CN115829326A - Mountain road construction risk evaluation method based on optimized combination weighting model - Google Patents
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Abstract
The invention discloses a mountainous road construction risk evaluation method based on an optimized combination empowerment model, and aims at the problems that in the prior art, the selection of a risk evaluation index system and the precision of an evaluation result still need to be improved. The invention comprises the following steps: 1. making a mountain road construction risk list; 2. optimizing the risk list by using a Delphi method to obtain a risk evaluation index system; 3. determining the weight of each risk index by adopting an optimized combined weighting model; 4. constructing a fuzzy comprehensive evaluation model; 5. and determining the risk level of each risk road section, and providing comprehensive management and control measures according to the risk level and the weight of each risk index. The technology applies a scientific investigation method, combines an analytic hierarchy process, an entropy weight method and a FUZZY comprehensive evaluation method to construct an AHP-EWM-FUZZY safety risk evaluation model, adopts a combined weighting method combining subjectivity and objectivity to determine an evaluation index risk weight, and further applies the FUZZY comprehensive evaluation method to determine a risk grade.
Description
Technical Field
The invention relates to the technical field of engineering construction risk identification and risk assessment, in particular to a mountain road construction risk evaluation method based on an optimized combination weighted model.
Background
With the continuous development of social economy, the highway is used as an important transportation facility and plays an important role in the interaction between the inter-regional economic development and politics, economy and culture. With the change of development direction, the construction of the expressway is gradually changed from plain areas to mountain areas with underdeveloped economy. However, compared with the general expressway, the mountain area has more complicated topography and varied geology, and the hydrological weather condition is greatly different from that of the plain area, so that the construction of the mountain area expressway is greatly difficult, and corresponding safety accidents happen more often. The great investment of engineering construction and the serious consequences caused by engineering accidents make the reasonable and effective evaluation and control of the safety risk problem of the mountain expressway construction become the focus of attention in the field.
In order to search for a scientific and reasonable safety risk evaluation method, experts in the related field make a great deal of intensive research. Cui Wenjuan and the like surround four factors of human-object-environment-management of a mountain expressway construction site, construct a mountain expressway construction site safety evaluation index system, and establish a mountain expressway construction safety fuzzy comprehensive evaluation model. Wu Wei and the like establish an evaluation system of four main risk factors including environment, technology, resources and management and 26 sub-factors, determine the influence weights of different indexes by adopting an AHP method, and find out the main risk factors. Jiang Hong, aiming at the construction of the highway bridge, a fuzzy comprehensive evaluation model is established based on an analytic hierarchy process and a fuzzy mathematical theory, and the safety management levels of different bridge constructions are compared and analyzed. Mo Wen introduces fuzzy evaluation method to carry out quantitative evaluation on the risk level. Chen Shian and the like estimate the safety risk in the road engineering construction process by using a relative difference function on the basis of determining the weight of each evaluation index through an entropy weight method.
In the existing research, a literature research method is mostly relied on in the risk identification process, common risk factors in the mountain high-grade highway construction process are selected, actual investigation on actual risks of specific highway construction projects is lacked, the obtained risk evaluation index system is not high in degree of contact with actual projects, and therefore the risk evaluation result is influenced. Meanwhile, in the process of determining the evaluation index risk weight, a single weight determination method is mostly adopted, and the calculation result depends on the subjective experience of experts to a greater extent or is inconsistent with the actual engineering situation.
Disclosure of Invention
Aiming at the problems that the selection of a risk evaluation index system and the precision of an evaluation result still need to be improved in the prior art, the invention provides the mountain road construction risk evaluation method based on the optimized combination weighting model, which is suitable for engineering practice, accurate in risk evaluation result and objective and reasonable in analysis result.
The technical scheme of the invention is to provide a mountain road construction risk evaluation method based on an optimized combination empowerment model, which comprises the following steps: comprises the following steps:
step 1, formulating a mountain road construction risk list;
step 2, optimizing the risk list by utilizing a Delphi method to obtain a risk evaluation index system;
step 3, determining the weight of each risk index by adopting an optimized combined weighting model;
step 4, constructing a fuzzy comprehensive evaluation model;
and 5, determining the risk level of each risk road section, and providing comprehensive management and control measures according to the risk level and the weight of each risk index.
Preferably, in the step 1, a system safety theory and a hall three-dimensional structure theory are combined to comb common risk factors in the mountain highway construction process, and the system safety theory divides risk sources into four aspects of people, objects, environment, management and the like; the Hall three-dimensional structure considers different dimensions of engineering construction, risks are identified from three angles of time, space and engineering logic, a new risk identification tool is constructed by combining two methods, and a mountain road construction risk list is obtained.
