Pier anti-scouring capability evaluation method based on RES-AHP multi-dimensional cloud model

Abstract

The invention provides a pier anti-scouring capability evaluation method based on an RES-AHP multi-dimensional cloud model. The method comprises the following steps: constructing evaluation indexes; encoding the digital characteristic value of the multi-dimensional cloud model based on the evaluation index; constructing an index relative importance evaluation feature matrix based on the digital feature values of the multi-dimensional cloud model; constructing an expert floating cloud model based on the index relative importance evaluation feature matrix; based on the expert floating cloud model, outputting index weights by adopting RES coding; and finally determining the anti-scouring capability evaluation grade of the pier foundation based on the membership degree of the evaluation grade of each multi-dimensional cloud model corresponding to the index weight output project. According to the method, traditional AHP importance expression is improved, the RES theory is introduced, the effect between the indexes and the effect between the indexes and the system are fully considered, the problem that in traditional evaluation, the subjectivity of index weights is high is solved, the result shows the fuzziness which can be expressed by a cloud model, the reference to actual engineering is higher, and the method has high application and popularization value.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering, and more specifically, to an evaluation method for the scour resistance of pier foundations. Background Art

[0002] Currently, the number of highway bridges in China has exceeded 1 million, and nearly 40% of them have been in service for more than 20 years. During operation, many disease problems have emerged in highway bridges. Among them, the overall structural instability caused by the scour of pier foundations has become a huge challenge for existing bridges. Timely and accurate evaluation of the scour resistance of existing bridges is the key to ensuring the safe operation of bridges.

[0003] Bridge water damage accidents caused by pier foundation scour account for more than 50%. Scour directly affects the buried depth and service status of pier foundations. The scour process involves the multi-scenario coupling of hydrological conditions, riverbed geological conditions, and pier shape foundation conditions. Existing monitoring schemes cannot timely reflect the multi-index interaction of the scour resistance of pier foundations. Due to the relatively complex underwater environment where pier foundations are located, it is difficult to observe, and initial failures are difficult to detect in a timely manner. When obvious settlement and inclination occur in the bridge, the damage has often been formed, even approaching the failure critical value, missing the best intervention opportunity. Therefore, scour damage is concealed and sudden. Once it occurs, it will cause greater social harm.

[0004] There are few evaluation methods for the scour resistance of pier foundations. Existing empirical formulas for calculating scour depth rely on empirical coefficients, and the evaluation results are too subjective and conservative; scour simulations based on numerical simulation require a large amount of computing power and are difficult to meet the timeliness requirements of engineering evaluations; traditional evaluation methods such as the analytic hierarchy process and fuzzy comprehensive evaluation method rely entirely on expert decision-making during index weighting, with a large degree of subjectivity; evaluation methods combining new theories such as FMEA method, weight back-analysis method, and extension theory have improved the problem of excessive subjective factors in traditional analysis methods, but most of them establish single-factor models, limited to the certainty of single factors, and cannot reflect the interaction effects between indicators. The final evaluation results often use functions with obvious segmentation such as trapezoidal membership functions, which are not conducive to showing the ambiguity in the definition between different ability evaluation levels and the randomness of the limit state between different levels of actual pier foundations under scour conditions.

[0005] The Rock System Engineering Theory (RES) starts from an overall perspective, fully considers the interaction between indicators and between indicators and the whole, objectively determines the weights of indicators from the perspective of the overall project, and can improve the problem of strong subjective dependence of the Analytic Hierarchy Process (AHP). Introducing the cloud model to convert qualitative concepts and quantitative data can better reflect the fuzziness and randomness of evaluation grading. By improving the traditional AHP with RES, considering the multi-dimensional interaction between indicators, obtaining objective and accurate indicator weights, and combining the characteristics of the cloud model to evaluate the anti-scour ability of bridge pier foundations. Summary of the Invention

[0006] Aiming at the characteristics of weak multi-dimensional interactivity and poor fuzziness in the current evaluation methods for the anti-scour ability of bridge pier foundations, the present invention provides a multi-dimensional cloud model evaluation method based on the RES-AHP theory to solve the problems of strong subjectivity in existing evaluation methods, ignoring the interaction between indicators, and poor robustness of evaluation grading. To solve the technical problems in the above background, the present invention defines the grading of indicators such as water flow velocity, riverbed slope, and water blocking ratio, and combines the evaluation of the anti-scour ability of bridge pier foundations with four gradings to obtain a method for comprehensively and accurately evaluating the anti-scour ability of bridge pier foundations.

