CN116681204A - Expressway bridge evaluation method and device based on multiple indexes and storage medium - Google Patents
Expressway bridge evaluation method and device based on multiple indexes and storage medium Download PDFInfo
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Abstract
The disclosure relates to a multi-index-based highway bridge evaluation method, a multi-index-based highway bridge evaluation device and a storage medium. The method comprises the following steps: obtaining basic data of a highway maintenance bridge; inputting the bridge technical condition annual score in the road maintenance bridge foundation data into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes; normalizing each bridge comprehensive evaluation index to generate a standardized matrix of the bridge comprehensive evaluation index; determining the combination weight of each bridge comprehensive evaluation index; and determining a bridge comprehensive evaluation result according to the standardized matrix and the combination weight. According to the bridge comprehensive evaluation method, the combination weight of each bridge comprehensive evaluation index is determined, and the bridge comprehensive evaluation result is determined according to the combination weight, so that a more real and accurate bridge comprehensive evaluation result can be obtained, and the accuracy of the bridge comprehensive evaluation result is improved.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to a multi-index-based highway bridge evaluation method, a multi-index-based highway bridge evaluation device and a storage medium.
Background
The comprehensive evaluation problem of the highway bridge has the characteristics of multiple indexes and multiple factors. According to different weight generation methods, the subjective weighting method and the objective weighting method are adopted, wherein the objectivity of the subjective weighting method is poor, and the attribute weight determined by the objective weighting method is sometimes contrary to the actual importance degree of the attribute. Both methods have drawbacks.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a multi-index-based highway bridge evaluation method, apparatus and storage medium, so as to solve the above problems.
According to a first aspect of embodiments of the present disclosure, there is provided a multi-index-based highway bridge evaluation method, including:
obtaining basic data of a highway maintenance bridge;
inputting the bridge technical condition annual score in the road maintenance bridge foundation data into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes;
normalizing each bridge comprehensive evaluation index to generate a standardized matrix of the bridge comprehensive evaluation index;
determining the combination weight of each bridge comprehensive evaluation index;
and determining a bridge comprehensive evaluation result according to the standardized matrix and the combination weight.
In one embodiment, the bridges are ranked according to the comprehensive bridge evaluation result.
In one embodiment, determining the combining weight of each bridge composite evaluation index includes:
generating a judgment matrix according to the collected expert opinion;
determining the maximum eigenvalue of the judgment matrix of the bridge comprehensive evaluation index;
determining a consistency check coefficient of the judgment matrix according to the maximum eigenvalue of the judgment matrix;
and responding to the consistency check coefficient of the judgment matrix to be smaller than a preset threshold value, and checking the judgment matrix to pass.
In one embodiment, determining the combining weight of each bridge composite evaluation index includes:
respectively adopting an analytic hierarchy process and an entropy weight process to determine a first weight vector and a second weight vector of each bridge comprehensive evaluation index;
determining the combined weight vector of each bridge comprehensive evaluation index according to the first weight vector and the second weight vector by adopting a combined weighting method;
and in the combined weight vector, the weight of each bridge comprehensive evaluation index is recorded.
In one embodiment, determining a first weight vector of each bridge composite evaluation index by using an analytic hierarchy process includes:
in the judgment matrix of the bridge comprehensive evaluation index, each row represents one index;
for any index, determining the weight of the index according to each element in the row corresponding to the index;
and generating the first weight vector according to the weight of each index.
In one embodiment, determining the weight of the index according to each element in the row corresponding to the index includes:
determining the n times square root of each element in the row corresponding to the index;
determining n times of square roots of each row to obtain a set of n times of square root values;
according to the sum value of the set of n times square roots;
determining the quotient of the n secondary root corresponding to the index and the sum value, wherein the quotient is the weight of the index;
where n is the number of elements in each row.
