CN116307772A - Bridge construction risk assessment method, system, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a bridge construction risk assessment method, a system, electronic equipment and a storage medium, and belongs to the technical field of bridge construction safety; the method comprises the steps of confirming a risk analysis total target of a target bridge; decomposing the risk analysis total target to construct a risk evaluation model of the layered structure; adopting an analytic hierarchy process to determine a weight value corresponding to the evaluation set, and determining a risk evaluation grade based on the evaluation set and the weight value; scoring each evaluation factor in the risk evaluation level to establish a membership matrix; calculating the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix; and 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 principle. The method adopts a hierarchical decomposition and analysis method to determine an evaluation set formed by the risk factors and the corresponding weights thereof, and adopts fuzzy comprehensive evaluation and a method of mathematical means to quantify the risk factors, thereby improving the comprehensiveness and the accuracy of the evaluation result.
Description
Technical Field
The invention belongs to the technical field of bridge construction safety, and particularly relates to a bridge construction risk assessment method, a system, electronic equipment and a storage medium.
Background
In recent years, with the acceleration of the economic development speed, the infrastructure construction projects are increasing, the road and bridge facilities are also increasing gradually, and the economic development process is accelerated to a great extent. The functions of roads and bridges in the connected cities and the connected areas are increasingly displayed and become an indispensable part of infrastructure construction; the requirements on the form and the function of the road and the bridge are also higher and higher, so that the development of the road and bridge construction technology is continuously promoted. Meanwhile, in the bridge construction process, various fault risks are easy to occur to the bridge, such as cracks are easy to occur to the bridge frequently in the bridge construction process, rust is easy to occur to bridge reinforcing steel bars, a bridge pavement layer is easy to loose and fall off, such as the risk of pile foundation fault data information occurs, cracks occur to the stirrup positions due to the appearance of marks on pier columns, and the technical problem to be solved is still urgent in improving the road and bridge engineering construction capability and improving the construction risk assessment.
Because the construction risk of the road and bridge is different from other risks, the construction process is special, the construction risk is difficult to predict, and the loss and the consequences caused by construction are serious. The risk factors and the structural safety of roads and bridges have complicated relations, and the risk factors and the structural safety of roads and bridges cannot be qualitatively expressed by using a very clear functional relation. Based on the above, the expert has long been in risk analysis of roads and bridges, and has also gained a lot of experience. The common risk assessment methods in the prior art include expert scoring, monte Carlo, sensitivity analysis, analytic hierarchy process, fuzzy comprehensive evaluation and the like. However, only a single evaluation method can be used for purely qualitatively or quantitatively analyzing risks, and the risk evaluation capability is improved to a certain extent compared with the traditional manual statistics method, empirical value method or bridge construction monitoring method, but the comprehensiveness and accuracy of the evaluation result are still lower.
Therefore, it is important to design an effective risk assessment method for safety assessment of road and bridge construction process.
Disclosure of Invention
In order to solve the technical problems, the invention provides a bridge construction risk assessment method, a system, electronic equipment and a storage medium, wherein an assessment set formed by risk factors and corresponding weights thereof are determined by adopting a hierarchical decomposition and analysis method, and the risk factors are quantified by adopting a fuzzy comprehensive assessment method of mathematical means so as to improve the accuracy of an assessment result and provide guidance for actual safety construction.
In a first aspect, an embodiment of the present application provides a bridge construction risk assessment method, including:
according to the collected historical risk data of similar bridge construction, confirming a risk analysis total target of the target bridge;
decomposing the risk analysis total target to construct a risk evaluation model of the layered structure; wherein, all risk factors of each layer in the risk evaluation model form an evaluation set of the layer;
adopting an analytic hierarchy process to determine a weight value corresponding to the evaluation set, and determining a risk evaluation grade based on the evaluation set and the weight value;
scoring each evaluation factor in the risk evaluation level to establish a membership matrix;
calculating the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix;
and 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 a maximum membership principle.
Preferably, the step of determining the total target of the risk analysis of the target bridge according to the collected historical risk data of the similar bridge construction specifically includes:
searching and collecting historical risk data of bridge construction similar to a target bridge according to project data of the target bridge;
manufacturing a risk source identification table according to the counted historical risk data;
based on the project data and the risk source identification table, adopting a rule of thumb to identify so as to obtain an identification result;
and arranging the identification result to form a risk source list table taking the sub engineering as a summarizing unit and the sub engineering as an identification unit so as to confirm the risk analysis total target of the target bridge.
