A bridge evaluation method and device based on multi-attribute utility and a storage medium

Abstract

This invention discloses a bridge evaluation method, apparatus, and storage medium based on multi-attribute utility. When determining the weight value of each attribute of the target bridge, the method takes into account the objective attribute value of each attribute itself and the expert's scoring suggestions, thus solving the problem that existing methods for evaluating bridge performance based on multi-attribute utility result in overly subjective evaluation results because they only determine the weight values ​​of each attribute of the bridge through expert scoring.

Description

Technical Field

[0001] This invention relates to the field of bridge comprehensive performance evaluation technology, and in particular to a bridge evaluation method, device and storage medium based on multi-attribute utility. Background Technology

[0002] Bridge performance assessment provides a crucial theoretical foundation and decision-making basis for urban infrastructure construction and maintenance. In existing research in my country, most scholars focus on the assessment and development of the bridge's own condition, and have conducted numerous studies. Traditional bridge management decision-makers use the Analytic Hierarchy Process (AHP) to decompose the assessment object into multiple indicator systems, assigning different weights to each indicator for technical condition assessment. This type of method is generally considered a constant-weight method, meaning that the weights of each indicator and component remain unchanged during the operational period. It is widely used due to its simplicity and convenience. However, the constant-weight method cannot reflect the phenomenon of external factors influencing the weights of bridge component technical conditions. Since the beginning of this century, domestic scholars have begun to explore variable-weight methods for calculating component weights, such as the factor-based variable-weight method and the time-based variable-weight method. The factor-based variable-weight method uses local defects of components as influencing factors, while the time-based variable-weight method considers the changes in the importance of components during the operational period, and is used for technical condition assessment at different times.

[0003] Furthermore, because bridges are vital engineering projects related to the coordinated development of society and the economy, in some European and American countries, bridge performance, maintenance costs, social effects, and environmental factors have been applied to the comprehensive performance evaluation of bridges using multi-attribute utility theory. For example, scholars in the Netherlands have analyzed the multi-attribute utility of the usage status of all bridges in the Netherlands using factors such as bridge condition, maintenance costs, environmental factors, and indirect costs caused by maintenance. American scholars have included factors such as life-cycle costs and disaster severity in their evaluation indicators, and combined this with multi-attribute utility theory to conduct corresponding research on the comprehensive performance evaluation and maintenance decisions of bridges.

[0004] However, existing methods for evaluating bridge performance based on multi-attribute utility typically determine the weight values ​​of each bridge attribute through expert scoring, and then perform weighted fusion of all attributes to obtain the evaluation result of bridge performance. Such evaluation methods have a certain degree of subjectivity and lack reflection of objective facts.

[0005] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a bridge evaluation method, device and storage medium based on multi-attribute utility, which addresses the above-mentioned defects of the prior art. The aim is to solve the problem that the existing methods for evaluating bridge performance based on multi-attribute utility are too subjective in their evaluation results because they only determine the weight values ​​of each attribute of the bridge through expert scoring.

[0007] The technical solution adopted by this invention to solve the problem is as follows:

[0008] In a first aspect, embodiments of the present invention provide a bridge evaluation method based on multi-attribute utility, wherein the method includes:

[0009] Acquire several attribute values ​​corresponding to the target bridge, where each attribute value is used to reflect a performance information of the target bridge;

[0010] Obtain the effect values ​​corresponding to the several attribute values ​​respectively, and determine the objective weight values ​​corresponding to the several attribute values ​​respectively based on the effect values ​​corresponding to the several attribute values ​​respectively;

[0011] Obtain expert rating information, and determine the subjective weight values ​​corresponding to the several attribute values ​​based on the expert rating information;

[0012] Based on the objective weight values ​​and subjective weight values ​​corresponding to the attribute values, determine the comprehensive weight values ​​corresponding to the attribute values.

[0013] The evaluation level of the target bridge is determined based on the effect value corresponding to each of the attribute values ​​and the comprehensive weight value corresponding to each of the attribute values.

[0014] In one implementation, obtaining several attribute values ​​corresponding to the target bridge includes:

[0015] Obtain the bridge condition index corresponding to the target bridge, and use the bridge condition index as the bridge condition attribute value corresponding to the target bridge.

[0016] Obtain the cost information corresponding to the target bridge, and determine the bridge cost attribute value corresponding to the target bridge based on the cost information;

[0017] The sustainability information corresponding to the target bridge is obtained, and the bridge sustainability attribute value corresponding to the target bridge is determined based on the sustainability information; the sustainability information is used to reflect the relationship between the target bridge and the economy, society and environment.

[0018] The bridge condition attribute value, the bridge cost attribute value, and the bridge sustainability attribute value are used as the several attribute values.

