Bridge maintenance evaluation method and system based on multi-source data fusion

Abstract

This application discloses a post-maintenance evaluation method and system for bridges using multi-source data fusion, including: constructing a post-maintenance evaluation index system for bridges, wherein the index system uses the repair and reinforcement effect as the target evaluation index, wherein the target evaluation index uses technical performance, economic benefits, social benefits, and implementation feasibility as first-level subordinate evaluation indicators, wherein the first-level subordinate indicators include several second-level subordinate evaluation indicators; calculating the weights of the first-level and second-level subordinate evaluation indicators; calculating the index values ​​of the second-level subordinate evaluation indicators of technical performance and economic benefits, and standardizing the index values; calculating the scores of technical performance, economic benefits, social benefits, and implementation feasibility; calculating the scores of the repair and reinforcement effect; and determining the evaluation level and / or evaluation recommendations for the repair and reinforcement effect. This application combines qualitative and quantitative index evaluation, which can solve the problem of the inability to collaboratively evaluate qualitative and quantitative multi-source data, and promote the transformation of post-maintenance evaluation of bridges towards scientific quantification.

Description

Technical Field

[0001] The present application relates to the technical field of bridge health monitoring and assessment, and in particular to a bridge post-maintenance assessment method and system based on multi-source data fusion. Background Art

[0002] As bridges age, they inevitably develop various defects under the influence of loads, environmental erosion, and other factors, leading to a dramatic increase in bridge maintenance workloads. Faced with the massive scale and daunting workload, selecting effective and efficient maintenance measures is crucial for achieving effective maintenance. Post-maintenance evaluations, based on experience and lessons learned, retrospectively evaluate existing maintenance measures, optimize maintenance plans and countermeasures, and maximize bridge maintenance benefits. Conducting post-maintenance evaluations has become an essential requirement for improving bridge maintenance efficiency. However, current post-maintenance evaluations still face the following challenges: First, the post-maintenance evaluation indicator system is incomplete and lacks scientific validity, rarely considering multidimensional information such as testing, monitoring, and human qualitative evaluation; second, the evaluation method is still primarily driven by subjective and empirical evaluation, lacking the effective integration of multiple sources of quantitative and qualitative information; and third, the weighting of evaluation indicators fails to reflect the inherent influences between indicators, limiting the credibility of the evaluation results.

[0003] Among the latest existing technologies, the invention patent application with publication number CN119046884A discloses a bridge health assessment method, system, and storage medium based on the fusion of inspection and monitoring data. This technical solution integrates a monitoring information index library and a structured inspection and monitoring bottom-level index library to construct a multi-level fusion model for inspection and monitoring. It then uses an improved analytic hierarchy process (AHP) to calculate the weights of indicators at each level in the multi-level fusion model. Finally, a weighted approach is used to calculate the health scores of the inspection and monitoring data layer by layer based on the indicator weights of each level, the indicator scores of the bottom-level indicators, and the indicator weights. The average of the two is taken as the final bridge health score. However, this method essentially relies on a weighted calculation of the bottom-level indicator scores and cannot simultaneously process both quantitative and qualitative multi-source fusion information. In particular, it cannot directly perform weighted calculations on qualitative evaluations. Furthermore, the weight calculation based on the AHP focuses on the pairwise comparison of influencing factors, which does not adequately reflect the cross-influence and coupling between indicators. The invention patent with the authorization announcement number CN119046600B discloses a cable-stayed bridge condition assessment method, system and storage medium that integrates inspection and monitoring. This technical solution introduces monitoring indicators on the basis of the existing hierarchical analysis model for the comprehensive technical condition assessment of cable-stayed bridges. At the same time, the assessment indicators are divided into qualitative indicators and quantitative indicators, and the assessment standards of each indicator are determined to form an integrated cable-stayed bridge condition assessment system that integrates inspection and monitoring. The assessment system is composed of multi-level indicators such as the part layer, component layer and damage layer. According to the damage deduction value and indicator weight of each damage indicator in the damage layer, the weighted comprehensive method is used to reversely calculate the scores of each component in the component layer, the scores of each part in the part layer and the condition score of the entire bridge to complete the health assessment of the cable-stayed bridge. However, this technology converts the qualitative damage evaluation indicators into deduction values ​​according to the existing technical condition assessment standards to achieve the conversion from qualitative to quantitative. In essence, it still converts all evaluation indicators into quantitative indicators that can be quantitatively calculated and weights them, and cannot directly process the qualitative evaluation indicators. Summary of the Invention

[0004] In response to the problems existing in the existing technology, this application proposes a bridge post-maintenance evaluation method and system that integrates multi-source data, combining qualitative indicator evaluation with quantitative indicator evaluation. It can solve the difficult problem that qualitative and quantitative multi-source data cannot be evaluated collaboratively, and promote the transformation of bridge post-maintenance evaluation towards scientific quantification.

[0005] First, a multi-source data fusion bridge maintenance assessment method includes the following:

[0006] S1: Construct a multi-source data fusion bridge maintenance evaluation index system, which includes the target layer, the criterion layer and the indicator layer;

[0007] The target layer uses the repair and reinforcement effect as the target evaluation indicator; the criterion layer includes several first-level subordinate evaluation indicators subordinate to the target evaluation indicator, namely technical performance, economic benefits, social benefits and implementation feasibility; the indicator layer includes several second-level subordinate evaluation indicators subordinate to the first-level subordinate evaluation indicators; multiple subordinate evaluation indicators of the same evaluation indicator constitute a parallel indicator group;

[0008] S2: Calculate the weights of the first-level subordinate evaluation indicators and the second-level subordinate evaluation indicators;

[0009] S3: Establishing characterization parameters for the secondary subordinate evaluation indicators of technical performance and economic benefits, and calculating the index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits based on the characterization parameters; determining the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; dividing the secondary subordinate evaluation indicators of technical performance and economic benefits into positive indicators and inverse indicators based on the consistency relationship between the direction of change of the index values ​​and the target expectations, and standardizing the index values ​​of the positive indicators and inverse indicators respectively to obtain the standardized index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits;

[0010] S4: Calculate the scores of the technical performance and economic benefits based on the standardized index values ​​and weights of the secondary subordinate evaluation indicators of the technical performance and economic benefits;

[0011] S5: Fuzzy comprehensive evaluation is used to calculate the scores of social benefits and implementation feasibility;

[0012] S6: Calculate the score of the target evaluation indicator based on the weights and scores of the evaluation indicators at the criterion layer;

[0013] S7: Determine the evaluation grade and / or evaluation recommendation based on the scores of the target evaluation indicators.