Preferably, the decomposing the total target to construct a risk assessment model of the layered structure; wherein, the step of forming the evaluation set of each layer of all risk factors in the risk evaluation model specifically comprises the following steps:
decomposing the total target into a plurality of sub-targets by adopting a hierarchical decomposition method to form a hierarchical analysis middle layer, and decomposing the middle layer into a next layer to form a hierarchical analysis third layer;
finding out the membership between the risk factors by analyzing the properties of each risk factor in each layer and the interrelationships among the risk factors;
and constructing a risk evaluation model corresponding to the risk analysis total target based on the membership.
Preferably, the step of determining the weight value corresponding to the evaluation set by using an analytic hierarchy process and determining the risk evaluation level based on the evaluation set and the weight value specifically includes:
determining the relative importance between each risk factor in the evaluation set based on a pairwise comparison method to construct a judgment matrix;
solving the judgment matrix by adopting a prefabrication rule to obtain a feature vector;
solving a target feature vector corresponding to the maximum feature vector based on the feature vector to obtain a weight value corresponding to the evaluation set;
and dividing the risk evaluation into a plurality of risk grades according to the evaluation set and the weight value so as to obtain the risk evaluation grade required by the risk analysis total target.
Preferably, the prefabrication rule is specifically: ax=λ max x, wherein A is a judgment matrix, x is an unknown vector, lambda max Is a feature vector.
Preferably, the step of scoring each evaluation factor in the risk evaluation level to establish a membership matrix specifically includes:
generating an expert scoring table according to each evaluation factor in the risk evaluation level;
calculating a mean value table of membership degrees given by each expert based on the collected expert filled data in the expert scoring table so as to establish a membership function;
and establishing a membership matrix for each evaluation factor according to the membership function.
Preferably, the step of calculating the membership matrix by using a comprehensive weighting method to obtain a fuzzy evaluation matrix specifically includes:
acquiring the comprehensive weight of one evaluation set relative to the evaluation set of the upper layer by a comprehensive weighting method based on the correlation consistency;
based on the comprehensive weight, a fuzzy evaluation matrix corresponding to the evaluation set is obtained by adopting a preset formula operation; wherein, the preset formula is: p (P) i =W Bi ×R Bi Wherein P is i Represents a fuzzy evaluation matrix, W Bi Represents the comprehensive weight, R Bi Representing the membership matrix.
In a second aspect, an embodiment of the present application provides a bridge construction risk assessment method, including:
the confirming module is used for confirming a risk analysis total target of the target bridge according to the collected historical risk data of the similar bridge construction;
the building module is used for decomposing the risk analysis total target to build a risk evaluation model of the layered structure; wherein, all risk factors of each layer in the risk evaluation model form an evaluation set of the layer;
the determining module is used for determining a weight value corresponding to the evaluation set by adopting a analytic hierarchy process and determining a risk evaluation grade based on the evaluation set and the weight value;
the scoring module is used for scoring each evaluation factor in the risk evaluation level to establish a membership matrix;
the operation module is used for carrying out operation on the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix;
and the judging module is used for calculating a judging 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 principle.
Preferably, the confirmation module includes:
the collecting unit is used for searching and collecting historical risk data of bridge construction similar to the target bridge according to project data of the target bridge;
the manufacturing unit is used for manufacturing a risk source identification table according to the counted historical risk data;
the identification unit is used for identifying the project data and the risk source identification table by adopting a rule of thumb to obtain an identification result;
and the confirming unit is used for arranging the identification result to form a risk source list table which takes the subsection engineering as a summarizing unit and the subsection engineering as an identification unit so as to confirm the risk analysis total target of the target bridge.
Preferably, the construction module includes:
the decomposition unit is used for decomposing the total target into a plurality of sub-targets by adopting a hierarchical decomposition method to form a hierarchical analysis middle layer, and decomposing the middle layer into a next layer to form a hierarchical analysis third layer;
the analysis unit is used for finding out the membership between the risk factors by analyzing the properties of each risk factor in each layer and the correlation between the risk factors;
and the construction unit is used for constructing a risk evaluation model corresponding to the risk analysis total target based on the membership.