[0019] In one implementation, obtaining the effect values ​​corresponding to the plurality of attribute values ​​respectively, and determining the objective weight values ​​corresponding to the plurality of attribute values ​​respectively based on the effect values ​​corresponding to the plurality of attribute values ​​respectively, includes:

[0020] Each of the several attribute values ​​is input into the effect function corresponding to each attribute value to obtain the effect value corresponding to each attribute value.

[0021] Based on the effect value, determine the information entropy corresponding to each attribute value;

[0022] Based on the information entropy, determine the objective weight value corresponding to each attribute value.

[0023] In one implementation, determining the information entropy corresponding to each attribute value based on the effect value includes:

[0024] Based on the effect value, determine the digital signal corresponding to each attribute value;

[0025] Based on the digital signal, determine the information entropy corresponding to each attribute value.

[0026] In one implementation, determining the subjective weight values ​​corresponding to the plurality of attribute values ​​based on the expert rating information includes:

[0027] Determine the pairwise comparison matrix based on the expert rating information;

[0028] Obtain the eigenvector corresponding to the largest eigenvalue in the pairwise comparison matrix to obtain the weight vector;

[0029] The subjective weight values ​​corresponding to the several attribute values ​​are determined based on the weight vector.

[0030] In one implementation, determining the subjective weight values ​​corresponding to the plurality of attribute values ​​based on the weight vector includes:

[0031] Obtain the order position corresponding to each of the several attribute values;

[0032] In the weight vector, the target real number corresponding to each attribute value is determined according to the order position;

[0033] The value of the target real number is used as the subjective weight value corresponding to each attribute value.

[0034] In one implementation, determining the evaluation level of the target bridge based on the effect values ​​corresponding to the plurality of attribute values ​​and the comprehensive weight values ​​corresponding to the plurality of attribute values ​​includes:

[0035] Obtain the target Sigmoid membership function, and input the effect values ​​corresponding to the several attribute values ​​into the target Sigmoid membership function to obtain the membership matrix;

[0036] The evaluation level of the target bridge is determined based on the comprehensive weight value corresponding to the aforementioned attribute values ​​and the membership matrix.

[0037] In one implementation, determining the evaluation level of the target bridge based on the comprehensive weight values ​​corresponding to the plurality of attribute values ​​and the membership matrix includes:

[0038] The elements in the membership matrix are weighted according to the comprehensive weight values ​​corresponding to the attribute values ​​to obtain a weighted membership vector.

[0039] The maximum membership degree is obtained by finding the real number with the largest value in the weighted membership vector.

[0040] The evaluation level corresponding to the target bridge is determined based on the maximum membership degree.

[0041] Secondly, embodiments of the present invention also provide a bridge evaluation device based on multi-attribute utility, wherein the device includes:

[0042] The attribute value acquisition module is used to acquire several attribute values ​​corresponding to the target bridge, and each of the several attribute values ​​is used to reflect a performance information of the target bridge.

[0043] An objective weight determination module is used to obtain the effect values ​​corresponding to the plurality of attribute values ​​respectively, and to determine the objective weight values ​​corresponding to the plurality of attribute values ​​respectively based on the effect values ​​corresponding to the plurality of attribute values ​​respectively.

[0044] The subjective weight determination module is used to obtain expert scoring information and determine the subjective weight values ​​corresponding to the several attribute values ​​based on the expert scoring information.

[0045] The comprehensive weight determination module is used to determine the comprehensive weight value corresponding to each of the several attribute values ​​based on the objective weight value corresponding to each of the several attribute values ​​and the subjective weight value corresponding to each of the several attribute values.

[0046] The bridge evaluation module is used to determine the evaluation level of the target bridge based on the effect values ​​corresponding to the attribute values ​​and the comprehensive weight values ​​corresponding to the attribute values.

[0047] Thirdly, embodiments of the present invention also provide a terminal, wherein the terminal includes a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the bridge evaluation method based on multi-attribute utility as described above; and the processor is used to execute the programs.

[0048] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a plurality of instructions, wherein the instructions are loaded and executed by a processor to implement the steps of any of the bridge evaluation methods based on multi-attribute utility described above.

[0049] The beneficial effects of this invention are as follows: This embodiment of the invention obtains several attribute values ​​corresponding to a target bridge, each of which reflects a performance information of the target bridge; based on the effect values ​​corresponding to each of the obtained attribute values, it determines the objective weight values ​​corresponding to each of the attribute values; based on the obtained expert rating information, it determines the subjective weight values ​​corresponding to each of the attribute values; based on the objective weight values ​​and subjective weight values ​​corresponding to each of the attribute values, it determines the comprehensive weight value corresponding to each of the attribute values; and based on the effect values ​​and comprehensive weight values ​​corresponding to each of the attribute values, it determines the evaluation level corresponding to the target bridge. In determining the weight value of each attribute of the target bridge, this method considers both the objective attribute value of each attribute and the expert rating suggestions, thus solving the problem that existing methods for evaluating bridge performance based on multi-attribute utility, which rely solely on expert ratings to determine the weight values ​​of each attribute, result in overly subjective evaluation results. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating the bridge evaluation method based on multi-attribute utility provided in an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of the process for obtaining the weighted membership vector provided in an embodiment of the present invention.