In one embodiment, the determining the second weight vector of each bridge comprehensive evaluation index by adopting the entropy weight method comprises the following steps:
determining the entropy value of each index; redundancy of entropy;
summing the entropy redundancy of each index to obtain an entropy redundancy sum value;
for any one index, determining the weight of the index according to the entropy redundancy of the index and the entropy redundancy sum value;
a weight vector is determined based on the weight of each indicator.
In a second aspect, the present application provides a comprehensive evaluation device for a highway bridge based on multiple indexes, including:
obtaining basic data of a highway maintenance bridge;
inputting the road maintenance bridge foundation data into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes;
determining the combination weight of each bridge comprehensive evaluation index;
and determining the bridge comprehensive evaluation result according to the combination weight of each bridge comprehensive evaluation index.
According to a third aspect of embodiments of the present disclosure, there is provided a multi-index-based highway bridge comprehensive evaluation device, including:
a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the above method.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the above-described method.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
according to the technical scheme, the combined weight of each bridge comprehensive evaluation index is determined, and then the bridge comprehensive evaluation result is determined according to the combined weight of each bridge comprehensive evaluation index, so that compared with a subjective weighting method and an objective weighting method in the related technology, more scientific weights can be adopted, more true and accurate bridge comprehensive evaluation results can be obtained, and the accuracy of the bridge comprehensive evaluation results is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a flow chart illustrating a multi-index based highway bridge comprehensive assessment method according to an exemplary embodiment;
FIG. 2 is a schematic diagram of a multi-index based highway bridge comprehensive assessment device according to an exemplary embodiment;
fig. 3 is a schematic structural view of another multi-index-based highway bridge comprehensive evaluation device according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be noted that, all actions of acquiring signals, information or data in the present application are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
The application provides a comprehensive evaluation method of a highway bridge based on multiple indexes, which is shown in a flow chart of the comprehensive evaluation method of the highway bridge shown in figure 1; the method may comprise the steps of:
in step S102, obtaining basic data of a highway maintenance bridge;
wherein, the basic data of the highway maintenance bridge are shown in table 1.
TABLE 1
In step S104, the bridge technical condition annual score in the road maintenance bridge foundation data is input into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes.
In this embodiment, the bridge technical condition prediction model may be a model in the prior art, for example, a BP model, a metabolism GM (1, 1) gray prediction model.
And before the bridge technical condition prediction model is adopted, carrying out exponential rule inspection on the original data. And after the index rule check is passed, inputting the index rule check into the bridge technical condition prediction model.
The bridge comprehensive evaluation index comprises the following contents: bridge technical condition score (C), daily traffic volume (V), age (Y), bridge span (L), predicted technical condition (P).
It should be noted that the bridge prediction model herein analyzes a probability model of natural aging of the bridge without any maintenance measures.
In step S106, carrying out normalization processing on each bridge comprehensive evaluation index to generate a standardized matrix of the bridge comprehensive evaluation index;
in step S108, a combination weight of each bridge comprehensive evaluation index is determined.
In this embodiment, the subjective weighting method weight and the objective weighting method weight of each bridge comprehensive evaluation index may be determined, and then the combination weight of the subjective weighting method weight and the objective weighting method weight may be determined.
In step S108, a bridge comprehensive evaluation result is determined according to the standardized matrix and the combination weight.
According to the technical scheme, the combined weight of each bridge comprehensive evaluation index is determined, and then the bridge comprehensive evaluation result is determined according to the combined weight of each bridge comprehensive evaluation index, so that compared with a subjective weighting method and an objective weighting method in the related technology, more scientific weights can be adopted, more true and accurate bridge comprehensive evaluation results can be obtained, and the accuracy of the bridge comprehensive evaluation results is improved.
In one embodiment, the bridges are ranked according to the comprehensive bridge evaluation result.
In this embodiment, the bridges may be ranked according to the comprehensive evaluation result of the bridges. For example, the ranking may be performed in order of high to low evaluation index or in order of low to high evaluation index.
In one embodiment, determining the combining weight of each bridge composite evaluation index includes:
generating a judgment matrix according to the collected expert opinion;
and determining the maximum eigenvalue of the judgment matrix of the bridge comprehensive evaluation index.