Preferably, the determining module includes:
a construction unit for determining the relative importance between each risk factor in the evaluation set based on a pairwise comparison method to construct a judgment matrix;
the solving unit is used for solving the judging matrix by adopting a prefabrication rule to obtain a feature vector; wherein, the prefabrication rule specifically comprises: ax=λ max x, wherein A is a judgment matrix, x is an unknown vector, lambda max Is a feature vector;
a solving unit, configured to solve, based on the feature vector, a target feature vector corresponding to a maximum feature vector, so as to obtain a weight value corresponding to the evaluation set;
the dividing unit is used for dividing the risk evaluation into a plurality of risk grades according to the evaluation set and the weight value so as to obtain the risk evaluation grade required by the risk analysis total target.
Preferably, the scoring module includes:
the generation unit is used for generating an expert scoring table according to each evaluation factor in the risk evaluation level;
the calculating unit is used for calculating a mean value table of membership degrees given by each expert based on the collected expert filled data in the expert scoring table so as to establish a membership function;
and the establishing unit is used for establishing a membership matrix for each evaluation factor according to the membership function.
Preferably, the operation module includes:
the acquisition unit is used for acquiring the comprehensive weight of one evaluation set relative to the evaluation set of the upper layer based on the comprehensive weighting method of the correlation consistency;
an arithmetic unit for based on the healdCombining weights, and calculating by adopting a preset formula to obtain a fuzzy evaluation matrix corresponding to the evaluation set; wherein, the preset formula is: p (P) i =W Bi ×R Bi Wherein P is i Represents a fuzzy evaluation matrix, W Bi Represents the comprehensive weight, R Bi Representing the membership matrix.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the bridge construction risk assessment method according to the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present application provide a storage medium having stored thereon a computer program, which when executed by a processor, implements the bridge construction risk assessment method according to the first aspect.
Compared with the prior art, the bridge construction risk assessment method, the bridge construction risk assessment system, the electronic equipment and the storage medium provided by the application have the advantages that the risk analysis total target of the target bridge is determined according to the historical risk data of the similar bridge, so that the risk identification result is more accurate and comprehensive; decomposing a risk analysis total target through a hierarchical decomposition method, finding out membership relations based on the properties and the correlation relations of each risk factor, and constructing a risk evaluation model so as to ensure the comprehensiveness of risk evaluation; adopting an analytic hierarchy process to determine a weight value corresponding to the evaluation set, and further determining a risk evaluation grade required by a risk analysis total target so as to ensure the rationality of analysis evaluation; scoring each evaluation factor in the risk evaluation grade by using a fuzzy comprehensive evaluation method of mathematical means to establish a membership matrix, and calculating to obtain a fuzzy evaluation matrix so as to quantify the risk factors; and calculating a judgment matrix corresponding to the evaluation set according to the fuzzy evaluation matrix, and obtaining the risk level of the evaluation set based on the maximum membership principle. Through the steps, an evaluation set formed by the risk factors and the corresponding weights thereof are determined by adopting a hierarchical decomposition and analysis method, and the risk factors are quantified by adopting a fuzzy comprehensive evaluation method of a mathematical means, so that the comprehensiveness and the accuracy of an evaluation result are improved, and guidance is provided for actual safety construction.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a bridge construction risk assessment method provided in embodiment 1 of the present invention;
fig. 2 is a block diagram of a bridge construction risk assessment system corresponding to the method of embodiment 1 provided in embodiment 2 of the present invention;
fig. 3 is a schematic hardware structure of an electronic device according to embodiment 3 of the present invention.
Reference numerals illustrate:
10-confirmation module, 11-collection unit, 12-making unit, 13-identifying unit, 14-confirmation unit;
a 20-construction module, a 21-decomposition unit, a 22-analysis unit, a 23-construction unit;
30-determining module, 31-constructing unit, 32-solving unit, 33-solving unit and 34-dividing unit;
40-scoring module, 41-generating unit, 42-calculating unit, 43-establishing unit;
a 50-operation module, a 51-acquisition unit and a 52-operation unit;
60-judging module;
70-bus, 71-processor, 72-memory, 73-communication interface.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied 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 thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Example 1
Specifically, fig. 1 is a schematic flow chart of a bridge construction risk assessment method according to the present embodiment.