[0053] Figure 3 This is a schematic diagram showing the situations corresponding to the maximum and minimum of the utility function provided in the embodiments of the present invention.

[0054] Figure 4 This is a schematic diagram of risk preference provided in an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of the Sigmoid function in different directions provided in the embodiments of the present invention.

[0056] Figure 6 This is the distribution of the mean values ​​of each attribute in the evaluation results of 1000 bridge samples provided in this embodiment of the invention.

[0057] Figure 7 This is a schematic diagram of the internal module connections of the bridge evaluation device based on multi-attribute utility provided in an embodiment of the present invention.

[0058] Figure 8 This is a schematic diagram of the terminal provided in the embodiment of the present invention.

[0059] Figure 9 This is a diagram showing the results of evaluating 1000 bridge samples according to an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0062] Bridge performance assessment provides a crucial theoretical foundation and decision-making basis for urban infrastructure construction and maintenance. In existing research in my country, most scholars focus on the assessment and development of the bridge's own condition, and have conducted numerous studies. Traditional bridge management decision-makers use the Analytic Hierarchy Process (AHP) to decompose the assessment object into multiple indicator systems, assigning different weights to each indicator for technical condition assessment. This type of method is generally considered a constant-weight method, meaning that the weights of each indicator and component remain unchanged during the operational period. It is widely used due to its simplicity and convenience. However, the constant-weight method cannot reflect the phenomenon of external factors influencing the weights of bridge component technical conditions. Since the beginning of this century, domestic scholars have begun to explore variable-weight methods for calculating component weights, such as the factor-based variable-weight method and the time-based variable-weight method. The factor-based variable-weight method uses local defects of components as influencing factors, while the time-based variable-weight method considers the changes in the importance of components during the operational period, and is used for technical condition assessment at different times.

[0063] Furthermore, because bridges are vital engineering projects related to the coordinated development of society and the economy, in some European and American countries, bridge performance, maintenance costs, social effects, and environmental factors have been applied to the comprehensive performance evaluation of bridges using multi-attribute utility theory. For example, scholars in the Netherlands have analyzed the multi-attribute utility of the usage status of all bridges in the Netherlands using factors such as bridge condition, maintenance costs, environmental factors, and indirect costs caused by maintenance. American scholars have included factors such as life-cycle costs and disaster severity in their evaluation indicators, and combined this with multi-attribute utility theory to conduct corresponding research on the comprehensive performance evaluation and maintenance decisions of bridges.

[0064] However, existing methods for evaluating bridge performance based on multi-attribute utility typically determine the weight values ​​of each bridge attribute through expert scoring, and then perform weighted fusion of all attributes to obtain the evaluation result of bridge performance. Such evaluation methods have a certain degree of subjectivity and lack reflection of objective facts.

[0065] To address the aforementioned shortcomings of existing technologies, this invention provides a bridge evaluation method based on multi-attribute utility. This method involves acquiring several attribute values ​​corresponding to a target bridge, each attribute value reflecting a performance characteristic of the target bridge. Based on the effect values ​​corresponding to each of the acquired attribute values, objective weight values ​​are determined for each attribute value. Subjective weight values ​​are determined based on expert ratings. A comprehensive weight value is determined for each attribute value based on both the objective and subjective weight values. Finally, the evaluation level of the target bridge is determined based on the effect values ​​and the comprehensive weight value of each attribute value. This method considers both the objective attribute value and expert ratings when determining the weight value of each attribute of the target bridge, thus solving the problem that existing multi-attribute utility-based bridge performance evaluation methods, which rely solely on expert ratings to determine the weight values ​​of each attribute, result in overly subjective evaluation results.

[0066] like Figure 1 As shown, the method includes the following steps:

[0067] Step S100: Obtain several attribute values ​​corresponding to the target bridge. Each of the several attribute values ​​is used to reflect a performance information of the target bridge.

[0068] Specifically, this embodiment uses the bridge requiring performance evaluation as the target bridge. To evaluate the overall performance of the target bridge, this embodiment needs to obtain multiple attribute values ​​for the target bridge. Each attribute value corresponds to a specific performance information of the target bridge, and different attribute values ​​correspond to different performance information. For example, this embodiment can determine attribute value A based on the economic utility of the target bridge, attribute value B based on the social utility of the target bridge, and attribute value C based on the environmental utility of the target bridge. Then, attribute values ​​A, B, and C correspond to the economic performance, social performance, and environmental performance of the target bridge, respectively.

[0069] In one implementation, step S100 specifically includes the following steps:

[0070] Step S101: Obtain the bridge condition index corresponding to the target bridge, and use the bridge condition index as the bridge condition attribute value corresponding to the target bridge.