[0014] In a possible implementation of the first aspect, step S2 uses the decision-making test evaluation to calculate the weights of the first-level subordinate evaluation indicators and the second-level subordinate evaluation indicators in the criterion layer and the indicator layer, specifically including the following:

[0015] Definition a ij Indicates the degree of influence of evaluation index i on evaluation index j, i, j∈[1,n], n represents the number of evaluation indicators in the same parallel indicator group;

[0016] Set the scale value used to judge the degree of influence between evaluation indicators, and construct the influence matrix X1 by comparing the evaluation indicators pairwise:

[0017] ;

[0018] Normalize the influence matrix to obtain the standardized influence matrix X:

[0019] ;

[0020] Among them, max represents the maximum value of the sum of rows or columns of the influence matrix X1;

[0021] Calculate the comprehensive impact matrix Y:

[0022] ;

[0023] Where E is the identity matrix;

[0024] The influence degree D of evaluation index i is calculated by comprehensive influence matrix Y i , influence degree C i and centrality M i :

[0025] ;

[0026] ;

[0027] ;

[0028] By the centrality M i Calculate the weight ω of each evaluation indicator i :

[0029] ;

[0030] Thus, the weight of each first-level subordinate evaluation index in the criterion layer is obtained A={ , , , }, and the weight of the secondary subordinate evaluation indicator corresponding to the technical performance in the indicator layer B1={ , , , }、The weight of the secondary subordinate evaluation index corresponding to economic benefits is B2={ , , }、The weight of the secondary subordinate evaluation index corresponding to social benefits B3={ , , , }、The weight of the second-level subordinate evaluation indicator corresponding to the implementation feasibility is B4={ , , }, N1, N2, N3, and N4 represent the number of secondary subordinate evaluation indicators of technical performance, economic benefits, social benefits, and implementation feasibility, respectively.

[0031] In a possible implementation of the first aspect, the secondary subordinate evaluation indicators corresponding to the technical performance in step S3 include technical status, load-bearing capacity, stiffness degradation, strength degradation, and interface failure; the secondary subordinate evaluation indicators corresponding to the economic benefits include investment scale and investment benefits;

[0032] The technical condition is expressed by the ratio of the technical condition scores (BCI) before and after the bridge reinforcement. The calculation formula of the bridge technical condition score (BCI) is as follows:

[0033] ;

[0034] Where, BCI m 、BCI s 、BCI x Represent the technical condition scores of the bridge deck system, superstructure and substructure respectively; ω m 、ω s 、ω x Respectively represent the weights of the bridge deck system, superstructure, and substructure, and are determined according to the specifications;

[0035] The load-bearing capacity is expressed by the ratio of the load test verification coefficient η before and after reinforcement. The calculation formula of the load test verification coefficient η is as follows:

[0036] ;

[0037] Where S s It represents the maximum calculated effect value of the internal force or displacement of the loading control section corresponding to the loading test under the action of the static load test; P represents the calculated value of the most unfavorable effect of the internal force or displacement of the same loading control section produced by the control load; μ represents the impact coefficient value taken according to the specification;

[0038] The strength degradation is represented by the ratio of the strain ε at the key part of the bottom of the mid-span beam during the monitoring and evaluation period after reinforcement, where the strain ε at the key part of the bottom of the mid-span beam is a measured indicator;

[0039] The stiffness degradation is expressed by the ratio of the mid-span deformation Se during the monitoring and evaluation period after reinforcement, and the mid-span deformation Se is a measured indicator;

[0040] The interface failure is represented by the ratio of the failure area to the total bonding area S;

[0041] The investment scale is expressed as the ratio of repair and reinforcement costs to bridge construction costs;

[0042] The investment benefit is expressed by the maintenance investment benefit coefficient.

[0043] In a possible implementation of the first aspect, the calculation process of the maintenance investment benefit coefficient is as follows:

[0044] Establish a concrete bridge bearing capacity assessment and prediction model to determine the bridge bearing capacity decay curve before and after maintenance and reinforcement;

[0045] The maintenance investment benefit area factor S is calculated based on the area enclosed by the bridge bearing capacity decay curve before and after maintenance and reinforcement during the assessment period. z ;

[0046] ;

[0047] The structural bearing capacity retention area factor S is calculated using the bridge bearing capacity decay curve before maintenance and reinforcement during the assessment period and the area enclosed by the horizontal axis. y ;

[0048] ;

[0049] Among them, f(D a ) is the decay curve function of the bridge bearing capacity before maintenance and reinforcement, f(D b ) is the decay curve function of the bridge bearing capacity after maintenance and reinforcement, t a is the maintenance and reinforcement time, t d is the post-evaluation time, t a to t d This is the post-maintenance evaluation period;

[0050] The ratio of the maintenance investment benefit area factor and the structural bearing capacity maintenance area factor is the maintenance investment benefit coefficient C:

[0051] ;

[0052] Among them, S z is the area factor of bridge maintenance investment benefit, S y Maintain area factor for structural capacity.

[0053] In a possible implementation of the first aspect, the technical status and investment benefit in the second-level subordinate evaluation indicators in step S3 are positive indicators, and the indicator values ​​of the positive indicators are normalized using the following formula, with the normalization range being [0, 100]:

[0054] ;

[0055] The load-bearing capacity, stiffness degradation, strength degradation, interface failure and investment scale in the secondary subordinate evaluation indicators are inverse indicators. The following formula is used to standardize the index values ​​of the inverse indicators, and the standardization range is [0,100]:

[0056] ;

[0057] Where, I represents the parameter value representing technical status, bearing capacity, stiffness degradation, strength degradation, interface failure, investment scale, and investment benefit, i.e., the index value; max , I min They represent the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits in the concrete bridge case library respectively;

[0058] Through the above standardization processing, the standardized index values ​​of technical status, bearing capacity, stiffness degradation, strength degradation, interface failure, investment scale and investment benefit are obtained.

[0059] In a possible implementation of the first aspect, the second-level subordinate evaluation indicators corresponding to the social benefits include environmental impact, low-carbon energy saving, traffic interference, and traffic satisfaction.

[0060] In a possible implementation of the first aspect, the second-level subordinate evaluation indicators corresponding to the implementation feasibility include process convenience and material extensiveness.