Preferably, in the step 2, the risk list is optimized by a delphire method according to actual risks in the engineering construction process, such as requirements on safety, construction period, guarantee and cost, so as to obtain a mountain road construction risk evaluation index system.
Preferably, the step 3 comprises the following steps:
step 3.1, determining the main and objective weights of each risk index through an analytic hierarchy process and an entropy weight method;
step 3.2, utilizing the optimized combined empowerment model
Determining a combined weight of risk indicators, wherein w i Represents the combining weight, u i (vi) subjective weight, v, calculated by surface analysis i Calculating objective weights by representing entropy weight method
For the objective function, the error between the combination weight and the objective weight is constrained to be minimum; by the formula u i ＞u j Then w is i ＞w j Determining that the combination weight is consistent with the objective weight size sorting; by the formula w i ∈[u i ,v i ]Constraining the combined weight to be in a reasonable interval; if u i ＜v i Then take u i ＜w i ＜v i If u is i ＞v i Then take u i ＞w i ＞v i . The sum of the weight values being 1, i.e. w 1 +w 2 +…+w n ＝1。
Preferably, the step 4 comprises the following steps:
step 4.1, establishing a risk evaluation factor set U and a risk evaluation grade set V, U = { U = 1 ,U 2 ,...,U m },U i Representing influence factors of an evaluation object, m representing the number of evaluation indexes, V = { V = 1 ,V 2 ,V 3 ,V 4 ,V 5 = { low risk, lower risk, general risk, higher risk, high risk };
step 4.2, calculating the combined weight of each risk index by the optimized combined weighted model to obtain an evaluation index weight set, wherein W = { W = 1 ，W 2 ，....，W n In which W is i Representing the weight of the ith evaluation index, and m representing the number of indexes;
4.3, carrying out single-factor fuzzy evaluation on evaluation indexes through a membership function
Determining the membership degree relation from each index of the evaluation factor set U to the comment set V, and further determining a membership degree matrix
In the formula, r ij Representing the membership of the ith factor to the jth evaluation level; d ij When the index i is evaluated, the number of people is evaluated as j level,
the total number of experts participating in evaluating a certain index;
step 4.4, by the formula D = W · R = (D) 1 ,d 2 ,....,d n ) Carrying out fuzzy comprehensive evaluation, wherein W is an evaluation index weight vector, R is a membership matrix, D represents a fuzzy comprehensive evaluation set, and D j Represents the sameAnd the membership degree of the evaluation object to the evaluation grade j, namely the evaluation grade in the evaluation set corresponding to the element with the maximum fuzzy comprehensive evaluation concentration value.
Preferably, in the step 5, risk road sections are preliminarily divided according to the on-site geological survey condition, a fuzzy comprehensive evaluation model is used for calculating the risk level of each risk road section, the road sections with higher risk are accurately identified, and then risk management and control measures are taken in a targeted manner.
Compared with the prior art, the mountain road construction risk evaluation method based on the optimized combination empowerment model has the following advantages: a new risk identification tool is provided by combining a system safety theory and a Hall three-dimensional structure theory, and risk factors can be comprehensively and pertinently identified by combining specific engineering projects; meanwhile, an optimized combined weighting model is established, qualitative analysis and quantitative analysis are combined by determining the risk weight by a combined analytic hierarchy process and an entropy weight process, and the risk factors are comprehensively and comprehensively analyzed and evaluated. By combining the engineering example of the Veriea Elisa project, various risk factors in the project process are analyzed in detail, the defects that the risk identification result is not suitable for the engineering practice and the risk evaluation result is not accurate enough are optimized, the analysis result is more objective and reasonable, and the construction progress of the Elisa project is guided better.
The method is characterized in that the construction of the expressway in Elsia is taken as the background, based on the Hall three-dimensional structure theory, the list of common risk factors in the construction period of the expressway in the mountain area is combed by arranging relevant documents and similar engineering project data. And by combining with a system safety theory, various factors causing risks are fundamentally mastered, and a multi-dimensional, multi-level and multi-angle risk evaluation index system is optimized. An AHP-EWM-FUZZY safety risk evaluation model is constructed by applying a scientific investigation method, combining a layer analysis method, an entropy weight method and a FUZZY comprehensive evaluation method, and an evaluation index risk weight is determined by adopting a combined weighting method combining subjectivity and objectivity so as to determine a risk grade by applying the FUZZY comprehensive evaluation method.
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FIG. 1 is a schematic diagram of a risk factor identification three-dimensional structure in the present invention;
FIG. 2 is a schematic structural view of an Elsiya highway construction safety evaluation index system according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
The mountain road construction risk evaluation method based on the optimized combination weighted model is further described with reference to the accompanying drawings and the specific implementation mode: as shown in the figure, the specific process of the present embodiment is as follows.