[0071] Step S102: Obtain the cost information corresponding to the target bridge, and determine the bridge cost attribute value corresponding to the target bridge based on the cost information;

[0072] Step S103: Obtain the sustainability information corresponding to the target bridge, and determine the bridge sustainability attribute value corresponding to the target bridge based on the sustainability information;

[0073] Step S104: Use the bridge condition attribute value, the bridge cost attribute value, and the bridge sustainability attribute value as the several attribute values.

[0074] The Bridge Condition Index (BCI) characterizes the integrity of a bridge structure. Based on the detection of bridge damage, it employs a stratified weighted method, first assessing the bridge deck system, superstructure, and substructure separately, and then synthesizing the results to obtain an overall assessment of the bridge's technical condition. Specifically, in this embodiment, the Bridge Condition Index of the target bridge is used as the bridge condition attribute value, which reflects the structural performance of the target bridge. Cost information refers to the total life-cycle cost of the target bridge, reflecting the user's maintenance costs, such as routine maintenance costs, inspection costs, repair costs, and potential failure losses. Therefore, this embodiment determines the bridge cost attribute value of the target bridge based on this cost information, which reflects the maintenance costs incurred by the target bridge throughout its entire service life. Sustainability information reflects the relationship between the target bridge and the economy, society, and environment. Sustainability information can include economic sustainability, environmental sustainability, and social sustainability. Economic sustainability primarily refers to the reconstruction and maintenance costs of bridges under disaster or special load scenarios, and can generally be considered a function of the overall bridge construction cost. Environmental sustainability mainly reflects energy loss, the greenhouse effect, and air pollution; in practical calculations, energy loss and carbon emissions are primarily considered as influencing factors. Social sustainability information is generally expressed as the detour time caused by traffic disruptions. Therefore, based on this sustainability information, the sustainability attribute value of the target bridge can be determined, which reflects the status of various indicators for the long-term development of the target bridge.

[0075] In one implementation, the Bridge Condition Index (BCI) is calculated using the following formula:

[0076] BCI = BCI m ·w m +BCI s ·w s +BCI x ·w x

[0077] Among them, BCI m BCI s and BCI w These are the individual index values ​​for the bridge deck system, superstructure, and substructure, respectively. m w s and w w The weights are for the bridge deck system, superstructure, and substructure.

[0078] In one implementation, the bridge cost attribute value CET is calculated using the following formula:

[0079] C ET =C T +C PM +C INS +C REP +C F

[0080] Among them, C T For the initial cost, C PM This refers to routine maintenance costs, C INS For testing fees, C REP For repair costs, C F This represents potential failure losses.

[0081] In one implementation, the bridge sustainability attribute value u s The following formula is used to calculate:

[0082] u s =k Eco u Eco +k Sco u Sco +k Env u Env

[0083] Where: k Eco k Sco and k Env These are the weights for economic, social, and environmental indicators, respectively; u Eco u Sco and u Env These are the effect functions of economic, social, and environmental indicators, respectively.

[0084] like Figure 1 As shown, the method further includes the following steps:

[0085] Step S200: Obtain the effect values ​​corresponding to the several attribute values ​​respectively, and determine the objective weight values ​​corresponding to the several attribute values ​​respectively based on the effect values ​​corresponding to the several attribute values ​​respectively.

[0086] Since the units and dimensions of each attribute value are not uniform, it is difficult to directly determine the corresponding objective weight value using the numerical value of each attribute value. Therefore, this embodiment first needs to obtain the effect value corresponding to each attribute value. All effect values ​​corresponding to each attribute value have the same unit and dimension. Therefore, the effect values ​​of each attribute value can be compared on the same platform. Only in this way can the objective weight values ​​of each attribute value be determined in a practical and reliable manner.

[0087] In one implementation, step S200 specifically includes the following steps:

[0088] Step S201: Input each of the several attribute values ​​into the effect function corresponding to each attribute value to obtain the effect value corresponding to each attribute value;

[0089] Step S202: Determine the information entropy corresponding to each attribute value based on the effect value;

[0090] Step S203: Determine the objective weight value corresponding to each attribute value based on the information entropy.

[0091] This embodiment requires determining the objective weight value corresponding to each attribute value. Since the determination method is the same, this embodiment takes determining the objective weight value of one attribute value as an example. Specifically, this embodiment first needs to determine the effect function of each attribute value. Specifically, this embodiment first uses a lottery betting method to determine the key parameters in the effect function, that is, the situations corresponding to the maximum and minimum of the effect function, such as... Figure 3 U(x) shown min )=0,U(x max ) = 1, and calculate the corresponding situation for the expected value EV of the effect function, EV = 0.5 * x min +0.5*x max ; The decision-maker gives a definite equivalent value CE with a 50 / 50 probability of maximum and minimum expected risk, and then combines it with Figure 4 The risk preference shown can be used to obtain the risk attitude RT value, and finally the effect function corresponding to the attribute value. Then, the effect value corresponding to the attribute value can be calculated based on the effect function corresponding to the attribute value.