[0061] In a possible implementation of the first aspect, step S5 specifically includes the following content:

[0062] Establishing factor sets , N3 represents the number of secondary subordinate evaluation indicators corresponding to social benefits;

[0063] Determine the evaluation level ,K represents the number of evaluation levels;

[0064] Through the evaluation of factor set indicators by experts, the fuzzy comprehensive evaluation matrix R is constructed:

[0065] ;

[0066] Where z nk represents the number of experts who give evaluation grade k to the nth secondary subordinate evaluation indicator, m is the total number of experts participating in the evaluation, r nk Indicates the membership of the secondary subordinate evaluation indicator to each evaluation level, n∈[1,N3], k∈[1,K];

[0067] The weight of the indicator layer evaluation index corresponding to social benefits is B3={ , , , } and the fuzzy comprehensive evaluation matrix R to perform fuzzy synthesis and calculate the comprehensive evaluation vector B of social benefits 社会效益 :

[0068] ;

[0069] Setting social benefit evaluation levels The fractions represented by v1, v2, …… 、v K ;

[0070] According to the comprehensive evaluation vector B of social benefits 社会效益 The social benefit score is calculated by weighted summing the scores represented by the evaluation level:

[0071] ;

[0072] Similarly, we can get the score of implementation feasibility:

[0073] .

[0074] In a possible implementation of the first aspect, in step S7, evaluation levels are divided according to the scoring range of the target evaluation indicator, and recommendations are given; the corresponding relationship between the scoring range, the evaluation level, and the evaluation recommendation is as follows:

[0075] When the score range is [80,100], the evaluation level is level I and the recommendation is "priority recommendation";

[0076] When the score range is [60,80], the assessment level is II and the recommendation is "recommended";

[0077] When the score range is [0,60], the assessment level is III and the recommendation is "not recommended".

[0078] In a second aspect, a system for executing the multi-source data fusion bridge maintenance assessment method includes:

[0079] Evaluation index system construction module, used to construct a multi-source data fusion bridge maintenance evaluation index system, which includes a target layer, a criterion layer, and an indicator layer;

[0080] A weight calculation module, used to calculate the weights of the evaluation indicators of the criterion layer and the indicator layer;

[0081] A standardized index value calculation module is used to establish characterization parameters for the secondary subordinate evaluation indicators of technical performance and economic benefits, calculate the index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits based on the characterization parameters; determine the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; and divide the secondary subordinate evaluation indicators of technical performance and economic benefits into positive indicators and inverse indicators based on the consistency relationship between the direction of change of the index values ​​and the target expectations, and standardize the index values ​​of the positive indicators and inverse indicators respectively to obtain standardized index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits;

[0082] The first scoring module of the criterion layer calculates the scores of the technical performance and economic benefits based on the standardized index values ​​and weights of the secondary subordinate evaluation indicators of technical performance and economic benefits;

[0083] The second scoring module of the criterion layer uses fuzzy comprehensive evaluation to calculate the scores of social benefits and implementation feasibility;

[0084] The target layer scoring module calculates the score of the target evaluation indicator according to the weight and score of each evaluation indicator in the criterion layer;

[0085] The evaluation result output module determines the evaluation grade and / or evaluation recommendations based on the scores of the target evaluation indicators.

[0086] Compared with the existing methods, this application has the following beneficial effects:

[0087] 1. This application introduces human-based qualitative collaborative evaluation indicators based on the integration of traditional inspection and monitoring quantitative data, and constructs a bridge maintenance assessment indicator system that integrates inspection and monitoring data with qualitative information in a multi-dimensional manner.

[0088] 2. This application overcomes the drawback of insufficient consideration of cross-influence in the traditional AHP weight calculation process. By calculating the coupling effect of indicator influence and influence, and determining indicator weights based on centrality, it improves the rationality and scientific nature of weight setting.

[0089] 3. The index values ​​for this application are calculated using adaptive calculation of the maximum and minimum values ​​in the curing cases, thus avoiding the defects of the hardening score based on the assessment standard, which is highly subjective and lacks rationality;

[0090] 4. This application combines fuzzy comprehensive evaluation of qualitative evaluation indicators with weighted calculation of quantitative indicators, which solves the problem that quantitative and qualitative multi-source data cannot be evaluated collaboratively, and promotes the transformation of post-maintenance evaluation of bridges from "expert experience" to "scientific quantification". BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A flowchart of a bridge maintenance evaluation method based on multi-source data fusion provided in an embodiment of the present application;

[0092] Figure 2 A concrete bridge bearing capacity assessment and prediction model is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solutions and advantages of this application clearer, this application will be further described in detail below with reference to the accompanying drawings.

[0094] It should be understood that the embodiments described are only a portion of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0095] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "an", "the" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0096] It should be understood that the term "and / or" used in this document is merely a way to describe the same field of associated objects, indicating that three relationships may exist.

[0097] First, as Figure 1 As shown in the figure, a multi-source data fusion bridge maintenance evaluation method includes the following contents:

[0098] S1: Construct a multi-source data fusion bridge maintenance evaluation index system, which includes the target layer, the criterion layer and the indicator layer;

[0099] The target layer takes the maintenance and reinforcement effect (EMR) as the target evaluation indicator; the criterion layer includes several first-level subordinate evaluation indicators subordinate to the target evaluation indicator, namely technical performance (TP), economic benefit (EB), social benefit (SE) and implementation feasibility (IF); the indicator layer includes several second-level subordinate evaluation indicators subordinate to the first-level subordinate evaluation indicators; multiple subordinate evaluation indicators of the same evaluation indicator constitute a parallel indicator group;

[0100] S2: Calculate the weights of each first-level subordinate evaluation indicator in the criterion layer and each second-level subordinate evaluation indicator in the indicator layer respectively;

[0101] S3: Establish characterization parameters for the secondary subordinate evaluation indicators of technical performance (TP) and economic benefits (EB), calculate the index values ​​of the secondary subordinate evaluation indicators of technical performance (TP) and economic benefits (EB) based on the characterization parameters, and determine the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance (TP) and economic benefits (EB); divide the secondary subordinate evaluation indicators of technical performance (TP) and economic benefits (EB) into positive indicators and inverse indicators based on the consistency relationship between the change direction of the index value and the target expectation, and standardize the index values ​​of the positive indicators and inverse indicators respectively to obtain the standardized index values ​​of the secondary subordinate evaluation indicators of technical performance (TP) and economic benefits (EB);

[0102] S4: Calculate the scores of the technical performance (TP) and economic benefits (EB) based on the standardized index values ​​and weights of the secondary subordinate evaluation indicators of the technical performance (TP) and economic benefits (EB);

[0103] S5: Fuzzy comprehensive evaluation is used to calculate the scores of social benefits (SE) and implementation feasibility (IF);

[0104] S6: Based on the weights and scores of the first-level subordinate evaluation indicators of the criterion layer, the score of the target evaluation indicator "repair and reinforcement effect" is weightedly calculated:

[0105] ;

[0106] Among them, EMR represents the score of the maintenance and reinforcement effect, TP, EB, SE, and IF represent the scores of technical performance, economic benefits, social benefits, and implementation feasibility, respectively; A represents the weight set of technical performance, economic benefits, social benefits, and implementation feasibility;

[0107] S7: Determine the evaluation level and / or evaluation recommendations based on the score of the target evaluation indicator "Repair and Reinforcement Effect".