1. Risk identification
1.1 mountain area highway construction risk evaluation index recognition tool
Due to complex hydrogeological conditions, variable climatic conditions and the like in the areas where the mountain expressway is constructed, the risk factors possibly existing in the construction period are many, and in order to scientifically and comprehensively identify various possible risks, the principles of scientificity, systematicness, typicality and operability are followed in screening the evaluation indexes. In consideration of complexity and diversity of risk factors, the method provided by the invention combines the system safety theory and the Hall three-dimensional structure theory to perform preliminary screening of the risk factors on the basis of strictly following the principle.
According to the role in the accident development process, the system safety theory divides the hazard sources into a first type of hazard source and a second type of hazard source. Among them, the first category of hazard is defined as energy (energy carrier) or dangerous substances that may be accidentally released, which themselves have the ability to interfere with human metabolism or work. Normally, the first type of hazard source is in a restricted state, and no accidental release, i.e. no dangerous accident, occurs. There is still the possibility that the restriction will be breached by a variety of factors, such that the first type of hazard will be breached is the second type of hazard. Mainly comprises three types of object faults, human errors and environmental factors. In general, the first type of hazard generates the energy required to initiate an casualty accident, and the second type of hazard provides an opportunity for a hazard to occur. According to the theory, the research summarizes and combs the risk evaluation of the high-grade highway in the Elisa period from four basic factors causing the risk occurrence, namely human, material, environment and management, and identifies the risk factors from a more essential point of view.
The Hall three-dimensional structure theory is a risk identification method summarized by American system engineers Hall (A.D.Hall) on the basis of a large amount of engineering practical experience, and has a good application effect on planning and management of large-scale complex system engineering. The Hall three-dimensional structure is classified based on different characteristics of engineering risks, various risk factors possibly occurring in construction of an engineering are carefully screened and judged from dimensions (common time dimension, knowledge dimension and logic dimension) such as systematicness, comprehensiveness and dynamics, and finally the risk identification result is reasonable and reliable, and the actual application degree is wide.
By combining the advantages of the two methods, referring to the process of comprehensively identifying the construction progress risk index by referring to the time dimension, the knowledge dimension and the logic dimension in the Hall three-dimensional structure identification model, and combining the actual construction situation of the high-grade highway in Elisa, the research comprehensively analyzes risk factors from the three dimensions of the construction time dimension, the construction space dimension and the risk logic dimension, and constructs the Hall three-dimensional risk identification structure as shown in figure 1.
1.2 Allia expressway construction risk evaluation index system
The bolivia alliysia highway project is the main road for inland countries where boli connects with brazil sea ports, and is the economic traffic life of the country. The project is a typical mountain road, the terrain is steep, the road is meandered and goes downwards, the starting point is high Cheng Haiba 1850m, the end point is high 450m, and the average longitudinal slope is 5%. The highway has a total length of 30.3km and comprises two separated tunnels of 1700 meters, a bridge 29 (3.4 km), a retaining wall structure of 5.6km, a culvert of 120 channels and a high bridge-tunnel ratio. The full width of the road is 20.6m, and the double-layer asphalt concrete pavement is designed. The conclusion of construction data shows that the construction of the ales road mainly exists: and (1) the project level is high, and the international influence is large. And (2) the item address condition is poor and the risk coefficient is high. (3) the area where the rain falls is much and the meteorological conditions are poor. (4) High social attention, high pressure of communication protection and the like.
And (3) after the Hall three-dimensional risk structure provided in the step 1.1 is utilized to carry out preliminary combing on risk factors in the construction period of the high-grade highway in Elsiya, and an expert scoring method is adopted to further optimize a risk evaluation index system. And determining an Elisa high-grade highway construction period risk evaluation index system as shown in figure 2 by combining results after multiple rounds of expert scoring and the proposed suggestions and considering factors such as engineering construction period safety requirements, construction period requirements, insurance requirements, cost requirements and the like.
The Hall three-dimensional risk identification structure is used for listing common risk factors in the construction process of the Airsiya high-grade highway, then an expert scoring method is adopted for optimizing a risk factor list, various requirements of the engineering construction period are considered, and a construction period risk evaluation index system is determined.