[0092] For example: For any single attribute value, assume it is in the form of an exponential function:

[0093]

[0094] The values ​​for each parameter are as follows:

[0095]

[0096]

[0097]

[0098] Where: U is the effect value when the value of a single indicator is x; A and B are scaling constants; Min(x) i ) and Max(x i ) represent the indicator values ​​at which the effect of a single indicator is minimized and maximized, respectively; RT (Risk Tolerance) represents risk attitude. CE represents the decision-maker's definite equivalent value for the expected maximum and minimum probability of risk being roughly equal. RT is the risk attitude value.

[0099] Then, based on the effect value corresponding to the attribute value, the information entropy corresponding to that attribute value is generated. This information entropy reflects the amount of information contained in the attribute value. Finally, the objective weight value corresponding to the attribute value is determined using this information entropy. In this embodiment, the amount of information corresponding to each attribute value can be compared using the information entropy of each attribute value, and the objective weight corresponding to each attribute value can be fairly determined using the information entropy of each attribute value.

[0100] In one implementation, step S202 specifically includes the following steps:

[0101] Step S2021: Determine the digital signal corresponding to each attribute value based on the effect value;

[0102] Step S2022: Determine the information entropy corresponding to each attribute value based on the digital signal.

[0103] Specifically, for a given attribute value, this embodiment first needs to standardize the effect value corresponding to that attribute value to obtain standard data. Then, based on the standard data, a digital signal corresponding to the attribute value is generated. Next, the digital signal is converted into digital signal entropy according to the formula for calculating information entropy. This digital signal entropy reflects the amount of information contained in the digital signal. Since digital signal is a very abstract concept, this embodiment uses information entropy instead of digital signal. This information entropy can reflect the amount of information contained in the digital signal, that is, it quantizes the digital signal.

[0104] In one implementation, the formula for calculating information entropy is as follows: for the information entropy of the i-th index:

[0105]

[0106]

[0107] Where n is the total number of samples.

[0108] In one implementation, for a given attribute value, this embodiment can determine the objective weight value corresponding to the attribute value based on the information entropy corresponding to the attribute value using the following formula:

[0109]

[0110] like Figure 1 As shown, the method further includes the following steps:

[0111] Step S300: Determine the subjective weight values ​​corresponding to the several attribute values ​​based on the expert scoring information.

[0112] Specifically, since the importance of each attribute value varies in the minds of expert decision-makers, this application also calculates the subjective weight value corresponding to each attribute value in order to fully consider the subjective intentions of expert decision-makers. These subjective weight values ​​are usually obtained by expert decision-makers based on their experience and subjective judgment, and can reflect the degree of importance that decision-makers subjectively attach to each attribute value.

[0113] In one implementation, step S300 specifically includes the following steps:

[0114] Step S301: Determine the pairwise comparison matrix based on the expert scoring information;

[0115] Step S302: Obtain the eigenvector corresponding to the largest eigenvalue in the pairwise comparison matrix to obtain the weight vector;

[0116] Step S303: Determine the subjective weight values ​​corresponding to the several attribute values ​​according to the weight vector.

[0117] Specifically, this embodiment primarily employs the Analytic Hierarchy Process (AHP) to determine the subjective weight values ​​corresponding to each attribute value. The AHP mainly refers to a decision-making method that decomposes elements relevant to decision-making into levels such as objectives, criteria, and solutions, and then performs qualitative and quantitative analysis based on this. The pairwise comparison matrix serves as the quantitative basis for the AHP, and it is constructed from information provided by experienced and discerning experts. Each column vector of this pairwise comparison matrix is ​​an eigenvector. To accurately determine the subjective weight values ​​corresponding to each attribute value, this embodiment selects the eigenvector corresponding to the largest eigenvalue in the pairwise comparison matrix. This eigenvector contains multiple real numbers, each corresponding to a subjective weight value of an attribute value. Therefore, the subjective weight values ​​corresponding to each attribute value can be obtained based on this eigenvector, where the sum of the subjective weight values ​​of all attribute values ​​is 1.

[0118] In one implementation, step S303 specifically includes the following steps:

[0119] Step S3031: Obtain the sequence position corresponding to each of the plurality of attribute values;

[0120] Step S3032: In the weight vector, determine the target real number corresponding to each attribute value according to the order position;

[0121] Step S3033: Use the value of the target real number as the subjective weight value corresponding to each attribute value.

[0122] Specifically, taking the determination of a subjective weight value for an attribute as an example, since each real number in the selected feature vector has a corresponding index, and this index corresponds to the order in which the real numbers are arranged in the feature vector, and there is also a certain order among the attribute values, this embodiment can determine the real number located in the same position in the feature vector based on the index and the order among the attribute values, and use the value of this real number as the subjective weight value of the attribute value located in the same position.