Determining a consistency check coefficient of the judgment matrix according to the maximum eigenvalue of the judgment matrix;
and responding to the consistency check coefficient of the judgment matrix to be smaller than a preset threshold value, and checking the judgment matrix to pass.
In this embodiment, the above-mentioned predetermined threshold value may be flexibly set, and preferably, may be set to 0.1. In response to the consistency check coefficient of the decision matrix being equal to or greater than a predetermined threshold, the decision matrix is not checked, in which case the data needs to be readjusted to regenerate the decision matrix. By the verification method, accuracy of the judgment matrix is improved.
In one embodiment, determining the combining weight of each bridge composite evaluation index includes:
and determining a first weight vector and a second weight vector of each bridge comprehensive evaluation index according to the judgment matrix by adopting an analytic hierarchy process and an entropy weight process.
Determining the combined weight vector of each bridge comprehensive evaluation index according to the first weight vector and the second weight vector by adopting a combined weighting method;
and in the combined weight vector, the weight of each bridge comprehensive evaluation index is recorded.
In this embodiment, a hierarchical analysis method and an entropy weight method may be used to obtain the first weight vector and the second weight vector, respectively.
In this embodiment, the basic steps of the combination weighting method are as follows:
construct a set of n weight vectors A k The set of components { A 1 ，A 2 ，…A n Any linear combination of these n vectors may constitute one possible set of weights:
α k as the weight coefficient, alpha k ＞0。
Find the most satisfactory weight vector A * Can be converted into a pair of linear combination weight coefficients alpha k And (3) optimizing:
according to the differential property of the matrix, the system of linear equations corresponding to the first derivative condition of the above formula optimization can be known as
Solving (alpha) 1 ，α 2 ，…，α n ) Then normalized treatment is carried outFinally, the combination weight is calculated:
in one embodiment, determining a first weight vector of each bridge comprehensive evaluation index according to the judgment matrix by using an analytic hierarchy process includes:
in the judgment matrix of the bridge comprehensive evaluation index, each row represents one index;
for any index, determining the weight of the index according to each element in the row corresponding to the index;
and generating the first weight vector according to the weight of each index.
In this embodiment, the determining the weight of the index according to each element in the row corresponding to the index may further include the following steps:
determining the n-th-order root m of each element in the row corresponding to the index i ；
Determining n times of square roots of each row to obtain a set of n times of square root values;
according to the sum value of the set of n times square roots;
determining the quotient of the n secondary root corresponding to the index and the sum value, wherein the quotient is the weight of the index; where n is the number of elements in each row.
In the present embodiment, the weight w is calculated using the following formula i ：
First weight vector A 1 ＝[w 1 ,w 2 ,…,w n ]。
In one embodiment, determining the second weight vector of each bridge comprehensive evaluation index according to the judgment matrix by adopting an entropy weight method includes:
determining the entropy value of each index; redundancy of entropy;
summing the entropy redundancy of each index to obtain an entropy redundancy sum value;
for any one index, determining the weight of the index according to the entropy redundancy of the index and the entropy redundancy sum value;
a weight vector is determined based on the weight of each indicator.
In the present embodiment, the entropy calculation formula is as follows:
where k=1/lnn >0.
Obtaining entropy redundancy d j ＝1-e j ，j＝1,2,…,m。
Calculating the entropy value E= [ E ] of each index according to the formula 1 ,e 2 ,…,e n ]The larger the entropy value, the lower the degree of dispersion of the index, and the less information is provided.
The calculation formula of each index weight is as follows:
weight vector A of entropy weight method is obtained 2 ＝[w 1 ,w 2 ,…,w n ]。
In some embodiments, the above-described method is described in detail below with a specific and complete example.