[0007] To achieve the above object, the present invention proposes an evaluation method for the anti-scour ability of bridge piers based on the RES-AHP multi-dimensional cloud model, including:

[0008] Construct evaluation indicators;

[0009] Encode the digital characteristic values of the multi-dimensional cloud model based on the evaluation indicators;

[0010] Construct an evaluation characteristic matrix of the relative importance of indicators based on the digital characteristic values of the multi-dimensional cloud model;

[0011] Construct an expert floating cloud model based on the evaluation characteristic matrix of the relative importance of indicators;

[0012] Based on the expert floating cloud model, use RES encoding to output the indicator weights;

[0013] Output the membership degrees of each multi-dimensional cloud model evaluation level corresponding to the project based on the indicator weights;

[0014] Determine the evaluation level of the anti-scour ability of the bridge pier foundation based on the membership degrees.

[0015] Further, the process of constructing an evaluation characteristic matrix of the relative importance of indicators based on the digital characteristic values of the multi-dimensional cloud model includes:

[0016] Quantify and grade each evaluation indicator, set the grading boundaries, construct an indicator grading cloud model matrix, and determine the interval expectation and variance;

[0017] Generate normal random numbers and multi-dimensional normal random numbers based on the interval expectation and variance;

[0018] Set the number of cloud droplets to be generated, and generate cloud droplets in a loop based on the normal random numbers and the multi-dimensional normal random numbers. Stop the loop when the number of cloud droplets to be generated is reached;

[0019] Retain the generated cloud droplets to form a multi-dimensional cloud model;

[0020] Encode the characteristic cloud model according to the AHP scale;

[0021] Aggregate the characteristic cloud models according to the expert weights to form the expert floating cloud model.

[0022] Further, the method for generating cloud droplets in a loop based on the normal random numbers and the multi-dimensional normal random numbers is as follows:

[0023]

[0024] Among them, ((x1, x2, …, x n ), μ i ) is a cloud droplet, which is a random realization of the concept on multi-dimensional values using the cloud model. (x1, x2, …, x n ) are index values, and μ i is the membership degree corresponding to the i-th cloud model at this point.

[0025] Further, the method for encoding the characteristic cloud model according to the AHP scale is as follows:

[0026] [C0 = (Ex0, En0, He0), C1 = (Ex1, En1, He1),..., C8 = (Ex8, En8, He8)].

[0027] Among them, (Ex0, En0, He0)…(Ex8, En8, He8) respectively represent the relative importance of the evaluation indicators represented by 9 AHP scales.

[0028] Further, the method for aggregating the characteristic cloud models according to the expert weights is as follows:

[0029]

[0030] Among them, Ex i represents the expectation of the i-th evaluation indicator, En i represents the entropy of the i-th evaluation indicator, and He i represents the hyper-entropy of the i-th evaluation indicator.

[0031] Further, the process of outputting the index weights using RES encoding includes:

[0032] Perform row encoding on the expert floating cloud model to obtain the impact of the evaluation index on the system;

[0033] Perform column encoding on the expert floating cloud model to obtain the impact of the system on the evaluation index;

[0034] Based on the interaction between the impact of the evaluation index on the system and the impact of the system on the evaluation index, calculate the weight of the evaluation index.

[0035] Furthermore, the method for calculating the weight of the evaluation index is as follows:

[0036]

[0037] Among them, ω i represents the weight of the evaluation index, C i represents the impact of the evaluation index on the system, and E i represents the impact of the system on the evaluation index.

[0038] Furthermore, the method for outputting the membership degree of each multi-dimensional cloud model evaluation level corresponding to the project based on the index weight is as follows:

[0039]

[0040] Among them, μ(x1, x2, …, x n ) represents the membership degree of the evaluation index, x i represents the i-th evaluation index, ω i represents the weight of the evaluation index, Ex i represents the expectation of this level when constructing the i-th level cloud model for this index, and En ki ' is a normal random number that follows En'(En1', En2', …, En n )~(Ex(Ex1, Ex2, …, Ex n ), He(He1, He2, …, He n ) 2 ) distribution.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] In the present invention, by improving the AHP weight coding, relative importance values are assigned to the indicators, a floating cloud model is constructed by aggregating the basic clouds. In view of the deficiencies of the fuzzy AHP in evaluation, the coding principle under the interaction of multi-dimensional indicators in the RES model is introduced, the weight assignment method is improved, the weights based on the indicator interaction matrix are obtained, four multi-dimensional cloud models are constructed to describe the fuzziness between different evaluation levels, and according to the principle of maximum membership degree, the comment set where the indicators are located is aggregated to determine the anti-scouring ability level, and an evaluation model for the anti-scouring ability of the pier foundation multi-dimensional cloud model is established.