[0123] like Figure 1 As shown, the method further includes the following steps:

[0124] Step S400: Determine the comprehensive weight value corresponding to each of the several attribute values ​​based on the objective weight value and the subjective weight value corresponding to each of the several attribute values.

[0125] Specifically, since the objective weight values ​​of each attribute are formed based on the actual data of each attribute in the decision-making scheme, meaning that the original information they contain comes from the objective environment, they have strong objectivity. The subjective weight values ​​of each attribute, on the other hand, are reasonably determined by expert decision-makers based on the actual decision problem and their own knowledge and experience, which can avoid situations where the weight values ​​of attributes contradict their actual importance. Therefore, in order to determine the weight values ​​corresponding to each attribute more reasonably and objectively, this embodiment integrates the objective and subjective weight values ​​of each attribute to obtain a comprehensive weight value for each attribute. This comprehensive weight value can reflect both the amount of information contained in the original information of each attribute and the preferences of the expert decision-makers. There are various integration methods, such as directly using a summation method or setting coefficients for the objective and subjective weight values ​​separately according to the decision-makers' preferences, multiplying the objective and subjective weight values ​​by their respective coefficients, and then summing them.

[0126] In one implementation, step S400 specifically includes the following steps:

[0127] Step S401: For each of the several attribute values, input the subjective weight value and the objective weight value corresponding to the attribute value into a preset formula;

[0128] Step S401: Obtain the value output by the preset formula based on the subjective weight value corresponding to the attribute value and the objective weight value corresponding to the attribute value, and use the value as the comprehensive weight value corresponding to the attribute value.

[0129] Specifically, this embodiment requires merging the objective weight value determined by the entropy weight method and the subjective weight value determined by the analytic hierarchy process into a comprehensive weight value. This embodiment employs a preset formula to merge the objective and subjective weight values. In one implementation, the preset formula is:

[0130]

[0131] Where, α i β i These are the subjective weight value and the objective weight value, ω. i This is the overall weighted value.

[0132] like Figure 1 As shown, the method further includes the following steps:

[0133] Step S500: Determine the evaluation level of the target bridge based on the effect value corresponding to each of the attribute values ​​and the comprehensive weight value corresponding to each of the attribute values.

[0134] Specifically, since each attribute value can reflect a performance information of the target bridge, and the comprehensive weight value of each attribute value can reflect the importance of each attribute value, the excellent state of the important performance and the excellent state of the non-important performance of the target bridge can be determined according to the comprehensive weight value of each attribute value. Based on this, the overall state of the target bridge can be comprehensively evaluated, and an evaluation level can be determined for the target bridge.

[0135] In one implementation, step S500 specifically includes the following steps:

[0136] Step S501: Obtain the target Sigmoid membership function. Input the effect values ​​corresponding to the several attribute values ​​into the target Sigmoid membership function to obtain the membership matrix.

[0137] Step S502: Determine the evaluation level of the target bridge based on the comprehensive weight value corresponding to the several attribute values ​​and the membership matrix.

[0138] This embodiment selects fuzzy comprehensive evaluation to classify the target bridge. Fuzzy comprehensive evaluation is a highly effective multi-factor decision-making method for comprehensively evaluating objects influenced by multiple factors. Membership degree is a concept in fuzzy evaluation functions, representing the degree of membership relationship. Specifically, this embodiment first needs to determine the target Sigmoid membership function to be used. In practical applications, the expression of the Sigmoid membership function is as follows:

[0139]

[0140] In this function, the value of 'a' determines the Sigmoid membership function, and 'c' is the boundary between two adjacent categories (for example, 'Excellent' represents scores of 80 to 100, and 'Good' refers to scores of 60 to 80; here, the boundary between 'Excellent' and 'Good' is 80). The distance between 'a' and 'c' determines the slope of the function. When 'a' is less than 0 in the expression, it is a left-leaning Sigmoid membership function; otherwise, it is a right-leaning function (e.g., ...). Figure 5 As shown). For a bidirectional Sigmoid membership function, it is composed of two Sigmoid membership functions with different directions (such as...). Figure 5 (As shown).

[0141] Then, the effect values ​​corresponding to each attribute value are input as variables into the target Sigmoid membership function to obtain the membership matrix. This membership matrix can reflect the degree of membership of each attribute value to the categories of excellent, good, average, poor, and very poor. Then, the membership matrix is ​​combined with the comprehensive weight values ​​corresponding to each attribute value to determine the true category of each attribute value, and finally determine the evaluation level of the target bridge.

[0142] In one implementation, this embodiment can weight each element in the membership matrix according to the comprehensive weight value corresponding to the several attribute values ​​to obtain a weighted membership vector; obtain the real number with the largest value in the weighted membership vector to obtain the maximum membership degree; and determine the evaluation level corresponding to the target bridge according to the maximum membership degree.