A section of expressway in Hubei province is selected as a research object to verify the feasibility of a prediction model and a comprehensive evaluation method. The road section has a design speed of 80 km/h and a design reference period of 100 years, and the automobile load grade is the highway-I grade. The road section is provided with 152 bridges, wherein 105 bridges are T-beam bridges, 29 bridge-culvert bridges (solid slab beam bridges) and the rest are box beam bridges, and the T-beam bridge accounts for 69% at most. So research is being conducted on the most widely used T-beam bridge on highways.
Firstly, deducing and predicting the technical condition assessment score of the bridge. The GM (1, 1) gray prediction model may be used for the derived predictions.
Before the metabolism GM (1, 1) grey prediction model is used, the original data is subjected to exponential rule inspection.
When judging whether the original data passes the exponential law inspection, calculating the smoothness ratio of each data, and counting the data duty ratio of which the smoothness ratio is smaller than a preset first smoothness ratio threshold value; the duty cycle is greater than a predetermined first duty cycle threshold, and the verification passes.
Illustratively, the first slip ratio threshold is 0.5, and the first duty ratio threshold may be flexibly set, for example, 0.8.
Taking a T-beam bridge on the road section as an example, the data with the smoothness ratio smaller than 0.5 accounts for 83.3 percent, and meets the requirements, and the predicted result of the metabolism GM (1, 1) shown in the table 2 is referred.
| Year of year | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
| Technical condition scoring | 92.2 | 91.1 | 89.8 | 88.6 | 87 | 87 |
| Smooth ratio ρ (k) | 0.922 | 0.474 | 0.317 | 0.2375 | 0.1884 | 0.1586 |
| Metabolism GM | 92.1321 | 90.9728 | 89.8282 | 88.6979 | 87.5819 | 86.4799 |
| Relative residual error | 0.0736％ | 0.1396％ | 0.0314％ | 0.1105％ | 0.6689％ | 0.5978％ |
TABLE 2
The technical condition data of 2017-2026 are predicted through the technical condition scoring data of 2017-2022 of the bridge, a BP neural network model is built, and the total data volume accounts for 70% of a training set, 15% of a verification set and 15% of a test set. The number of hidden layer nodes starts training from 3, the learning rate is 0.01, the target error is 0.0001, the weight and the threshold are solved by adopting the Levenberg-Martensitic method, and the number of hidden layer nodes can be increased to enable the mean square error to be smaller.
And selecting 9T-shaped beam bridges on the road section for comprehensive bridge evaluation. The prediction results of the future technical condition of the 9 bridges are obtained through the gray BP neural network model, and the technical condition historical data and 2023-2025 predicted data of the 9 bridges in 2017-2022 are shown in table 3.
TABLE 3 Table 3
The comprehensive indexes of 9 bridges are shown in table 4. The prediction result shows the prediction trend of the natural degradation of the bridge without considering maintenance, and the degradation of the bridge structure along with time is an unavoidable process in the service life cycle of the bridge.
TABLE 4 Table 4
The technical condition score and the technical condition predicted value are forward indexes, and the larger the value is, the better the value is; daily traffic, service life and bridge length are reverse indexes, and the smaller the value is, the better the value is. The index data in table 4 is normalized to obtain a normalized matrix R.
A construction judgment matrix was established by the terfei expert evaluation method, as shown in table 5.
| C | V | Y | L | H | |
| C | 1 | 4 | 3 | 5 | 7 |
| V | 1/5 | 2 | 1/3 | 1 | 2 |
| Y | 1/4 | 1 | 1/2 | 2 | 4 |
| L | 1/3 | 2 | 1 | 3 | 5 |
| H | 1/7 | 1/4 | 1/5 | 1/2 | 1 |
TABLE 5
Calculated maximum eigenvalue lambda of matrix max = 5.09917, and a consistency check coefficient cr= 0.0221 is calculated<0.1, the consistency of the decision matrix is considered acceptable.
The calculation formula of the consistency check coefficient is as follows:
wherein lambda is max Judging the maximum eigenvalue of the matrix; RI is a random consistency index, and is related to the order n of the judgment matrix, and ri=1.12 when n=5.