2. Risk assessment
2.1, AHP-EWM combined weighting method to determine risk index weight
At present, there are many methods for determining the index weight, which can be roughly divided into subjective weighting method and objective weighting method. Specifically, the subjective weighting method determines the index weight according to the subjective importance degree of experts on each index, and is simple and easy to operate. However, the subjective judgment of experts is more focused on, so the objectivity of the empowerment result is poor. Common subjective weighting methods include AHP method, least squares method, efficacy coefficient method, delphires method, binomial coefficient method, and the like. The objective weighting method determines the weight according to the relation between original data, does not depend on subjective judgment of people, has a strong mathematical theory basis in a judgment result, and is easily affected by random errors of index samples to cause instability of the weight. The commonly used objective weighting methods include principal component analysis, entropy weight method, dispersion maximization method, etc. In order to embody the principle of quantitative and qualitative combination which needs to be observed in the risk evaluation process, the invention uses an AHP-EWM combined weighting method to determine the weight of the risk index.
2.1.1 determination of subjective weights by AHP method
Analytic Hierarchy Process (AHP) is a hierarchical weight decision analysis method proposed by the american college of america saty in the early 70 s of the 20 th century. The method comprises the following steps:
(1) Structural judgment matrix
The inviting experts score individual risk factors in the risk evaluation index system, construct a judgment matrix for elements at the same level, facilitate comparison between every two, and the specific form is as follows:
wherein aij represents the importance degree of the ith factor relative to the jth factor in the risk assessment index system, and the value can be assigned by a 1-9 scale method. Thus, it is known that ij =1 (i = j), a ij ＝1/a ji ，a ij >0。
Using a 1 to 9 scale of a ij And (3) giving a certain value from 1 to 9, and determining the result of the two-to-two comparison of the factors. The importance of the different factors is shown from a quantitative point of view. Wherein the different numbers specifically represent the meanings as indicated in the following table.
TABLE 1a ij Value and meaning
| Scale (aij) | Degree of importance |
| 1 | Factor i is equally important compared to factor j |
| 3 | The i factor is slightly more important than the j factor |
| 5 | The i factor is significantly more important than the j factor |
| 7 | The i factor is more important than the j factor |
| 9 | The i factor is extremely important than the j factor |
| 2、4、6、8 | Intermediate values of the above-mentioned adjacent scale importance |
(2) Calculating risk weight of each factor
1. Judging matrix column vector normalization
Wherein, a ij In order to determine the ith row and jth column elements in the matrix (i.e., the result of pairwise comparison), n is the order of the determination matrix.
2. Normalized matrix row and column
3. For the vectors after line calculation
Performing normalization processing
Obtaining a characteristic vector W = (W) of the judgment matrix after processing 1 ，W 2 ，W 3 ，…，Wn) T ,
W i And representing the weight of the ith index to the previous index in the risk evaluation system.
4. Calculating the maximum characteristic root lambda of the judgment matrix max
The maximum characteristic root lambda of the judgment matrix can be obtained by the formula AW = lambda W max
(3) Performing consistency check
Calculating random consistency check indicators, i.e.
Wherein RI is referred to as randomness index, which can be obtained by table lookup,
abbreviated as CI. When C.R is less than or equal to 0.1, the consistency is considered to meet the requirement. RI values are shown in the following Table
TABLE 2 random consistency index RI values
2.1.2EWM method for determining objective weight
Entropy is essentially a concept in thermodynamics, a measure of the degree of disorder or disorder of a system, and was introduced into information theory by american mathematician c.e. shannon in the 50's of the 20 th century and referred to as information entropy. The larger the entropy is, the more disorderly the system is (namely, the less information is carried), the smaller the utility value is, and the smaller the weight occupied in the comprehensive evaluation is; conversely, the smaller the entropy is, the more orderly the system is (i.e. the more information is carried), the more weight the system occupies in the comprehensive evaluation. Therefore, the weight of each index can be calculated by utilizing the property of the information entropy, and a basis is provided for multi-index comprehensive evaluation. The detailed calculation process is as follows:
(1) Obtaining an original evaluation data matrix
And inviting n experts to score the m evaluation indexes to obtain an m multiplied by n order judgment matrix. The scale of the score may be defined in the range of 1-9. And dividing the risk degree of the evaluation index into five grades of low risk, general risk, high risk and high risk, and respectively assigning values by 9, 7, 5, 3 and 1. If the risk level lies between two adjacent levels, it is represented by the number in the middle of the numbers representing these two risk levels, 8, 6, 4, 2, 0 respectively. Determine matrix X as follows
Wherein x is ij Representing the scoring value of the ith index by the jth expert.