[0143] like Figure 2 As shown, in order to combine the membership matrix with the comprehensive weight values ​​corresponding to each attribute value, this embodiment needs to multiply the weight vector, which is composed of the comprehensive weight values ​​corresponding to each attribute value, with each column element of the membership matrix to obtain a weighted membership vector. Since this weighted membership vector contains both objective information about each attribute value and the comprehensive weights generated based on the subjective opinions of expert decision-makers and objective information, using this weighted membership vector to rank the target bridge will produce a more accurate level.

[0144] For example, suppose the weighted membership vector is B, the weight vector formed by combining the comprehensive weight values ​​corresponding to each attribute value is W, and the membership matrix is ​​R, then we have:

[0145]

[0146] Then, the largest real number in the weighted membership vector B is determined to obtain the maximum membership degree, and the evaluation corresponding to the maximum membership degree is used as the evaluation level of the target bridge.

[0147] To illustrate the technical effects of this invention, the inventors selected the index parameter distribution of a certain transportation network and generated 1000 bridge samples using this distribution and parameters. These samples were then evaluated using the bridge evaluation method based on multi-attribute utility provided by this invention. Figure 9 As shown, the overall performance of bridges gradually decreases from Class I to Class V. Class I bridges have relatively high performance across all individual indicators, while Class V bridges have relatively low performance across all indicators. The indicators for the remaining bridge classes are distributed within the indicator utility space. Figure 6 It can be seen that, for bridges of the same grade, there are no significant differences in individual indicators. However, as the overall evaluation result of the bridge improves, the average value of each individual indicator also increases. This indicates that only by simultaneously strengthening all indicators can the overall performance of the bridge be improved. Therefore, the overall evaluation result of the bridge obtained using the method of this invention does not show obvious "indicator bias," meaning that an excessively large or small value of a single indicator will not cause the overall evaluation result to be seriously biased to one side.

[0148] Based on the above embodiments, the present invention also provides a bridge evaluation device based on multi-attribute utility, such as... Figure 7 As shown, the device includes:

[0149] The attribute value acquisition module 01 is used to acquire several attribute values ​​corresponding to the target bridge, and each of the several attribute values ​​is used to reflect a performance information of the target bridge.

[0150] Objective weight determination module 02 is used to obtain the effect values ​​corresponding to the several attribute values ​​respectively, and determine the objective weight values ​​corresponding to the several attribute values ​​respectively based on the effect values ​​corresponding to the several attribute values ​​respectively.

[0151] Subjective weight determination module 03 is used to obtain expert scoring information and determine the subjective weight values ​​corresponding to the several attribute values ​​based on the expert scoring information.

[0152] The comprehensive weight determination module 04 is used to determine the comprehensive weight value corresponding to the several attribute values ​​based on the objective weight value corresponding to the several attribute values ​​and the subjective weight value corresponding to the several attribute values ​​respectively.

[0153] Bridge evaluation module 05 is used to determine the evaluation level of the target bridge based on the effect values ​​corresponding to the aforementioned attribute values ​​and the comprehensive weight values ​​corresponding to the aforementioned attribute values.

[0154] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 8As shown, the terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a bridge evaluation method based on multi-attribute utility. The display screen can be a liquid crystal display (LCD) or an e-ink display.

[0155] Those skilled in the art will understand that Figure 8 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0156] In one implementation, the terminal's memory stores one or more programs, and these programs are configured to be executed by one or more processors, and the programs contain instructions for performing a bridge evaluation method based on multi-attribute utility.

[0157] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0158] In summary, this invention discloses a bridge evaluation method, apparatus, and storage medium based on multi-attribute utility. The method involves acquiring several attribute values ​​corresponding to a target bridge, each attribute value reflecting a performance information of the target bridge; determining objective weight values ​​for each attribute value based on its corresponding effect value; determining subjective weight values ​​for each attribute value based on obtained expert ratings; determining a comprehensive weight value for each attribute value based on both its objective and subjective weight values; and determining the evaluation level of the target bridge based on its corresponding effect value and comprehensive weight value. This method considers both the objective attribute value and expert ratings when determining the weight value of each attribute of the target bridge, thus solving the problem that existing methods for evaluating bridge performance based on multi-attribute utility, which rely solely on expert ratings to determine the weight values ​​of each attribute, result in overly subjective evaluation results.