[0043] One of the innovations of the present invention is that in the evaluation method of the anti-scouring ability of the pier foundation, the expression of the traditional AHP importance scale is improved to reflect the randomness of the indicator range. The RES theory is introduced to fully consider the interactions between indicators and between indicators and the system, and the weights of the indicators are determined in multiple dimensions, improving the problem of strong subjectivity of the indicator weights in traditional evaluations. A multi-dimensional evaluation level cloud model is constructed, the measured values of the indicators are input, and according to the principle of maximum membership degree, the anti-scouring ability level of the pier foundation can be determined. The result reflects the fuzziness that can be expressed by the cloud model, has higher reference value for actual projects, and has strong application and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0045] Figure 1 is a schematic flow chart of the evaluation method for the anti-scouring ability of the pier foundation based on the RES-AHP multi-dimensional cloud model provided by the present invention;

[0046] Figure 2 is a schematic diagram of the reference index system for the anti-scouring ability of the pier foundation provided by the present invention;

[0047] Figure 3 is a schematic diagram of the expression of the importance scale of the indicators after improving the AHP coding according to the present invention;

[0048] Figure 4 is a schematic diagram of optimizing the interaction matrix by introducing the RES theory according to the present invention;

[0049] Figure 5 is a cloud model diagram for the evaluation of the anti-scouring ability of the pier foundation according to a specific embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0050] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0051] Embodiment 1

[0052] This embodiment proposes an evaluation method for the scour resistance of bridge piers based on the RES-AHP multi-dimensional cloud model, including:

[0053] (1) Select n evaluation indicators (x1, x2,..., x n ) according to specifications, engineering evaluation reports, and the mechanism of scour action, and quantify the index grading.

[0054] (2) Determine the digital characteristics of the multi-dimensional cloud model for the quantitative conversion of the natural language of each index grading, and determine the double-boundary constraint limit interval T[N1, N2] of the cloud model of each comment set. The interval expectation Ex = (N min + N max ) / 2, and the entropy The hyperentropy He = λEn.

[0055] (3) Generate normal random numbers En'(En1', En2',..., En n ) with Ex (Ex1, Ex2,..., Ex n ) and He (He1, He2,..., He 2 ) as the expectation and variance. n ')

[0056] (4) Generate n-dimensional normal random numbers with Ex (Ex1, Ex2,..., Ex n ) and En'(En1', En2',..., En n ) 2 as the expectation and variance.

[0057] (5) The calculation formula is as follows:

[0058]

[0059] Among them, ((x1, x2,..., x n ), μ i ) is a cloud droplet, which is a random realization of the concept using the cloud model in multi-dimensional values. (x1, x2,..., x n ) are the values of each dimension, and μi is the membership degree of this point corresponding to the i-th cloud model.

[0060] (6) Input the number of cloud droplets N to be generated in the algorithm, loop the steps (3) to (5) for N times, and retain the generated cloud droplets to form a multi-dimensional cloud model.

[0061] (7) Improve the AHP importance expression according to the cloud model coding principle. Based on the 9 scales determined in AHP, analogously establish 9 cloud model codings to determine the characteristic cloud model of the relative importance expression of each index. The coding is as follows:

[0062] [C0 = (Ex0, En0, He0), C1 = (Ex1, En1, He1),..., C8 = (Ex8, En8, He8)]

[0063] (8) The aggregation algorithm constructs a floating cloud model. Form a comprehensive floating cloud from multiple cloud models, and aggregate the opinions of multiple evaluation subjects into the decision-making. Group experts describe and judge the relative importance of risk factors according to the language set of the cloud model. According to the expert weight λ i Aggregate the evaluation cloud models to the basic cloud to generate a floating cloud model and obtain the interaction matrix coding. Aggregate according to step (9).