As shown in fig. 1, the bridge construction risk assessment method of the present embodiment includes the following steps:
s101, confirming a risk analysis total target of the target bridge according to the collected historical risk data of similar bridge construction.
In particular, risk assessment is mainly based on qualitative, mainly due to the lack of risk accident statistics. The absence of the underlying database is the most important reason for the minimal use of quantitative risk assessment, and the risk accident statistics are the most important and the most important part of the underlying database. Risk accidents possibly occurring in the bridge construction process are identified through letters and inquiry modes, but incomplete and inaccurate identification results can be caused by subjective factors of experts and limited cognition of the risk accidents. Aiming at the statistics of bridge accidents, the embodiment collects the risk accident data generated by similar projects and similar branches, gathers and statistics to obtain relatively complete risk accident data, and the result is more objective and comprehensive, so that the defects of expert investigation methods can be effectively avoided.
Further, the specific steps of step S101 include:
and S1011, searching and collecting historical risk data of bridge construction similar to the target bridge according to project data of the target bridge.
Specifically, due to limited resources and channels, the embodiment obtains safety accidents occurring during construction of various bridges by referring to documents, journals, news reports, government websites and the like. Historical risk data of similar bridges based on target bridges are required to be continuously accumulated and perfected so as to improve efficiency and accuracy of project risk identification. It can be appreciated that the more complete the database, the more pronounced the effect.
S1012, manufacturing a risk source identification table according to the counted historical risk data.
Specifically, the risk identification stage mainly aims at collecting risk factors possibly generated, and generally can be performed by means of consulting data, borrowing similar project experience, querying an expert and the like. The more complete the risk is identified, the wider the risk is included, and the more accurate the result of the assessment. The engineering related data of the target bridge collected by the embodiment comprises a survey report, hydrogeologic materials, design and construction drawings and the like. And (3) according to the statistical data of construction safety accidents of similar engineering bridges, making a risk source identification table mainly based on the itemized engineering.
S1013, based on the project data and the risk source identification table, adopting a rule of thumb to conduct identification to obtain an identification result.
Specifically, industry experts in the bridge field are invited, analysis is carried out by combining with the bridge experience which is abundant by the industry experts according to project data and risk source identification tables, analysis results are obtained by taking the project as an identification unit, all analysis results are classified and summarized, relevant risk factors are identified, and the identification results are summarized and obtained.
S1014, the identification result is arranged to form a risk source list table which takes the sub engineering as a summarizing unit and the sub engineering as an identification unit, so as to confirm the total risk analysis target of the target bridge.
Specifically, according to the expert investigation improvement method based on accident statistics, the statistical data of the safety risk accidents can be used for compensating a part of risk sources which are ignored by experts due to insufficient experience and other reasons in the judging process; and meanwhile, the brain storm method can well predict bridge projects which can not be referred to by similar engineering. The combination of the two can give full play to the advantages of the two, so that the risk identification result is more accurate and comprehensive.
S102, decomposing the risk analysis total target to construct a risk evaluation model of the layered structure; wherein, all risk factors of each layer in the risk evaluation model form an evaluation set of the layer;
specifically, bridge construction risk analysis is a very complex system engineering, and has many targets, many constituent members of the bridge, complex construction procedures and many joints related to the construction procedures. The risk identification method has respective application range and advantages and disadvantages, and the bridge construction risk is difficult to be completely and accurately identified by adopting a single identification method, so that when the risk identification method is selected, specific problems are required to be specifically analyzed, and the selected method is required to be suitable for a model and environment using the method. In this embodiment, the early-stage recognition operation is performed by adopting a comprehensive recognition angle based on correlation consistency.
Further, the specific steps of step S102 include:
s1021, decomposing the total target into a plurality of sub-targets by a hierarchical decomposition method to form an intermediate layer of the hierarchical analysis, and decomposing the intermediate layer into a next layer to form a third layer of the hierarchical analysis.
S1022, finding out the membership between the risk factors by analyzing the properties of each risk factor in each layer and the correlation between the risk factors;
s1023, constructing a risk evaluation model corresponding to the risk analysis total target based on the membership.