[0108] In a possible implementation, step S2 uses decision-making test evaluation to calculate the weights of each evaluation indicator in the criterion layer and the indicator layer. The specific process is as follows:

[0109] Definition a ij Indicates the degree of influence of evaluation index i on evaluation index j, a ij The larger the value, the greater the influence of evaluation index i on evaluation index j, i, j∈[1,n], n represents the number of evaluation indicators in the same parallel indicator group;

[0110] Set the scale value used to judge the degree of influence between evaluation indicators. It is generally recommended to set a 5-level scale {0, 1, 2, 3, 4}, and the value represents the strength of the indicator influence relationship;

[0111] Construct the influence matrix X1 by comparing the evaluation indicators pairwise:

[0112] ;

[0113] After normalizing the influence matrix, we get the standardized influence matrix X:

[0114] ;

[0115] Among them, max represents the maximum value of X1 summed by row or column;

[0116] Calculate the comprehensive impact matrix Y:

[0117] ;

[0118] Where E is the identity matrix;

[0119] The influence degree D of evaluation index i is calculated by comprehensive influence matrix Y i , influence degree C i and centrality M i :

[0120] ;

[0121] ;

[0122] ;

[0123] By the centrality M i Calculate the weight ω of each evaluation indicator i :

[0124] ;

[0125] The weight of each first-level subordinate evaluation index in the criterion layer is calculated as A={ , , , }, and the weight of the secondary subordinate evaluation indicator corresponding to the technical performance in the indicator layer B1={ , , , }、The weight of the secondary subordinate evaluation index corresponding to economic benefits is B2={ , , }、The weight of the secondary subordinate evaluation index corresponding to social benefits B3={ , , , }、The weight of the second-level subordinate evaluation indicator corresponding to the implementation feasibility is B4={ , , }, N1, N2, N3, and N4 represent the number of secondary subordinate evaluation indicators of technical performance, economic benefits, social benefits, and implementation feasibility, respectively.

[0126] In addition to the above-mentioned decision test evaluation method, step S2 can also use existing technologies such as hierarchical analysis method, entropy weight method, principal component analysis method, etc. to calculate the weights of each evaluation index in the criterion layer and indicator layer, which is not limited here.

[0127] In one possible implementation, step S3 acquires test data through regular inspections, health monitoring, special inspections, etc., and establishes characterization parameters for the secondary subordinate evaluation indicators of technical performance (TP) and economic benefits (EB) based on the test data;

[0128] The secondary subordinate evaluation indicators corresponding to the technical performance (TP) include but are not limited to technical condition (TC), bearing capacity (BC), stiffness degradation (SD), strength degradation (SC), and interface failure (IE); the secondary subordinate evaluation indicators corresponding to the economic benefits (EB) include but are not limited to investment scale (SI) and investment benefit (IB);

[0129] The technical condition (TC) is expressed by the ratio of the technical condition scores BCI before and after the bridge reinforcement; the bearing capacity (BC) is expressed by the ratio of the load test verification coefficient η before and after the reinforcement; the strength degradation (SC) is expressed by the ratio of the strain ε at the key part of the bottom of the beam in the middle of the span during the monitoring and evaluation period after the reinforcement; the stiffness degradation (SD) is expressed by the ratio of the deformation Se in the middle of the span during the monitoring and evaluation period after the reinforcement; the interface failure (IE) is expressed by the ratio of the failure area to the total bonding area S; specifically:

[0130] The calculation formula for the bridge technical condition score BCI is as follows:

[0131] ;

[0132] Where, BCI m 、BCI s 、BCI x Represent the technical condition scores of the bridge deck system, superstructure and substructure respectively; ω m 、ω s 、ω x They represent the weights of the bridge deck system, superstructure and substructure respectively, and are taken according to the specifications.

[0133] The calculation formula of the load test verification coefficient η is as follows:

[0134] ;

[0135] Where S s It represents the maximum calculated effect value of the internal force or displacement of the loading control section corresponding to a certain loading test item under the action of the static load test load; P represents the calculated value of the most unfavorable effect of the internal force or displacement of the same loading control section produced by the control load; μ represents the impact coefficient value taken according to the specification.

[0136] The strain ε at the key position of the bottom of the mid-span beam and the deformation Se at the mid-span are measured indicators.

[0137] The scale of investment (SI) is expressed as the ratio of the repair and reinforcement cost to the bridge construction cost; the investment benefit (IB) is expressed as the maintenance investment benefit coefficient. The calculation process of the maintenance investment benefit coefficient is as follows:

[0138] like Figure 2 As shown in the figure, a concrete bridge bearing capacity assessment and prediction model is established, D n represents the bearing capacity of the bridge before maintenance and reinforcement, D h Express the bearing capacity of the bridge after maintenance and reinforcement, and determine the decay curve of the bridge bearing capacity before and after maintenance and reinforcement;

[0139] The maintenance investment benefit area factor S is calculated based on the area enclosed by the bridge bearing capacity decay curve before and after maintenance and reinforcement during the assessment period. z ;

[0140] ;

[0141] The structural bearing capacity retention area factor S is calculated using the bridge bearing capacity decay curve before reinforcement within the assessment period and the area enclosed by the horizontal axis. y ;

[0142] ;

[0143] Among them, f(D a ) is the decay curve function of the bridge bearing capacity before maintenance and reinforcement, f(D b ) is the decay curve function of the bridge bearing capacity after maintenance and reinforcement, t a is the maintenance and reinforcement time, t d is the post-evaluation time, t a to t d This is the post-maintenance evaluation period;

[0144] The ratio of the maintenance investment benefit area factor and the structural bearing capacity maintenance area factor is the maintenance investment benefit coefficient C:

[0145] ;

[0146] Among them, S z is the area factor of bridge maintenance investment benefit, S y Maintain area factor for structural capacity.