Calculating and judging the product of elements in each row of the matrix by using a square root method, and solving n times of square root m for the product of each row i The weight calculation formula of each index is as follows:weight vector A of analytic hierarchy process is obtained through calculation 1 ＝[w 1 ,w 2 ,…,w n ]；
A 1 ＝[0.4895，0.1462,0.2318,0.0851,0.0474]；
Calculating entropy value E= [ E ] of each index 1 ，e 2 ，…，e n ]＝[0.9322，1，1,0.744,0.9343]The larger the entropy value, the lower the degree of dispersion of the index, and the less information is provided. The calculation formula of each index weight is as follows:weight vector A of entropy weight method is obtained 2 ＝[w 1 ，w 2 ，…，w n ]；
A 2 ＝[0.1741,0,0,0.6574,0.1685]；
Subjective weighting is carried out by adopting an analytic hierarchy process, objective weighting is carried out by adopting an entropy weighting method, and a linear equation set is obtained according to formulas (6) to (8) based on a game theory combined weighting theory:
its solution is (alpha) 1 ，α 2 ) = (0.6277,0.8093), then the combination of the two methods weights result a * The method comprises the following steps:
A * ＝[0.3119,0.0639,0.1012,0.4074,0.1156]；
the final evaluation result b=a·r can be obtained by the combination weighting method as shown in table 6.
| Sequence number | Analytic hierarchy process | Entropy weight method | Combined weighting method |
| 1 | 0.1117 | 0.1653 | 0.1163 |
| 2 | 0.1123 | 0.1687 | 0.1167 |
| 3 | 0.1010 | 0.0401 | 0.1042 |
| 4 | 0.1042 | 0.0009 | 0.0996 |
| 5 | 0.1157 | 0.1923 | 0.1195 |
| 6 | 0.1156 | 0.1919 | 0.1194 |
| 7 | 0.1123 | 0.1697 | 0.1168 |
| 8 | 0.1101 | 0.0424 | 0.1044 |
| 9 | 0.1082 | 0.0287 | 0.1028 |
TABLE 6
As can be seen from comparing the bridge comprehensive evaluation results obtained by the different methods in the table 6, the evaluation result of the combined weighting method is between the analytic hierarchy process and the entropy weighting method, so that not only is the deviation of subjective factors of artificial experience in the analytic hierarchy process avoided, but also the condition that the weight distribution is unreasonable in the entropy weighting method is considered, the influence of the two methods is balanced, and the comprehensive evaluation result is more scientific and reasonable.
The calculation result of the game theory based combined weighting method can obtain the comprehensive evaluation good order of the 9T-shaped beam bridges (from sequence number 1 to sequence number 9) as 5,4,7,9,1,2,3,6,8. The comprehensive evaluation of the bridge No. 4 is worst, and the comprehensive evaluation of the bridge No. 5 is optimal. In fact, as a reference for road network bridge maintenance decision, the sequence can be regarded as maintenance priority ranking, namely, under the condition of certain maintenance funds, the T-shaped beam bridge on the same road section can be maintained and maintained preferentially for the bridge No. 4.
In a second aspect, the present application provides a comprehensive evaluation device for a highway bridge based on multiple indexes, referring to fig. 2, including:
an acquisition module 21, configured to acquire highway maintenance bridge foundation data;
the input module 22 is configured to input the bridge technical condition annual score in the road maintenance bridge foundation data into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes;
the standardized matrix determining module 23 is configured to perform normalization processing on each bridge comprehensive evaluation index, and generate a standardized matrix of the bridge comprehensive evaluation indexes;
a combination weight determining module 24, configured to determine a combination weight of each bridge comprehensive evaluation index;
and the result determining module 25 is used for determining a bridge comprehensive evaluation result according to the standardized matrix and the combination weight.