(2) Matrix data normalization
The raw data is normalized. This step of processing is divided into two forms. The process for the larger and more optimal type index is called forward process, and the formula is as follows:
the process for smaller and better type indicators is called inverse process, and the formula is as follows:
in the formula: mjn j (x ij ) Lowest score, max, assigned to the ith index by the expert j (x ij ) Marking the ith index for the expertHighest score of p ij A standard value for the j-th expert to score the i-th index, and 0<p ij <1。
(3) Calculating index entropy
Comprises the following steps of; 0<i<m, wherein e i Is the entropy value of the ith evaluation index,
and specify when z ij When =0, z ij ×lnz ij ＝0。
(4) Calculating the index entropy weight
In the formula: d i Entropy weight of the i-th evaluation index, 0<D i <1，
2.1.3 determining the index weight by a combination weighting method
(1) Conventional combined empowerment model
Because the subjective weighting method and the objective weighting method have respective advantages and disadvantages, the disadvantage of overlarge subjectivity exists only by using the subjective weighting method, and the actual significance of indexes and the preference of a decision maker cannot be reflected only by using the objective weighting method, so the method of main and objective combination weighting is often adopted in practical application. The current commonly used combined weighting model comprises a linear combined weighting model, a normalized combined weighting optimization model and a game theory combined weighting model.
1) Linear combined empowerment model
w i ＝αu i +(1-α)v i ,0＜θ＜1 (10)
In the formula, w i Is the combining weight; u. of i Is a subjective weight; v. of i Is an objective weight; alpha isThe weight ratio coefficient is determined by a decision maker and is generally 0.5.
2) Normalized combined weighting model
In the formula, w i Is the combining weight; u. u i Is a subjective weight; v. of i Is an objective weight.
The above is a common combined weighting model, and the combination with objective weight makes up the disadvantage of the subjective weighting method that the subjectivity is too strong to a certain extent, and is essentially a 'compromise' scheme. From another perspective, the invention explores a combined empowerment optimization model.
The above analysis of the AHP and EWM quality shows. The main advantage of the subjective weighting method is that the practical situation of the abundant experience and index of experts in the problem can be reflected, such as X 1 ,X 2 ，X 3 ，X 4 The indexes are calculated by a subjective weighting method to obtain index weight W = (0.165,0.416,0.337,0.082), namely the importance X of the four indexes is finally determined by experience of abundant expert experience 2 ＞X 3 ＞X 1 ＞X 4 . But at the same time, X 2 Ratio X 1 The accuracy is obtained by expert experience, and is difficult to avoid being too subjective. The objective weighting method (such as entropy weighting method) is based on the data itself, and the final value is accurately calculated according to the information content contained in the data. However, since the weight is determined from the viewpoint of the amount of information, the significance and importance of the index in the actual problem are not considered, and the result of calculation may be contrary to the actual situation.
Therefore, in order to retain the advantages of the two methods as much as possible and reduce the disadvantages of the two methods as much as possible, a combination model combining the advantages of the two methods is constructed: on the premise of not changing the index importance sequence calculated by the subjective weighting method, the combination weight with the minimum deviation with the objective weight numerical value is solved, so that the index importance sequence calculated by utilizing abundant expert experience is reserved, and the objective weight calculation numerical value is also maximally reserved.
(2) Combined empowerment optimization model construction process
For n index elements, calculating a subjective weight vector u = (u) by an AHP method 1 ，u 2 ，...，u n ) V = (v) calculated by EWM 1 ，v 2 ，...，v n ) The combination weight is w = (w) 1 ，w 2 ，...，w n ) For convenience of the subsequent description, assume u 1 ＞u 2 ＞…＞u n I.e. when i < j, u i ＞u j 。
1) Determining an objective function
The minimum deviation between the combination weight and the objective weight is targeted, as shown in the following formula.
2) Determining constraints
1. The combination weight is consistent with the subjective weight size
For u assumed above 1 ＞u 2 ＞…＞u n In order, the obtained combining weight also satisfies w 1 ＞w 2 ＞…＞w n 。
2. The combination weight falls within a reasonable interval
If u i ＜v i Then take u i ＜w i ＜v i (ii) a If u i ＞v i Then take u i ＞w i ＞v i . The sum of the weight values being 1, i.e. w 1 +w 2 +…+w n ＝1。
3) Establishing a combined empowerment optimization model
In order to retain the advantages of the two methods as much as possible and reduce the defects of the two methods as much as possible, a combination model combining the advantages of the two methods is constructed: on the premise of not changing the index importance sequence calculated by the subjective weighting method, the combination weight with the minimum deviation with the objective weight numerical value is solved, so that the index importance sequence calculated by utilizing abundant expert subjective experiences is reserved, and the objective weight calculation numerical value is also reserved to the maximum extent.