[0147] Furthermore, in a possible implementation, step S3 constructs a case library of maintenance and reinforcement of concrete bridges and determines the maximum value I of each secondary subordinate evaluation index of technical performance (TP) and economic benefit (EB) in the case library. max and minimum value I min ;

[0148] Among the secondary evaluation indicators of technical performance (TP) and economic benefits (EB), technical condition (TC) and investment benefit (IB) are positive indicators (the larger the indicator value, the better); bearing capacity (BC), stiffness degradation (SD), strength degradation (SC), interface failure (IE) and investment scale (SI) are negative indicators (the smaller the indicator value, the better);

[0149] The following formula is used to normalize the index value of the positive index. The normalization range is [0,100], and the normalized score is:

[0150] ;

[0151] The following formula is used to standardize the index value of the inverse index. The standardization range is [0,100], and the standardized score is:

[0152] ;

[0153] Where I represents the characterization parameter values ​​of technical condition (TC), bearing capacity (BC), stiffness degradation (SD), strength degradation (SC), interface failure (IE), investment scale (SI), and investment benefit (IB), i.e., the index value;

[0154] The above standardized scores are used as standardized indicator values ​​for technical condition (TC), bearing capacity (BC), stiffness degradation (SD), strength degradation (SC), interface failure (IE), investment scale (SI), and investment benefit (IB).

[0155] In step S4, the scores of technical performance (TP) and economic benefits (EB) in the criterion layer are calculated using weighted calculations based on the standardized index values ​​determined in step S3 and the weights determined in step S2:

[0156] ;

[0157] .

[0158] In a possible implementation, step S5 specifically includes the following content:

[0159] Establishing factor sets , N3 represents the number of secondary subordinate evaluation indicators corresponding to social benefits (SE);

[0160] Determine the evaluation level ,K represents the number of evaluation levels;

[0161] Through the evaluation of factor set indicators by experts, the fuzzy comprehensive evaluation matrix R is constructed:

[0162] ;

[0163] Where z nk represents the number of experts who give evaluation grade k to the nth secondary subordinate evaluation indicator, m is the total number of experts participating in the evaluation, r nk Indicates the membership of the secondary subordinate evaluation indicator to each evaluation level, n∈[1,N3], k∈[1,K];

[0164] The weight of the indicator layer evaluation index corresponding to social benefit (SE) is B3={ , , , } and the fuzzy comprehensive evaluation matrix R to perform fuzzy synthesis and calculate the comprehensive evaluation vector B of social benefits 社会效益 :

[0165] ;

[0166] Setting social benefit evaluation levels The fractions represented by v1, v2, …… 、v K ;

[0167] According to the comprehensive evaluation vector B of social benefits 社会效益 The quantitative score of social benefit (SE) is calculated by weighted summing the scores represented by the evaluation level:

[0168] ;

[0169] Similarly, we can get the score of feasibility (IF):

[0170] .

[0171] In a possible implementation, step S7 divides the evaluation levels according to the scoring range of the repair and reinforcement effect and provides recommendations. The corresponding relationship between the scoring range, evaluation level, and evaluation recommendations is as follows:

[0172] Table 1 Scoring range, evaluation level and evaluation suggestions for maintenance and reinforcement effects

[0173] Rating range Assessment level Recommendations [80,100] Ⅰ Priority recommendation [60,80] Ⅱ recommend [0,60] Ⅲ Not recommended

[0174] In a preferred implementation method, the indicator layer contains a total of 13 indicators, among which the technical performance (TP) sets the secondary indicators of technical status (TC), bearing capacity (BC), stiffness degradation (SD), strength degradation (SC), and interface failure (IE); the economic benefit (EB) sets the secondary indicators of investment scale (SI) and investment benefit (IB); the social benefit (SE) sets the secondary indicators of environmental impact (EI), low carbon energy saving (LC), traffic interference (TI), and traffic satisfaction (ST); the implementation feasibility (IF) sets the secondary indicators of process convenience (PC) and material universality (MU).

[0175] In this embodiment, the criterion layer weight A={ , , , }, Technical Performance (TP) indicator layer weight B1={ , , , }, Economic Benefit (EB) indicator layer weight B2={ , }、Social benefit (SE) indicator layer weight B3={ , , , }、Implementation feasibility (IF) indicator layer weight B4={ , }.

[0176] Correspondingly, the factor set , where u1, u2, u3, and u4 represent environmental impact (EI), low-carbon energy saving (LC), traffic interference (TI), and traffic satisfaction (ST), respectively.

[0177] Determine the evaluation level , where 1, 2, 3, 4, and 5 represent excellent, good, average, poor, and bad, respectively.

[0178] Through the evaluation of factor set indicators by experts, the fuzzy comprehensive evaluation matrix R is constructed:

[0179] ;

[0180] Where z nk represents the number of experts who give evaluation grade k to the nth secondary subordinate evaluation indicator. m is the total number of experts participating in the evaluation, r nk Indicates the degree of membership of the secondary indicator to each evaluation level.

[0181] The weight vector of the subordinate indicators of social benefits calculated in step S3 , where b31 , b 32 , b 33 , b 34 Represent the weights of environmental impact (EI), low carbon energy saving (LC), traffic interference (TI), and traffic satisfaction (ST). The weight set B3 and the fuzzy comprehensive evaluation matrix R are fuzzy synthesized to calculate the comprehensive evaluation vector B of social benefits. 社会效益 :

[0182] .

[0183] The scores represented by the social benefit evaluation levels are set as follows: excellent 100 points, good 75 points, average 50 points, poor 25 points, and poor 0 points. 社会效益 The social benefit (SE) score is calculated by weighted summing the scores represented by the evaluation level:

[0184] ;

[0185] Similarly, we can get the score of feasibility (IF):

[0186] .

[0187] In a second aspect, the present application provides a system for executing the multi-source data fusion bridge maintenance assessment method, comprising:

[0188] The indicator system construction module is used to construct a multi-source data fusion bridge maintenance evaluation indicator system, which includes a target layer, a criterion layer, and an indicator layer;

[0189] A weight calculation module, used to calculate the weights of the evaluation indicators of the criterion layer and the indicator layer;

[0190] A standardized index value calculation module is used to establish characterization parameters for the secondary subordinate evaluation indicators of technical performance and economic benefits, calculate the index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits based on the characterization parameters; determine the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; and divide the secondary subordinate evaluation indicators of technical performance and economic benefits into positive indicators and inverse indicators based on the consistency relationship between the direction of change of the index values ​​and the target expectations, and standardize the index values ​​of the positive indicators and inverse indicators respectively to obtain standardized index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits;

[0191] The first scoring module of the criterion layer calculates the scores of the technical performance and economic benefits based on the standardized index values ​​and weights of the secondary subordinate evaluation indicators of technical performance and economic benefits;

[0192] The second scoring module of the criterion layer uses fuzzy comprehensive evaluation to calculate the scores of social benefits and implementation feasibility;

[0193] The target layer scoring module calculates the score of the target evaluation indicator according to the weight and score of each evaluation indicator in the criterion layer;

[0194] The evaluation result output module determines the evaluation grade and / or evaluation recommendations based on the scores of the target evaluation indicators.