Specifically, the risk evaluation model in this embodiment includes three layers from top to bottom, namely layer 1, layer 2, and layer 3. The risk factors in the same hierarchy may govern the risk factors of the next hierarchy, while also being governed by the risk factors in the previous hierarchy. Layer 1 is typically the uppermost layer of the model structure, which often has only 1 risk factor. The set of all risk factors for each layer constitutes the risk factor evaluation set for that layer. Such as: in the bridge construction process, taking the bridge construction as a layer 1 of a model structure; because the affected conditions are different, the method can be roughly divided into 4 factors of construction process, construction site management, natural disasters and constructors, wherein the 4 factors are used as layer 2, the natural disasters can be further divided into fire, earthquake, water and other affected factors, and the factors are used as layer 3 of a structural hierarchy.
S103, determining a weight value corresponding to the evaluation set by adopting a analytic hierarchy process, and determining a risk evaluation grade based on the evaluation set and the weight value.
Specifically, the analytic hierarchy process can effectively solve the problem of multiple risk factors in the bridge engineering system, split the risk factors into different grades, obtain weight values corresponding to all evaluation sets according to risk evaluation models established by interrelationships among the risk factors, and then grade and determine the risks in the target bridge construction process according to the evaluation sets and the weight values corresponding to the evaluation sets.
Further, the specific steps of step S103 include:
and S1031, determining the relative importance among each risk factor in the evaluation set based on a pairwise comparison method to construct a judgment matrix.
Specifically, the importance of the present embodiment is mainly formulated by a pairwise comparison method using the following quantitative method.
According to the principle, n factors are compared with each other two by two, so that a comparison judgment matrix A can be obtained, and the specific judgment matrix A is as follows:
s1032, solving the judgment matrix by adopting a prefabrication rule to obtain a feature vector;
wherein, the prefabrication rule specifically comprises: ax=λ max x, wherein A is a judgment matrix, x is an unknown vector, lambda max Is a feature vector.
S1033, solving a target feature vector corresponding to the maximum feature vector based on the feature vector to obtain a weight value corresponding to the evaluation set.
Specifically, for the judgment matrix a, according to the formula ax=λ max x, solving eigenvector lambda max And obtaining a feature vector corresponding to the maximum feature value, wherein the feature vector is a weight value for judging the relative importance of the matrix A. It should be noted that, the expert constructs the judgment matrix according to the above principle according to his own experience, so the scoring result is affected by subjective factors, and the consistency of the judgment matrix cannot be ensured; further consistency checks of the decision matrix are therefore required.
S1034, dividing the risk evaluation into a plurality of risk grades according to the evaluation set and the weight value to obtain the risk evaluation grade required by the risk analysis total target.
Specifically, in this embodiment, risk evaluation of the bridge is classified into 5 grades, which are respectively: the high risk, higher risk, medium risk, lower risk, low risk, the rating corresponding to this can be represented by v= (9,7,5,3,1), the ratings between 2 ratings being represented by 8, 6, 4, 2. Wherein: the meaning of the risk representation is that the risk probability and the risk loss are relatively large, the highest degree of importance should be given to the risk, and the risk is avoided as much as possible; the meaning of the second-class risk representation is that although the risk probability is general, the risk loss is relatively large, and measures are taken to reduce the occurrence of the risk loss; the meaning of the three types of risks is that the risk probability and the risk loss are all general, and the risks can be reduced by adopting some corresponding means; the significance of the four types of representatives is that the risk probability and the risk loss are smaller, and only the protection needs to be enhanced; the five types of risk probability and risk loss are smaller, and only general checking protection is needed.
And S104, scoring each evaluation factor in the risk evaluation level to establish a membership matrix.
Further, the specific steps of step S104 include:
s1041, generating an expert scoring table according to each evaluation factor in the risk evaluation level.
Specifically, in the present embodiment, the "expert score table" shown in the following table is formulated according to each evaluation factor in the set risk evaluation level.
S1042, calculating the average value table of the membership degree given by each expert based on the collected expert filled data in the expert scoring table to establish the membership function.
Specifically, the collected expert score table in the present embodiment is such as shown in the following table.
The average value table of the membership degrees given by each expert is calculated according to the above table and is shown in the following table.
S1043, establishing a membership matrix for each evaluation factor according to the membership function.
Wherein, the present realityMembership matrix R of the examples Bi The following are provided:
specifically, the membership matrix for the primary risk factor is obtained as follows:
the membership matrix of the material and equipment risk B1 is as follows:
；
the membership matrix of the natural disaster risk B2 is as follows:
；
the membership matrix of the personnel risk B3 is:
；
the membership matrix of the design technical risk B4 is as follows:
。
s105, calculating the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix.