2.1.4 example Calculations
(1) Calculating combining weight by using conventional normalized combining weight model
The subjective weight calculated by the equations (1) to (4) is W H The objective weight calculated by the equations (5) to (9) is W E And calculating the combined weight by using a normalized combined weighted model of a formula (11) on the principle of combining the qualitative weight and the quantitative weight. As shown in the following table:
TABLE 3 normalized combination weighting model for calculating index weight of each factor layer
(2) Computing combining weights using an optimized combining weighted model
The subjective weight calculated by the equations (1) to (4) is W H The objective weight calculated by the equations (5) to (9) is W E And (3) calculating the combination weight by using a qualitative and quantitative combination principle and optimizing a combination weighting model according to a formula (13). As shown in the following table:
TABLE 4 optimized combination weighting model for calculating index weight of each factor layer
| Subjective weight W H | Objective weight W E | Combining weight W | |
| C11 weathered shale slope | 0.097 | 0.087 | 0.186 |
| C12 groundwater and river water invasion | 0.059 | 0.044 | 0.057 |
| C13 sudden geological disaster | 0.032 | 0.044 | 0.031 |
| C14 black sludge soft foundation | 0.173 | 0.089 | 0.34 |
| C15 long-term heavy rainfall | 0.114 | 0.153 | 0.39 |
| C21 construction equipment replacement | 0.026 | 0.045 | 0.23 |
| C22 weather conditions | 0.078 | 0.044 | 0.675 |
| C23 construction material supply | 0.012 | 0.04 | 0.094 |
| Amount of C31 repair works | 0.045 | 0.044 | 0.172 |
| C32 later-period operation and maintenance | 0.025 | 0.089 | 0.193 |
| C33 reselection line | 0.082 | 0.089 | 0.634 |
| Large C41 traffic flow | 0.094 | 0.087 | 0.33 |
| C42 road construction | 0.114 | 0.144 | 0.67 |
2.2 fuzzy comprehensive evaluation method for determining risk level of Elisa highway
2.2.1 fuzzy evaluation model building
A fuzzy comprehensive evaluation method (FCE for short) is a method which is based on fuzzy mathematics, converts qualitative evaluation into quantitative evaluation according to a membership degree theory, and applies the fuzzy mathematics to comprehensively evaluate objects or objects restricted by various factors, so that the fuzziness of system description can be effectively quantified in a fuzzy set mode. During the construction of the mountain highway, the mountain highway is restricted by factors such as environment, geology, manpower, machinery and management, and has certain complexity and uncertainty, thereby causing the ambiguity of system description. Therefore, in order to solve the above problems, it is decided to determine the risk level by the fuzzy comprehensive evaluation method. The specific evaluation steps are as follows:
(1) Establishing a risk evaluation grade set V
The risk evaluation of the construction of the high-grade highway in Elsiya is a comprehensive evaluation on the construction safety management and the overall safety performance. The research takes the thought of 'prevention as the main', and the comments are divided into 5 grades according to the existing research and standard guidelines, namely: "high risk, higher risk, general risk, lower risk, low risk".
V＝{V 1 ,V 2 ,V 3 ,V 4 ,V 5 } = { low risk, lower risk, general risk, higher risk, high risk } = {1,3,5,7,9}
(2) Establishing a risk evaluation factor set U
And establishing an alexiya high-grade highway construction risk evaluation hierarchical model and establishing an evaluation factor set U according to the risk identification result. U = { U = { (U) 1 ,U 2 ,...,U m },U i Representing the influence factor of the evaluation object, and m representing the number of the evaluation indexes.
(3) Calculating a weight vector W
Constructing all judgment matrixes of each hierarchy according to the combined weighting method provided in the previous section, and calculating to obtain a corresponding weight vector W = { W = 1 ，W 2 ，....，W n W, where Wi represents the ith evaluationThe weight of the indexes, and m represents the number of the indexes; . The weight vector represents the degree of importance of each evaluation index in the evaluation factor set U.
(3) And carrying out single-factor fuzzy evaluation on evaluation indexes.
By function of degree of membership
Determining the membership degree relation from each index of the evaluation factor set U to the comment set V, and further determining a membership degree matrix
In the formula: r is ij Representing the degree of membership of the ith factor to the jth evaluation level, d ij When the index i is evaluated, the number of persons is evaluated as j.
The total number of experts participating in the evaluation of a certain index.
(4) Fuzzy comprehensive evaluation
Represented by the formula D = W · R = (D) 1 ,d 2 ,....,d n ) And carrying out fuzzy comprehensive evaluation. W is an evaluation index weight vector, and R is a membership matrix. D represents a fuzzy comprehensive evaluation set, D j Representing the membership degree of the evaluation object to the evaluation grade j in the whole.
(5) Comprehensive evaluation
And respectively assigning initial scores to construct score value vectors S = (1, 3,5,7 and 9) according to 5 grades of 'low risk, general risk, high risk and high risk' in the Airsia high-grade highway construction safety risk comment set. Final risk grade F = D × S.