[0195] It should be understood that the division of the processing units in the above system is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity, or they may be physically separated. Furthermore, the processing units in the system may be implemented in the form of a processor calling software; for example, the system includes a processor connected to a memory storing instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of the processing units in the system, wherein the processor is a general-purpose processor, such as a central processing unit or a microprocessor, and the memory is a memory within the system or a memory outside the system.

[0196] The following takes the main bridge of a certain bridge in Xuzhou City as an example to explain in detail the bridge maintenance evaluation method based on multi-source data fusion of this application, and at the same time verify the feasibility and beneficial effects of this application.

[0197] Bridge Overview: The main span of a certain bridge in Xuzhou City is a (65+105+65)m three-span prestressed concrete continuous box girder with variable height. The approach spans on both sides utilize 30m and 35m prestressed concrete composite box girders, initially simply supported and then continuous. The spans are arranged as 5×30+5×35+(65+105+65)+5×35+4×35+4×35+4×35+4×35+6×30m, with a total length of 1482.931m. The bridge was completed in 2011. During operation, inspections revealed numerous cracks in the concrete box girders, primarily longitudinal cracks in the top slab, diagonal cracks in the top slab, and diagonal cracks in the web, with a significant increase in the cracks. To ensure the safe service of the bridge, it was repaired and reinforced in 2020 using bonded steel plates. After the repair and reinforcement, the cracks in the concrete box girders were effectively addressed. In 2024, a post-evaluation of the repair and reinforcement of the Longhu Bridge was conducted using the evaluation method proposed in this application. The specific process is as follows:

[0198] Step 1: Establish a bridge maintenance evaluation indicator system.

[0199] With the maintenance and reinforcement effect as the goal, a bridge maintenance evaluation index system was established, including quantitative and qualitative indicators. The specific evaluation indicators at the criterion level and indicator level are shown in the following table;

[0200] Table 2 Evaluation indicators of the bridge maintenance evaluation index system

[0201]

[0202] Step 2: Considering the potential correlation between indicators, the weights of each evaluation indicator at the criterion layer and the indicator layer are calculated using decision-making test evaluation.

[0203] Here, we take the calculation of the weights of the evaluation indicators at the criterion layer as an example. In this embodiment, a 5-level scale {0, 1, 2, 3, 4} is set. The scale value is used to judge the strength of the indicator influence relationship. The direct influence matrix X1 is constructed by comparing the indicators pairwise:

[0204] ;

[0205] After normalizing the direct influence matrix, we get the normalized direct influence matrix X:

[0206] ;

[0207] Calculate the comprehensive impact matrix Y:

[0208] ;

[0209] Calculate the influence degree D of each indicator by the comprehensive influence matrix Y i , influence degree C i and centrality M i :

[0210] Table 3 Influence, influence and centrality of criterion level indicators

[0211]

[0212] By the centrality M i Calculate the weight ω of each indicator i :

[0213] ;

[0214] The calculated weight of technical performance (TP) is 0.25, the weight of economic benefit (EB) is 0.31, the weight of social benefit (SE) is 0.24, and the weight of implementation feasibility (IF) is 0.20.

[0215] The weight calculation process of each evaluation indicator at the indicator layer is the same as that shown above and will not be repeated here. The direct impact matrices of each evaluation indicator at the indicator layer belonging to technical performance (TP), economic benefits (EB), social benefits (SE), and implementation feasibility (IF) are:

[0216] ;

[0217] ;

[0218] ;

[0219] ;

[0220] The weight calculation results of each evaluation indicator are as follows:

[0221] Table 4 Weights of evaluation indicators in the post-maintenance evaluation index system of bridges

[0222]

[0223] Step 3: Through various methods, including regular inspections, health monitoring, and special inspections, we obtain inspection data from bridge repairs and reinforcements, establishing parameters for the evaluation indicators of technical performance (TP) and economic benefit (EB). Through research, we build a case library of concrete bridge reinforcements and determine the maximum and minimum values ​​of the evaluation indicators for technical performance and economic benefit within the case library. We perform positive normalization on positive indicators such as technical condition (TC) and investment benefit (EB), and reverse normalization on inverse indicators such as bearing capacity (BC), strength degradation (SC), stiffness degradation (SD), interface failure (IE), and investment scale (SI), using a normalization range of [0,100].

[0224] The specific process is as follows:

[0225] (1) Technical performance (TP)

[0226] Technical condition (TC): expressed by the BCI ratio of the bridge before and after reinforcement, with a parameter value of 1.1924;

[0227] Bearing capacity (BC): It is expressed by the ratio of the load test verification coefficient η after reinforcement to that before reinforcement, and the characterization parameter value is 0.7581;

[0228] Stiffness degradation (SD): expressed as the strain ε ratio of the key part of the bottom of the mid-span beam during the monitoring and evaluation period after reinforcement, with the characterization parameter value being 1.0655;

[0229] Strength degradation (SC): mid-span deformation S during monitoring and evaluation after reinforcement e The ratio indicates that the characterization parameter value is 1.0831;

[0230] Interface failure (IE): expressed as the ratio of the failure area to the total bonding area S, with a characterization parameter value of 0.0951;

[0231] The concrete bridge reinforcement case library shows that:

[0232] The maximum value of the BCI ratio is 1.2131 and the minimum value is 1.0515; the maximum value of the calibration coefficient η ratio is 0.8447 and the minimum value is 0.7376; the maximum value of the strain ε ratio is 1.2426 and the minimum value is 1.0438; the mid-span deformation S e The maximum value of the ratio is 1.2248, and the minimum value is 1.0563; the maximum value of the ratio of the failure area to the total pasted area S is 0.3125, and the minimum value is 0.0489;

[0233] From this, the standardized scores of technical condition (TC), bearing capacity (BC), stiffness degradation (SD), strength degradation (SC), and interface failure (IE) can be calculated as follows:

[0234] Technical Condition (TC): ;

[0235] Carrying capacity (BC): ;

[0236] Stiffness degradation (SD): ;

[0237] Strength degradation (SC): ;

[0238] Interface failure (IE): ;

[0239] (2) Economic Benefit (EB)

[0240] Investment scale (SI): expressed as the ratio of repair and reinforcement costs to bridge construction costs, with a characterization parameter value of 0.0184;

[0241] Investment benefit (IB): expressed as the maintenance investment benefit coefficient (i.e., the ratio of the maintenance investment benefit area factor to the structural bearing capacity maintenance area factor). The calculated maintenance investment benefit coefficient C is 0.1856;

[0242] The concrete bridge reinforcement case library shows that:

[0243] The maximum value of the ratio of repair and reinforcement costs to bridge construction costs is 0.0246, and the minimum value is 0.0172; the maximum value of the maintenance investment benefit coefficient is 0.2032, and the minimum value is 0.1386;

[0244] From this, the standardized scores of investment scale (SI) and investment benefit (IB) can be calculated as follows:

[0245] Investment size (SI): ;

[0246] Investment Benefit (IB): .