[0064]

[0065] (10) The coding rule of the index interaction matrix determines the symmetry of the coding on the diagonal of the coding area. It is expressed as:

[0066] (11) Aggregate the row coding of the index interaction matrix to obtain the influence C of a certain index on the system in the RES theory i , and the coding rules of the three characteristic values are as follows:

[0067]

[0068] (12) Aggregate the column coding of the index interaction matrix to obtain the influence E of the system on a certain index in the RES theory i , and the coding rules of the three characteristic values are as follows:

[0069]

[0070]

[0071] (13) The C + E value of each index represents the expression of the interaction intensity between indexes and between indexes and the system, and is used to calculate the weight of the index in the evaluation method of the scour resistance of bridge piers. The coding is as follows:

[0072]

[0073] (14) For a bridge with measured values of a certain collection index, the membership degrees corresponding to the anti-scouring ability levels of each pier foundation are summarized as follows:

[0074]

[0075] (15) Adopting the principle of maximum membership degree, the level where the membership degree is the largest is the level to which the anti-scouring ability of the pier foundation belongs.

[0076] Example Two

[0077] As Figure 1 shown, the method for evaluating the anti-scouring ability of the 15# pier foundation of a certain cross-river bridge according to the embodiment of the present invention includes the following steps:

[0078] 101. Determine 13 evaluation indicators.

[0079] 102. Encode the characteristic values of the cloud models of each index level.

[0080] 103. According to the improved AHP, encode the evaluation characteristic matrix of the relative importance of the indicators.

[0081] 104. Aggregate the matrix to generate a floating cloud model.

[0082] 105. According to RES, encode and output the index weights.

[0083] 106. Output the membership degrees of the evaluation levels of each multi-dimensional cloud model corresponding to the project.

[0084] 107. Traverse the membership degrees to complete the determination of the evaluation level of the anti-scouring ability of the pier foundation.

[0085] Through the above technical solutions, the present invention first determines 13 evaluation indicators according to the current specifications, scouring action mechanism and engineering reports, as Figure 2 shown.

[0086] Furthermore, each index is quantitatively graded, the grading boundaries of each index are set, and an index grading cloud model matrix is constructed.

[0087] According to Figure 3 the importance scale shown, a basic evaluation characteristic matrix of the relative importance of the indicators is formed, the floating cloud models of experts are aggregated, and a final evaluation matrix is constructed.

[0088] Furthermore, according to Figure 4The RES coding principle shown above aggregates row coding and column coding and outputs the index weights. The actual engineering data is normalized and input into each comment set cloud model, and the membership degrees of the corresponding multi-dimensional cloud model evaluation levels of this project are output. The membership degree of the multi-dimensional cloud model corresponding to the anti-scouring ability of Pier 15 at level III is as follows Figure 5 as shown.

[0089] Furthermore, by comparing the membership degree cloud models of each classification, the evaluation level of the anti-scouring ability of the pier foundation is determined according to the principle of maximum membership degree.

[0090] For the evaluation result of Pier 15, through the interaction between multi-dimensional indicators, it is shown that the membership degree for level III is 0.7806 and the membership degree for level IV is 0.8117. According to the principle of maximum membership degree, the disaster prevention ability level of Pier 15 should belong to level IV. However, since the membership degrees of both level III and level IV are close to 0.8, it means that the randomness and fuzziness of the actual structure's disaster prevention ability under complex service conditions can be better shown in the multi-dimensional system. This is consistent with the result described in the actual engineering report that Pier 15 is in a dangerous state of damage, indicating that the specific disaster prevention ability of the pier foundation during service is affected by the combined action of multiple factors, optimizing the single evaluation result of the disaster prevention state in the existing evaluation method and making the description of the actual situation more realistic.

[0091] The present invention establishes an improved AHP-RES interaction matrix, comprehensively considering the influence of each index on the system and the action received by the system. There is an interactive linkage relationship between the indexes and between the indexes and the system, rather than a single action and reaction relationship. It reflects the complexity within the system, as well as the randomness and fuzziness that the membership level of the anti-scouring ability of the pier foundation should have under specific working conditions.

