi This represents the membership matrix.

2. The bridge construction risk assessment method according to claim 1, characterized in that, The step of determining the weight values ​​corresponding to the evaluation set using the analytic hierarchy process (AHP) and determining the risk assessment level based on the evaluation set and the weight values ​​specifically includes: The relative importance of each risk factor in the evaluation set is determined based on a pairwise comparison method in order to construct a judgment matrix; The eigenvectors are obtained by solving the judgment matrix using a pre-defined rule. Based on the feature vector, the target feature vector corresponding to the largest feature vector is obtained, so as to obtain the weight value corresponding to the evaluation set; The risk assessment is divided into several risk levels based on the evaluation set and the weight values ​​to obtain the risk assessment level required for the overall risk analysis objective.

3. The bridge construction risk assessment method according to claim 2, characterized in that, The prefabrication rule is specifically: Ax = λ max x, where A is the judgment matrix, x is the unknown vector, and λ max These are the eigenvectors.

4. The bridge construction risk assessment method according to claim 1, characterized in that, The step of scoring each evaluation factor in the risk assessment level to establish a membership matrix specifically includes: An expert scoring table is generated based on each evaluation factor in the aforementioned risk assessment level; Based on the data filled in by experts in the collected expert rating table, a mean table of membership degrees given by each expert is calculated to establish a membership function; A membership matrix is ​​established for each evaluation factor based on the membership function.

5. A bridge construction risk assessment system, characterized in that, include: The confirmation module is used to confirm the overall risk analysis objectives of the target bridge based on historical risk data collected from similar bridge construction projects. The confirmation module is specifically used to: search and collect historical risk data of bridge construction similar to the target bridge based on the project information of the target bridge; A risk source identification table is created based on the historical risk data presented in the statistics; Based on the project data and the risk source identification table, the identification results were obtained by using empirical rules. The identification results are compiled into a risk source list with sub-projects as summary units and individual projects as identification units, in order to confirm the overall risk analysis objective of the target bridge; A construction module is used to decompose the overall risk analysis objective to construct a layered risk assessment model; wherein, all risk factors in each layer of the risk assessment model constitute the assessment set of that layer; The construction module is specifically used to: decompose the total objective into several sub-objectives using a hierarchical decomposition method to form an intermediate layer of hierarchical analysis, and further decompose the intermediate layer into a next layer to form a third layer of hierarchical analysis. By analyzing the nature of each risk factor in each layer and the interrelationships between the risk factors, the membership relationships between the risk factors can be identified. Based on the aforementioned affiliation, a risk assessment model corresponding to the overall risk analysis objective is constructed. The determination module is used to determine the weight values ​​corresponding to the evaluation set using the analytic hierarchy process (AHP), and to determine the risk assessment level based on the evaluation set and the weight values. The scoring module is used to score each evaluation factor in the risk assessment level to establish a membership matrix; The calculation module is used to perform calculations on the membership matrix using a comprehensive weighting method to obtain a fuzzy evaluation matrix; The computation module is specifically used to: obtain the comprehensive weight of one of the evaluation sets relative to its superior evaluation set using a comprehensive weighting method based on relevance consistency. Based on the comprehensive weights, the fuzzy evaluation matrix corresponding to the evaluation set is obtained by using a preset formula; wherein, the preset formula is: P i =W Bi ×R Bi In the formula, P i W represents the fuzzy evaluation matrix. Bi R represents the overall weight. Bi Represents the membership matrix; The evaluation module is used to calculate the evaluation matrix corresponding to the evaluation set based on the fuzzy evaluation matrix, and to derive the risk level of the evaluation set based on the principle of maximum membership.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the bridge construction risk assessment method as described in any one of claims 1 to 4.

7. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the bridge construction risk assessment method as described in any one of claims 1 to 4.

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