Further, the specific steps of step S105 include:
s1051, acquiring the comprehensive weight of one evaluation set relative to the evaluation set of the upper layer based on the comprehensive weighting method of the correlation consistency.
Specifically, in this embodiment, each comprehensive weight is as follows:
W B1-C =(0.583,0.417) T ,W B2-C =(0.495,0.505) T ,W B3-C =(0.712,0.288) T ,
W B4-C =(0.682,0.318) T 。
s1052, based on the comprehensive weight, calculating by adopting a preset formula to obtain a fuzzy evaluation matrix corresponding to the evaluation set; wherein, the preset formula is: p (P) i =W Bi ×R Bi Wherein P is i Represents a fuzzy evaluation matrix, W Bi Represents the comprehensive weight, R Bi Representing the membership matrix.
S106, calculating a judgment matrix corresponding to the evaluation set according to the fuzzy evaluation matrix, and obtaining the risk level of the evaluation set based on a maximum membership principle.
In particular, from the above calculations and from the principle of maximum membership, it can be seen that: the material and equipment risks belong to the category of "low risk"; natural disaster risk belongs to "low risk"; personnel risk belongs to "low risk"; design technology risk belongs to the "medium risk"; construction technology risks belong to the "lower risk".
In summary, determining the risk analysis total target of the target bridge according to the historical risk data of the similar bridge enables the risk recognition result to be more accurate and comprehensive; decomposing a risk analysis total target through a hierarchical decomposition method, finding out membership relations based on the properties and the correlation relations of each risk factor, and constructing a risk evaluation model so as to ensure the comprehensiveness of risk evaluation; adopting an analytic hierarchy process to determine a weight value corresponding to the evaluation set, and further determining a risk evaluation grade required by a risk analysis total target so as to ensure the rationality of analysis evaluation; scoring each evaluation factor in the risk evaluation grade by using a fuzzy comprehensive evaluation method of mathematical means to establish a membership matrix, and calculating to obtain a fuzzy evaluation matrix so as to quantify the risk factors; calculating a judgment matrix corresponding to the evaluation set according to the fuzzy evaluation matrix, and obtaining the risk level of the evaluation set based on a maximum membership principle; thereby achieving the purpose of improving the comprehensiveness and the accuracy of the evaluation result.
Example 2
This embodiment provides a block diagram of a system corresponding to the method described in embodiment 1. Fig. 2 is a block diagram of the construction risk assessment system for a bridge according to the present embodiment, and as shown in fig. 2, the system includes:
a confirmation module 10, configured to confirm a risk analysis total target of the target bridge according to the collected historical risk data of the similar bridge construction;
a construction module 20 for decomposing the risk analysis total target to construct a risk assessment model of the layered structure; wherein, all risk factors of each layer in the risk evaluation model form an evaluation set of the layer;
the determining module 30 is configured to determine a weight value corresponding to the evaluation set by using a analytic hierarchy process, and determine a risk evaluation level based on the evaluation set and the weight value;
a scoring module 40, configured to score each evaluation factor in the risk evaluation level to establish a membership matrix;
the operation module 50 is used for carrying out operation on the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix;
and the evaluation module 60 is configured to calculate an evaluation matrix corresponding to the evaluation set according to the fuzzy evaluation matrix, and obtain a risk level of the evaluation set based on a maximum membership rule.
Further, the confirmation module 10 includes:
a collecting unit 11 for searching and collecting historical risk data of bridge construction similar to a target bridge according to project data of the target bridge;
a making unit 12, configured to make a risk source identification table according to the counted historical risk data;
the identifying unit 13 is configured to identify by using a rule of thumb based on the project data and the risk source identification table to obtain an identification result;
and the confirmation unit 14 is used for arranging the identification results to form a risk source list table taking the subsection engineering as a summarizing unit and the subsection engineering as an identification unit so as to confirm the total risk analysis target of the target bridge.
Further, the construction module 20 includes:
a decomposition unit 21, configured to decompose the total target into a plurality of sub-targets by using a hierarchical decomposition method to form an intermediate layer of the hierarchical analysis, and decompose the intermediate layer into a next layer to form a third layer of the hierarchical analysis;
an analysis unit 22, configured to find membership relationships between risk factors by analyzing properties of each risk factor in each layer and correlations between the risk factors;
a construction unit 23, configured to construct a risk evaluation model corresponding to the risk analysis total target based on the membership.