In a third aspect, the present application further provides a comprehensive evaluation device for a highway bridge based on multiple indexes, referring to fig. 3, the device includes: a processor 31; a memory 32 for storing processor-executable instructions; wherein the processor 31 is configured to execute the executable instructions to implement the method of any of the above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A comprehensive evaluation method for a highway bridge based on multiple indexes is characterized by comprising the following steps:
obtaining basic data of a highway maintenance bridge;
inputting the bridge technical condition annual score in the road maintenance bridge foundation data into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes;
normalizing each bridge comprehensive evaluation index to generate a standardized matrix of the bridge comprehensive evaluation index;
determining the combination weight of each bridge comprehensive evaluation index;
and determining a bridge comprehensive evaluation result according to the standardized matrix and the combination weight.
2. The multi-index-based comprehensive evaluation method for highway bridges according to claim 1, wherein,
and sequencing the bridges according to the comprehensive bridge evaluation result.
3. The multi-index-based highway bridge comprehensive evaluation method according to claim 1, wherein determining the combination weight of each bridge comprehensive evaluation index comprises:
generating a judgment matrix according to the collected expert opinion;
determining the maximum eigenvalue of the judgment matrix of the bridge comprehensive evaluation index;
determining a consistency check coefficient of the judgment matrix according to the maximum eigenvalue of the judgment matrix;
and responding to the consistency check coefficient of the judgment matrix to be smaller than a preset threshold value, and checking the judgment matrix to pass.
4. The multi-index-based comprehensive evaluation method for highway bridges according to claim 3, wherein,
determining the combination weight of each bridge comprehensive evaluation index comprises the following steps:
respectively adopting an analytic hierarchy process and an entropy weight process to determine a first weight vector and a second weight vector of each bridge comprehensive evaluation index according to the judgment matrix;
determining the combined weight vector of each bridge comprehensive evaluation index according to the first weight vector and the second weight vector by adopting a combined weighting method;
and in the combined weight vector, the weight of each bridge comprehensive evaluation index is recorded.
5. The multi-index-based comprehensive evaluation method for highway bridges according to claim 4, wherein,
determining a first weight vector of each bridge comprehensive evaluation index according to the judgment matrix by adopting an analytic hierarchy process, wherein the method comprises the following steps:
in the judgment matrix of the bridge comprehensive evaluation index, each row represents one index;
for any index, determining the weight of the index according to each element in the row corresponding to the index;
and generating the first weight vector according to the weight of each index.
6. The multi-index-based comprehensive evaluation method for highway bridges according to claim 5, wherein,
determining the weight of the index according to each element in the row corresponding to the index comprises the following steps:
determining the n times square root of each element in the row corresponding to the index;
determining n times of square roots of each row to obtain a set of n times of square root values;
determining a set sum value from the set of n secondary roots;
determining the quotient of the n secondary root corresponding to the index and the sum value, wherein the quotient is the weight of the index;
where n is the number of elements in each row.
7. The multi-index-based comprehensive evaluation method for highway bridges according to claim 3, wherein,
determining a second weight vector of each bridge comprehensive evaluation index according to the judgment matrix by adopting an entropy weight method, wherein the method comprises the following steps:
determining the entropy value of each index; redundancy of entropy;
summing the entropy redundancy of each index to obtain an entropy redundancy sum value;
for any one index, determining the weight of the index according to the entropy redundancy of the index and the entropy redundancy sum value;
a weight vector is determined based on the weight of each indicator.
8. Highway bridge comprehensive evaluation device based on many indexes, characterized by comprising:
the acquisition module is used for acquiring the basic data of the highway maintenance bridge;
the input module is used for inputting the road maintenance bridge foundation data into a pre-trained bridge technical condition prediction model to obtain a plurality of bridge comprehensive evaluation indexes;
the combination weight determining module is used for determining the combination weight of each bridge comprehensive evaluation index;
and the result determining module is used for determining the bridge comprehensive evaluation result according to the combination weight of each bridge comprehensive evaluation index.
9. Highway bridge comprehensive evaluation device based on many indexes, characterized by comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the method of any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1 to 7.
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