[0159] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A bridge assessment method based on multi-attribute utility, characterized by, The method comprises: obtaining a plurality of attribute values corresponding to a target bridge, each attribute value of the plurality of attribute values being used to reflect a performance information of the target bridge, comprising: obtaining a bridge condition index corresponding to the target bridge, taking the bridge condition index as a bridge condition attribute value corresponding to the target bridge; obtaining overhead information corresponding to the target bridge, determining a bridge cost attribute value corresponding to the target bridge according to the overhead information; obtaining sustainability information corresponding to the target bridge, determining a bridge sustainability attribute value corresponding to the target bridge according to the sustainability information; the sustainability information is used to reflect the relationship between the target bridge and economy, society and environment; taking the bridge condition attribute value, the bridge cost attribute value and the bridge sustainability attribute value as the plurality of attribute values; obtaining effect values corresponding to the plurality of attribute values respectively, and determining objective weight values corresponding to the plurality of attribute values respectively according to the effect values corresponding to the plurality of attribute values respectively; obtaining expert score information, and determining subjective weight values corresponding to the plurality of attribute values respectively according to the expert score information; determining comprehensive weight values corresponding to the plurality of attribute values respectively according to the objective weight values corresponding to the plurality of attribute values respectively and the subjective weight values corresponding to the plurality of attribute values respectively; determining an evaluation grade corresponding to the target bridge according to the effect values corresponding to the plurality of attribute values respectively and the comprehensive weight values corresponding to the plurality of attribute values respectively, comprising: obtaining a target Sigmoid membership function, inputting the effect values corresponding to the plurality of attribute values respectively into the target Sigmoid membership function to obtain a membership matrix; determining the evaluation grade corresponding to the target bridge according to the comprehensive weight values corresponding to the plurality of attribute values respectively and the membership matrix.

2. The multi-attribute utility based bridge evaluation method according to claim 1, wherein, The method comprises: inputting each attribute value of the plurality of attribute values into an effect function corresponding to each attribute value to obtain an effect value corresponding to each attribute value; determining an information entropy corresponding to each attribute value according to the effect value; determining an objective weight value corresponding to each attribute value according to the information entropy.

3. The bridge assessment method based on multi-attribute utility according to claim 2, wherein, The method comprises: determining a digital signal corresponding to each attribute value according to the effect value; determining an information entropy corresponding to each attribute value according to the digital signal.

4. The multi-attribute utility based bridge evaluation method according to claim 1, wherein, The method comprises: determining a pair-wise comparison matrix according to the expert score information; obtaining a characteristic vector corresponding to a maximum eigenvalue in the pair-wise comparison matrix to obtain a weight vector; determining the subjective weight values corresponding to the plurality of attribute values respectively according to the weight vector.

5. The bridge assessment method based on multi-attribute utility according to claim 4, wherein, The method comprises: obtaining a sequence position corresponding to each attribute value of the plurality of attribute values; In the weight vector, a target real number corresponding to each attribute value is determined according to the order bit; The numerical value of the target real number is taken as a subjective weight value corresponding to each attribute value.

6. The multi-attribute utility based bridge evaluation method according to claim 1, wherein, The evaluation grade corresponding to the target bridge is determined according to the comprehensive weight value corresponding to each attribute value and the membership matrix, including: Each element in the membership matrix is weighted according to the comprehensive weight value corresponding to each attribute value to obtain a weighted membership vector; The maximum real number in the weighted membership vector is obtained to obtain the maximum membership degree; The evaluation grade corresponding to the target bridge is determined according to the maximum membership degree.

7. A bridge assessment device based on multi-attribute utility, characterized by, The device comprises: An attribute value acquisition module is configured to acquire a plurality of attribute values corresponding to a target bridge, each attribute value in the plurality of attribute values being used to reflect a performance information of the target bridge, including: acquiring a bridge condition index corresponding to the target bridge, taking the bridge condition index as a bridge condition attribute value corresponding to the target bridge; acquiring opening information corresponding to the target bridge, determining a bridge cost attribute value corresponding to the target bridge according to the opening information; acquiring sustainability information corresponding to the target bridge, determining a bridge sustainability attribute value corresponding to the target bridge according to the sustainability information; the sustainability information is used to reflect the relationship between the target bridge and economy, society and environment; and taking the bridge condition attribute value, the bridge cost attribute value and the bridge sustainability attribute value as the plurality of attribute values; An objective weight determination module is configured to acquire an effect value corresponding to each attribute value in the plurality of attribute values, and determine an objective weight value corresponding to each attribute value in the plurality of attribute values according to the effect value corresponding to each attribute value; A subjective weight determination module is configured to acquire expert score information, and determine a subjective weight value corresponding to each attribute value in the plurality of attribute values according to the expert score information; A comprehensive weight determination module is configured to determine a comprehensive weight value corresponding to each attribute value in the plurality of attribute values according to the objective weight value corresponding to each attribute value and the subjective weight value corresponding to each attribute value; A bridge evaluation module is configured to determine an evaluation grade corresponding to the target bridge according to the effect value corresponding to each attribute value and the comprehensive weight value corresponding to each attribute value, including: acquiring a target Sigmoid membership function, inputting the effect value corresponding to each attribute value into the target Sigmoid membership function to obtain a membership matrix; and determining the evaluation grade corresponding to the target bridge according to the comprehensive weight value corresponding to each attribute value and the membership matrix.

8. A computer-readable storage medium storing a plurality of instructions, characterized in that, The processor loads and executes the instructions to implement the steps of the bridge evaluation method based on multi-attribute utility according to any one of claims 1-6.

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