[0247] Step 4: Calculate the technical performance (TP) and economic benefit (EB) scores in the criterion layer.

[0248] ;

[0249] .

[0250] Step 5: Conduct fuzzy comprehensive evaluation of social benefits (SE) and implementation feasibility (IF).

[0251] This embodiment takes social benefit (SE) as an example for explanation; factor set: U 社会 = {environmental impact, low-carbon energy conservation, traffic interference, and traffic satisfaction}, with evaluation levels V = {excellent, good, moderate, poor, and poor}. Experts evaluated the factor set indicators and constructed a fuzzy comprehensive evaluation matrix R. This was done by consulting with key maintenance technicians from domestic large-span bridge maintenance and management units, senior engineers working in the bridge industry, and professors researching bridge maintenance at universities. A total of 50 expert questionnaires were distributed, of which 46 were returned, for a response rate of 92%.

[0252] ;

[0253] Social benefit indicator weight vector B3:

[0254] ;

[0255] Calculate the comprehensive evaluation vector B of social benefits 社会效益 :

[0256] ;

[0257] According to the comprehensive evaluation vector B of social benefits 社会效益 The social benefit (SE) score is calculated by weighted summing the scores represented by the evaluation level:

[0258] ;

[0259] Similarly, the quantitative score of implementation feasibility (IF) is calculated, and its fuzzy comprehensive evaluation matrix R is:

[0260] ;

[0261] The quantitative score of implementation feasibility (IF) is:

[0262] .

[0263] Step 6: Calculate the maintenance and reinforcement effect (EMR) score of the target layer.

[0264] ;

[0265] Rating calculation results:

[0266] Table 5 Evaluation index scores of the bridge post-maintenance evaluation index system

[0267]

[0268] Step 7: Determine the maintenance and reinforcement effect evaluation level and evaluation recommendations.

[0269] According to the final repair and reinforcement effect (EMR) score and Table 1, the repair and reinforcement effect (EMR) score of this embodiment is 87.49 points, indicating that the steel plate gluing repair and reinforcement technology recommendation for this bridge is the priority recommendation.

[0270] The above embodiments are intended only to illustrate the technical concepts and features of this application. Their purpose is to enable those skilled in the art to understand the content of this application and implement it accordingly. They are not intended to limit the scope of protection of this application. Those skilled in the art may make improvements and modifications without departing from the principles of this application, and such improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A bridge maintenance evaluation method based on multi-source data fusion, characterized by: Includes the following: S1: Construct a multi-source data fusion bridge maintenance evaluation index system, which includes the target layer, the criterion layer and the indicator layer; The target layer uses the repair and reinforcement effect as the target evaluation indicator; the criterion layer includes several first-level subordinate evaluation indicators subordinate to the target evaluation indicator, namely technical performance, economic benefits, social benefits and implementation feasibility; the indicator layer includes several second-level subordinate evaluation indicators subordinate to the first-level subordinate evaluation indicators; multiple subordinate evaluation indicators of the same evaluation indicator constitute a parallel indicator group; S2: Calculate the weights of the first-level subordinate evaluation indicators and the second-level subordinate evaluation indicators; S3: Establishing characterization parameters for the secondary subordinate evaluation indicators of technical performance and economic benefits, and calculating the index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits based on the characterization parameters; determining the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; dividing the secondary subordinate evaluation indicators of technical performance and economic benefits into positive indicators and inverse indicators based on the consistency relationship between the direction of change of the index values ​​and the target expectations, and standardizing the index values ​​of the positive indicators and inverse indicators respectively to obtain the standardized index values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; S4: Calculate the scores of the technical performance and economic benefits based on the standardized index values ​​and weights of the secondary subordinate evaluation indicators of the technical performance and economic benefits; S5: Use fuzzy comprehensive evaluation to calculate the scores of social benefits and implementation feasibility; specifically include the following: Establishing factor sets , N3 represents the number of secondary subordinate evaluation indicators corresponding to social benefits; Determine the evaluation level ,K represents the number of evaluation levels; Through the evaluation of factor set indicators by experts, the fuzzy comprehensive evaluation matrix R is constructed: ； Where z nk represents the number of experts who give evaluation grade k to the nth secondary subordinate evaluation indicator, m is the total number of experts participating in the evaluation, r nk Indicates the membership of the secondary subordinate evaluation indicator to each evaluation level, n∈[1,N3], k∈[1,K]; The weight of the indicator layer evaluation index corresponding to social benefits is B3={ , , , } and the fuzzy comprehensive evaluation matrix R to perform fuzzy synthesis and calculate the comprehensive evaluation vector B of social benefits 社会效益 : ； Setting social benefit evaluation levels The fractions represented by v1, v2, …… 、v K ; According to the comprehensive evaluation vector B of social benefits 社会效益 The social benefit (SE) score is calculated by weighted summing the scores represented by the evaluation level: ； Similarly, we can get the score of feasibility (IF): ； S6: Calculate the score of the target evaluation indicator based on the weights and scores of the evaluation indicators at the criterion layer; S7: Determine the evaluation grade and / or evaluation recommendation based on the scores of the target evaluation indicators.

2. The bridge maintenance evaluation method based on multi-source data fusion according to claim 1 is characterized by: In step S2, the weights of the first-level and second-level subordinate evaluation indicators in the criterion layer and the indicator layer are calculated using the decision-making test evaluation method, which specifically includes the following contents: Definition a ij Indicates the degree of influence of evaluation index i on evaluation index j, i, j∈[1,n], n represents the number of evaluation indicators in the same parallel indicator group; Set the scale value used to judge the degree of influence between evaluation indicators, and construct the influence matrix X1 by comparing the evaluation indicators pairwise: ； Normalize the influence matrix to obtain the standardized influence matrix X: ； Among them, max represents the maximum value of the sum of rows or columns of the influence matrix X1; Calculate the comprehensive impact matrix Y: ； Where E is the identity matrix; Calculate the influence D of evaluation index i by comprehensive influence matrix Y i , influence degree C i and centrality M i : ； ； ； By the centrality M i Calculate the weight ω of each evaluation indicator i : ； Thus, the weight of each first-level subordinate evaluation index in the criterion layer is obtained A={ , , , }, and the weight of the secondary subordinate evaluation indicator corresponding to the technical performance in the indicator layer B1={ , , , }、The weight of the secondary subordinate evaluation index corresponding to economic benefits is B2={ , , }、The weight of the secondary subordinate evaluation index corresponding to social benefits B3={ , , , }、The weight of the second-level subordinate evaluation indicator corresponding to the implementation feasibility is B4={ , , }, N1, N2, N3, and N4 represent the number of secondary subordinate evaluation indicators of technical performance, economic benefits, social benefits, and implementation feasibility, respectively.