[0092] The method of the present invention first screens and quantifies 13 key evaluation indexes based on current specifications, scouring mechanisms, and engineering practices, improves AHP coding, and constructs an evaluation feature matrix of the relative importance of indexes by aggregating floating cloud models. Subsequently, the indexes are quantitatively graded according to the grading interval, the index weights are calculated by RES coding aggregation, and the normalized engineering data is input into the multi-dimensional cloud model to obtain the membership degree distribution of each evaluation level. Finally, according to the principle of maximum membership degree, the anti-scouring ability level of the pier foundation is determined, improving the scientificity and robustness of the evaluation result and providing an efficient and reliable decision-making method for the field of bridge safety evaluation.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multidimensional cloud model, characterized in that: include: Construct evaluation indicators; Encoding the digital eigenvalues ​​of the multidimensional cloud model based on the evaluation index; Constructing an indicator relative importance evaluation characteristic matrix based on the digital characteristic values ​​of the multidimensional cloud model; Constructing an expert floating cloud model based on the relative importance evaluation feature matrix of the indicators; Based on the expert floating cloud model, RES coding is used to output indicator weights; Output the degree of membership of the project corresponding to each multi-dimensional cloud model evaluation level based on the indicator weight; The evaluation grade of the scour resistance of the pier foundation is determined based on the membership degree.

2. The method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multidimensional cloud model according to claim 1 is characterized in that: The process of constructing an indicator relative importance evaluation characteristic matrix based on the digital characteristic values ​​of the multidimensional cloud model includes: Quantify and grade each evaluation indicator, set the grade boundaries, build the indicator grade cloud model matrix, and determine the interval expectation and variance; Generate normal random numbers and multidimensional normal random numbers based on the interval expectation and variance; Setting the number of cloud droplets to be generated, cyclically generating cloud droplets based on the normal random number and the multidimensional normal random number, and stopping the cycle when the number of cloud droplets to be generated is reached; The generated cloud droplets are retained to form a multi-dimensional cloud model; The feature cloud model is encoded according to the AHP scale; The feature cloud models are aggregated according to expert weights to form the expert floating cloud model.

3. The method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multidimensional cloud model according to claim 2 is characterized in that: The method for cyclically generating cloud droplets based on the normal random number and the multi-dimensional normal random number is as follows: Among them, ((x1,x2,…,x n ),μ i ) is a cloud droplet, which is a random realization of the concept using the cloud model on multi-dimensional values, (x1, x2, …, x n ) is the value of each dimension, μ i is the membership degree of the point corresponding to the i-th cloud model.

4. The method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multi-dimensional cloud model according to claim 2 is characterized in that: The method of encoding the feature cloud model according to the AHP scale is as follows: [C0=(Ex0,En0,He0), C1=(Ex1,En1,He1),...,C8=(Ex8,En8,He8)]. Among them, (Ex0, En0, He0)…(Ex8, En8, He8) respectively represent the relative importance of the evaluation indicators represented by the 9 AHP scales.

5. The method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multi-dimensional cloud model according to claim 2 is characterized in that: The method of aggregating the feature cloud models according to the expert weights is as follows: Among them, Ex i represents the expectation of the i-th evaluation index, En i represents the entropy of the i-th evaluation index, He i Represents the super entropy of the i-th evaluation index.

6. The method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multi-dimensional cloud model according to claim 1 is characterized in that: The process of using RES coding to output indicator weights includes: Performing row coding on the expert floating cloud model to obtain the impact of the evaluation index on the system; Column encoding is performed on the expert floating cloud model to obtain the impact of the system on the evaluation index; The weight of the evaluation index is calculated based on the interaction between the influence of the evaluation index on the system and the influence of the system on the evaluation index.

7. According to the bridge pier anti-scour capacity evaluation method based on RES-AHP multi-dimensional cloud model according to claim 1, the method for calculating the weight of the evaluation index is as follows: in, ω i represents the weight of the evaluation index, C i Indicates the impact of the evaluation index on the system, E i Indicates the impact of the system on the evaluation index.

8. The method for evaluating the anti-scour capacity of bridge piers based on the RES-AHP multi-dimensional cloud model according to claim 1 is characterized in that: The method of outputting the degree of membership of the project corresponding to each multi-dimensional cloud model evaluation level based on the indicator weight is as follows: Among them, μ(x1,x2,…,x n ) represents the membership degree of the evaluation index, x i represents the i-th evaluation index, ω i Indicates the weight of the evaluation index, Ex i It indicates the expectation of this level when constructing the i-th level cloud model. ki ' is subject to En'(En1',En2',…,En n ')~(Ex(Ex1,Ex2,…,Ex n ), He(He1,He2,…,He n ) 2 ) distributed normal random number.

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