Further, the determining module 30 includes:
a construction unit 31 for determining the relative importance between each risk factor in the evaluation set based on a pairwise comparison method to construct a judgment matrix;
a solving unit 32, configured to solve the judgment matrix by using a prefabrication rule to obtain a feature vector; wherein, the prefabrication rule specifically comprises: ax=λ max x, wherein A is a judgment matrix, x is an unknown vector, lambda max Is a feature vector;
a calculating unit 33, configured to calculate a target feature vector corresponding to a maximum feature vector based on the feature vector, so as to obtain a weight value corresponding to the evaluation set;
the dividing unit 34 is configured to divide the risk assessment into a plurality of risk levels according to the assessment set and the weight value, so as to obtain a risk assessment level required by the risk analysis overall objective.
Further, the scoring module 40 includes:
a generating unit 41, configured to generate an expert scoring table according to each evaluation factor in the risk evaluation level;
a calculating unit 42, configured to calculate a mean value table of membership degrees given by each expert based on the collected expert filled data in the expert scoring table, so as to establish a membership function;
and the establishing unit 43 is configured to establish a membership matrix for each evaluation factor according to the membership function.
Further, the operation module 50 includes:
an obtaining unit 51, configured to obtain a comprehensive weight of one evaluation set relative to a previous level evaluation set based on a comprehensive weighting method of correlation consistency;
an operation unit 52 for obtaining the fuzzy corresponding to the evaluation set by adopting a preset formula operation based on the comprehensive weightEvaluating the matrix; wherein, the preset formula is: p (P) i =W Bi ×R Bi Wherein P is i Represents a fuzzy evaluation matrix, W Bi Represents the comprehensive weight, R Bi Representing the membership matrix.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Example 3
The bridge construction risk assessment method described in connection with fig. 1 may be implemented by an electronic device. Fig. 3 is a schematic diagram of the hardware structure of the electronic device according to the present embodiment.
The electronic device may include a processor 71 and a memory 72 storing computer program instructions.
In particular, the processor 71 may include a Central Processing Unit (CPU), or an application specific integrated circuit (ApplicationSpecific Integrated Circuit, abbreviated as ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
The processor 71 implements the bridge construction risk assessment method of embodiment 1 described above by reading and executing the computer program instructions stored in the memory 72.
In some of these embodiments, the electronic device may also include a communication interface 73 and a bus 70. As shown in fig. 3, the processor 71, the memory 72, and the communication interface 73 are connected to each other through the bus 70 and perform communication with each other.
The communication interface 73 is used to enable communication between various modules, devices, units and/or units in embodiments of the application. Communication interface 73 may also enable communication with other components such as: and the external equipment, the image/data acquisition equipment, the database, the external storage, the image/data processing workstation and the like are used for data communication.
The electronic device may acquire the bridge construction risk assessment system, and execute the bridge construction risk assessment method of this embodiment 1.
In addition, in combination with the bridge construction risk assessment method in the above embodiment 1, the embodiment of the application may provide a storage medium for implementation. The storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement the bridge construction risk assessment method of embodiment 1 described above.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The bridge construction risk assessment method is characterized by comprising the following steps of:
according to the collected historical risk data of similar bridge construction, confirming a risk analysis total target of the target bridge;
decomposing the risk analysis total target to construct a risk evaluation model of the layered structure; wherein, all risk factors of each layer in the risk evaluation model form an evaluation set of the layer;
adopting an analytic hierarchy process to determine a weight value corresponding to the evaluation set, and determining a risk evaluation grade based on the evaluation set and the weight value;
scoring each evaluation factor in the risk evaluation level to establish a membership matrix;
calculating the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix;
and 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 a maximum membership principle.
2. The bridge construction risk assessment method according to claim 1, wherein the step of confirming the risk analysis total target of the target bridge based on the collected historical risk data of the similar bridge construction specifically comprises:
searching and collecting historical risk data of bridge construction similar to a target bridge according to project data of the target bridge;
manufacturing a risk source identification table according to the counted historical risk data;
based on the project data and the risk source identification table, adopting a rule of thumb to identify so as to obtain an identification result;
and arranging the identification result to form a risk source list table taking the sub engineering as a summarizing unit and the sub engineering as an identification unit so as to confirm the risk analysis total target of the target bridge.