3. The bridge maintenance evaluation method based on multi-source data fusion according to claim 1 is characterized by: In step S3, the secondary subordinate evaluation indicators corresponding to the technical performance include technical status, load-bearing capacity, stiffness degradation, strength degradation, and interface failure; the secondary subordinate evaluation indicators corresponding to the economic benefits include investment scale and investment benefits; The technical condition is expressed by the ratio of the technical condition scores (BCI) before and after the bridge reinforcement. The calculation formula of the bridge technical condition score (BCI) is as follows: ； Where, BCI m 、BCI s 、BCI x Represent the technical condition scores of the bridge deck system, superstructure and substructure respectively; ω m 、ω s 、ω x Respectively represent the weights of the bridge deck system, superstructure, and substructure, and are determined according to the specifications; The load-bearing capacity is expressed by the ratio of the load test verification coefficient η before and after reinforcement. The calculation formula of the load test verification coefficient η is as follows: ； Where S s It represents the maximum calculated effect value of the internal force or displacement of the loading control section corresponding to the loading test under the action of the static load test; P represents the calculated value of the most unfavorable effect of the internal force or displacement of the same loading control section produced by the control load; μ represents the impact coefficient value taken according to the specification; The strength degradation is represented by the ratio of the strain ε at the key part of the bottom of the mid-span beam during the monitoring and evaluation period after reinforcement, where the strain ε at the key part of the bottom of the mid-span beam is a measured indicator; The stiffness degradation is expressed by the ratio of the mid-span deformation Se during the monitoring and evaluation period after reinforcement, where the mid-span deformation Se is a measured indicator; The interface failure is represented by the ratio of the failure area to the total bonding area S; The investment scale is expressed as the ratio of repair and reinforcement costs to bridge construction costs; The investment benefit is expressed by the maintenance investment benefit coefficient.

4. The bridge maintenance evaluation method based on multi-source data fusion according to claim 3 is characterized by: The calculation process of the maintenance investment benefit coefficient is as follows: Establish a concrete bridge bearing capacity assessment and prediction model to determine the bridge bearing capacity decay curve before and after maintenance and reinforcement; The maintenance investment benefit area factor S is calculated based on the area enclosed by the bridge bearing capacity decay curve before and after maintenance and reinforcement during the assessment period. z ; ； The structural bearing capacity retention area factor S is calculated using the bridge bearing capacity decay curve before maintenance and reinforcement during the assessment period and the area enclosed by the horizontal axis. y ; ； Among them, f(D a ) is the decay curve function of the bridge bearing capacity before maintenance and reinforcement, f(D b ) is the decay curve function of the bridge bearing capacity after maintenance and reinforcement, t a is the maintenance and reinforcement time, t d is the post-evaluation time, t a to t d This is the post-maintenance evaluation period; The ratio of the maintenance investment benefit area factor and the structural bearing capacity maintenance area factor is the maintenance investment benefit coefficient C: ； Among them, S z is the area factor of bridge maintenance investment benefit, S y Maintain area factor for structural capacity.

5. The bridge maintenance evaluation method based on multi-source data fusion according to claim 3 is characterized by: In step S3, the technical status and investment benefits among the secondary subordinate evaluation indicators are positive indicators, and the indicator values ​​of the positive indicators are normalized using the following formula, with the normalization range being [0,100]: ； The load-bearing capacity, stiffness degradation, strength degradation, interface failure and investment scale in the secondary subordinate evaluation indicators are inverse indicators. The following formula is used to standardize the index values ​​of the inverse indicators, and the standardization range is [0,100]: ； Where, I represents the parameter value representing technical status, bearing capacity, stiffness degradation, strength degradation, interface failure, investment scale, and investment benefit, i.e., the index value; max , I min They represent the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits in the concrete bridge case library respectively; Through the above standardization processing, the standardized index values ​​of technical status, bearing capacity, stiffness degradation, strength degradation, interface failure, investment scale and investment benefit are obtained.

6. The bridge maintenance evaluation method based on multi-source data fusion according to claim 1 is characterized by: The secondary subordinate evaluation indicators corresponding to the social benefits include environmental impact, low-carbon energy saving, traffic interference, and traffic satisfaction.

7. The bridge maintenance evaluation method based on multi-source data fusion according to claim 1 is characterized by: The second-level subordinate evaluation indicators corresponding to the implementation feasibility include process convenience and material availability.

8. The bridge maintenance evaluation method based on multi-source data fusion according to claim 1 is characterized by: In step S7, the evaluation levels are divided according to the scoring range of the target evaluation indicators, and recommendations are given; the corresponding relationship between the scoring range, evaluation level, and evaluation recommendations is as follows: When the score range is [80,100], the evaluation level is Level I and the recommendation is "priority recommendation"; When the score range is [60,80], the assessment level is II and the recommendation is "recommended"; When the score range is [0,60], the assessment level is III and the recommendation is "not recommended".

9. A system for executing the method according to any one of claims 1 to 8, characterized in that: include: Evaluation index system construction module, used to construct a multi-source data fusion bridge maintenance evaluation index system, which includes a target layer, a criterion layer, and an indicator layer; A weight calculation module, used to calculate the weights of the evaluation indicators of the criterion layer and the indicator layer; A standardized indicator value calculation module is used to establish characterization parameters of the secondary subordinate evaluation indicators of technical performance and economic benefits, and calculate the indicator values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits based on the characterization parameters; Determine the maximum and minimum values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; divide the secondary subordinate evaluation indicators of technical performance and economic benefits into positive indicators and inverse indicators according to the consistency relationship between the direction of change of the indicator values ​​and the target expectations; standardize the indicator values ​​of the positive indicators and inverse indicators respectively to obtain the standardized indicator values ​​of the secondary subordinate evaluation indicators of technical performance and economic benefits; The first scoring module of the criterion layer calculates the scores of the technical performance and economic benefits based on the standardized index values ​​and weights of the secondary subordinate evaluation indicators of technical performance and economic benefits; The second scoring module of the criterion layer uses fuzzy comprehensive evaluation to calculate the scores of social benefits and implementation feasibility; The target layer scoring module calculates the score of the target evaluation indicator according to the weight and score of each evaluation indicator in the criterion layer; The evaluation result output module determines the evaluation grade and / or evaluation recommendations based on the scores of the target evaluation indicators.

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