2.2.2 example Calculations
(1) Fuzzy comprehensive evaluation based on optimized combined weighted model
The risk section IV of the Airsiya road is an area with the largest construction difficulty in the whole project, and the risk section IV is practically applied by taking a risk section 4 (pile number K7+ 600-K12 + 800) as an example, so as to determine the overall risk level of the risk section.
(1) By issuing a questionnaire, adopting a Delphi method to score experts, collecting and sorting the survey results to obtain a fuzzy relation matrix about the secondary indexes:
R 1 : safety risks
R 2 : risk of construction period
R 3 : cost risk
R 4 : safety ventilation insurance
(2) Determining a local weight vector W of the factor layer index according to the optimized combined weighting method:
WR 1 ＝(0.186，0.057，0.031，0.34，0.39)
WR 2 ＝(0.23，0.675，0.094)
WR 3 ＝(0.172，0.193，0.634)
WR 4 ＝(0.33，0.67)
(3) Solving a fuzzy comprehensive evaluation set of the criterion layer
Selecting a weighted average type comprehensive evaluation model, solving the single-factor fuzzy comprehensive evaluation vector of the secondary indexes of the factor layer as follows:
fuzzy comprehensive evaluation vector of human risk: d Safety risks ＝R 1 ×WR 1 ＝(0.02，0.095，0.108，0.268，0.509)
The fuzzy comprehensive evaluation set of the risk of the construction period is as follows: d Risk of construction period ＝R 2 ×WR 2 ＝(0.061，0.094，0.144，0.374，0.328)
A cost risk fuzzy comprehensive evaluation set: d Cost risk ＝R 3 ×WR 3 ＝(0.092，0.05，0.133，0.265，0.461)
And (3) ensuring a fuzzy comprehensive evaluation set of ventilation risk: d Safety ventilation insurance ＝R 4 ×WR 4 ＝(0，0.068，0.182，0.318，0.432)
(4) Constructing a fuzzy comprehensive evaluation judgment matrix
Constructing an integral fuzzy comprehensive evaluation judgment matrix by the single-factor fuzzy evaluation vector
(5) Calculating an overall comprehensive evaluation vector
And performing fuzzy synthesis operation according to the criterion layer index combination weight W determined by the optimized combination weighting method and the overall fuzzy judgment matrix R, and calculating an overall fuzzy comprehensive evaluation vector B.
W＝(0.427，0.087，0.1662，0.324)
D＝W×R＝(0.038，0.084，0.15，0.32，0.51)
(6) Determining an engineering overall risk level
And respectively endowing initial scores to construct a score value vector S according to 5 grades of 'low risk, general risk, high risk and high risk' in the construction safety risk score set.
F＝D×S＝7.8(2.17)
The risk evaluation result of the 4# risk road section of the Exosella high-grade road is calculated to be 7.8 points, and between a high risk and a high risk, a targeted control measure needs to be taken on the road section to avoid the risk.
(2) Fuzzy comprehensive evaluation based on normalized combined weighting model
And repeating the steps by using a normalized empowerment model, and calculating that the risk evaluation result of the 4# risk road section of the Exicschia high-grade road is 6.2 points, which is between the general risk and the higher risk.
(3) Analysis of results
Comparing the fuzzy risk evaluation results based on the optimized combined weighting model and the normalized weighting model, wherein the risk result calculated by adopting the optimized combined weighting model is 7.8, and the risk level is between higher risk and high risk; and the risk grade calculated by using the common normalized weighted model is 6.2, which is between the common risk and the higher risk. The main reason for this is that when calculating the risk level using the fuzzy evaluation model, the final settlement result is different due to the different weights of the risk factors, and thus an accurate weight value is very important. The alexiya high-grade road is a main road for connecting the welfare and the brazil sea-going port in inland countries, is an economic traffic life line of the country, and is of great importance in ensuring the safety in the road construction process. Therefore, the risk level is calculated from a stricter angle, the construction side is facilitated to strengthen self risk control, and the engineering construction safety is better ensured.
3. Conclusion
The geological and hydrological environment of the region where the Airsia expressway construction project is located is complex, the engineering system is huge, and the technical difficulty is high. In order to ensure the safety of engineering construction, an evaluation method combining quantification and qualification is provided aiming at the condition that the existing evaluation method is focused on the evaluation or the expert experience or the quantitative analysis. By utilizing a Hall three-dimensional structure theory and combining a system safety theory, the method fundamentally grasps the basic factors causing the risk and adopts a multi-dimensional, multi-level and multi-angle optimized risk evaluation index system. And determining the risk factor weight by combining quantification and qualification by adopting an AHP-EWM combined weighting method. The result shows that the construction risk level of the Airsinia road project is medium risk, and the whole project is in a safe and controllable state after certain high risk factors are controlled. A construction risk evaluation model is constructed by using a fuzzy comprehensive evaluation method, an effective quantitative tool and a scientific theoretical method are provided for the engineering risk evaluation, and reference is provided for developing safety risk evaluation research similar to mountain expressway construction in the future.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (6)
1. The utility model provides a mountain road construction risk evaluation based on optimize combination empowerment model which characterized in that: comprises the following steps:
step 1, formulating a mountain road construction risk list;
step 2, optimizing the risk list by utilizing a Delphi method to obtain a risk evaluation index system;
step 3, determining the weight of each risk index by adopting an optimized combined weighting model;
step 4, constructing a fuzzy comprehensive evaluation model;
and 5, determining the risk level of each risk road section, and providing comprehensive management and control measures according to the risk level and the weight of each risk index.