3. The bridge construction risk assessment method according to claim 1, wherein the decomposing the total target to construct a risk assessment model of a layered structure; wherein, the step of forming the evaluation set of each layer of all risk factors in the risk evaluation model specifically comprises the following steps:
decomposing the total target into a plurality of sub-targets by adopting a hierarchical decomposition method to form a hierarchical analysis middle layer, and decomposing the middle layer into a next layer to form a hierarchical analysis third layer;
finding out the membership between the risk factors by analyzing the properties of each risk factor in each layer and the interrelationships among the risk factors;
and constructing a risk evaluation model corresponding to the risk analysis total target based on the membership.
4. The bridge construction risk assessment method according to claim 1, wherein the step of determining the weight value corresponding to the assessment set by using a hierarchical analysis method and determining the risk assessment level based on the assessment set and the weight value specifically comprises:
determining the relative importance between each risk factor in the evaluation set based on a pairwise comparison method to construct a judgment matrix;
solving the judgment matrix by adopting a prefabrication rule to obtain a feature vector;
solving a target feature vector corresponding to the maximum feature vector based on the feature vector to obtain a weight value corresponding to the evaluation set;
and dividing the risk evaluation into a plurality of risk grades according to the evaluation set and the weight value so as to obtain the risk evaluation grade required by the risk analysis total target.
5. The bridge construction risk assessment method according to claim 4, wherein the prefabrication rule is specifically: ax=λ max x, wherein A is a judgment matrix, x is an unknown vector, lambda max Is a feature vector.
6. The bridge construction risk assessment method according to claim 1, wherein the step of scoring each evaluation factor in the risk evaluation level to establish a membership matrix specifically comprises:
generating an expert scoring table according to each evaluation factor in the risk evaluation level;
calculating a mean value table of membership degrees given by each expert based on the collected expert filled data in the expert scoring table so as to establish a membership function;
and establishing a membership matrix for each evaluation factor according to the membership function.
7. The bridge construction risk assessment method according to claim 1, wherein the step of calculating the membership matrix by using a comprehensive weighting method to obtain a fuzzy evaluation matrix specifically comprises:
acquiring the comprehensive weight of one evaluation set relative to the evaluation set of the upper layer by a comprehensive weighting method based on the correlation consistency;
based on the comprehensive weight, a fuzzy evaluation matrix corresponding to the evaluation set is obtained by adopting a preset formula operation; wherein, the preset formula is: p (P) i =W Bi ×R Bi Wherein P is i Represents a fuzzy evaluation matrix, W Bi Represents the comprehensive weight, R Bi Representing the membership matrix.
8. The bridge construction risk assessment method is characterized by comprising the following steps of:
the confirming module is used for confirming a risk analysis total target of the target bridge according to the collected historical risk data of the similar bridge construction;
the building module is used for decomposing the risk analysis total target to build a risk evaluation model of the layered structure; wherein, all risk factors of each layer in the risk evaluation model form an evaluation set of the layer;
the determining module is used for determining a weight value corresponding to the evaluation set by adopting a analytic hierarchy process and determining a risk evaluation grade based on the evaluation set and the weight value;
the scoring module is used for scoring each evaluation factor in the risk evaluation level to establish a membership matrix;
the operation module is used for carrying out operation on the membership matrix by adopting a comprehensive weighting method to obtain a fuzzy evaluation matrix;
and the judging module is used for calculating a judging 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 principle.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the bridge construction risk assessment method according to any one of claims 1 to 7 when executing the computer program.
10. A storage medium having stored thereon a computer program, which when executed by a processor implements the bridge construction risk assessment method according to any one of claims 1 to 7.
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| CN117093842A (en) * | 2023-08-31 | 2023-11-21 | 四川省公路规划勘察设计研究院有限公司 | Geological deformation upper risk bridge monitoring demand assessment method and system |
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| CN117093842A (en) * | 2023-08-31 | 2023-11-21 | 四川省公路规划勘察设计研究院有限公司 | Geological deformation upper risk bridge monitoring demand assessment method and system |
| CN117093842B (en) * | 2023-08-31 | 2024-03-19 | 四川省公路规划勘察设计研究院有限公司 | Geological deformation upper risk bridge monitoring demand assessment method and system |
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