2. The mountain road construction risk evaluation method based on the optimized combination weighted model according to claim 1, characterized in that: in the step 1, common risk factors in the mountain highway construction process are combed by combining a system safety theory and a Hall three-dimensional structure theory, risk sources are divided into four aspects of people, objects, environment, management and the like by the system safety theory, different dimensions of engineering construction are considered by the Hall three-dimensional structure, risks are identified from three angles of time, space and engineering logic, and a new risk identification tool is constructed by combining two methods to obtain a mountain highway construction risk list.
3. The mountain road construction risk evaluation method based on the optimized combination weighted model according to claim 1, characterized in that: and in the step 2, optimizing the risk list by using a Delphi method according to various actual risks in the engineering construction process, such as requirements on safety, construction period, guarantee and cost, so as to obtain a mountain highway construction risk evaluation index system.
4. The mountain road construction risk evaluation method based on the optimized combination weighted model according to claim 1, characterized in that: the step 3 comprises the following steps:
step 3.1, determining the main and objective weights of each risk index by an analytic hierarchy process and an entropy weight method;
step 3.2, utilizing the optimized combined empowerment model
Determining a combined weight of risk indicators, wherein w i Represents the combining weight, u i (vi) subjective weight, v, calculated by surface analysis i Calculating objective weights by representing entropy weight method
For the objective function, the combining weight and the objective weight error are constrained to be minimum, which is expressed by the formula u i ＞u j Then w is i ＞w j Determining the combination weight to be consistent with the ranking of the objective weight, which is represented by the formula w i ∈[u i ,v i ]Constraining the combined weight to be in a reasonable interval if u i ＜v i Then take u i ＜w i ＜v i If u is i ＞v i Then take u i ＞w i ＞v i . The sum of the weight values being 1, i.e. w 1 +w 2 +…+w n ＝1。
5. The mountain road construction risk evaluation method based on the optimized combination empowerment model according to claim 1, characterized in that: the step 4 comprises the following steps:
step 4.1, establishing a risk evaluation factor set UAnd risk assessment level set V, U = { U = 1 ,U 2 ,...,U m },U i Representing influence factors of an evaluation object, m representing the number of evaluation indexes, V = { V = 1 ,V 2 ,V 3 ,V 4 ,V 5 = { low risk, lower risk, general risk, higher risk, high risk };
step 4.2, calculating the combined weight of each risk index by the optimized combined weighted model to obtain an evaluation index weight set, wherein W = { W = 1 ，W 2 ，....，W n In which W is i Representing the weight of the ith evaluation index, and m representing the number of indexes;
4.3, carrying out single-factor fuzzy evaluation on evaluation indexes through a membership function
Determining the membership degree relation from each index of the evaluation factor set U to the comment set V, and further determining a membership degree matrix
In the formula, r ij Representing the membership of the ith factor to the jth evaluation level; d ij When the index i is evaluated, the number of persons evaluated as j level,
the total number of experts participating in evaluating a certain index;
step 4.4, by the formula D = W · R = (D) 1 ,d 2 ,....,d n ) Carrying out fuzzy comprehensive evaluation, wherein W is an evaluation index weight vector, R is a membership matrix, D represents a fuzzy comprehensive evaluation set, and D j And the evaluation level represents the membership degree of the whole evaluation object to the evaluation level j, namely the evaluation level in the comment set corresponding to the element with the maximum fuzzy comprehensive evaluation concentration value.
6. The mountain road construction risk evaluation method based on the optimized combination weighted model according to claim 1, characterized in that: in the step 5, risk road sections are preliminarily divided according to the on-site geological survey condition, risk grade calculation is respectively carried out on each risk road section by utilizing a fuzzy comprehensive evaluation model, the road section with higher risk is accurately identified, and then risk management and control measures are pertinently taken.
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| CN116701878A (en) * | 2023-07-05 | 2023-09-05 | 华南理工大学 | Risk prediction system and method for mud cake formation of cutter head of earth pressure balance shield machine |
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