A method and system for maintenance evaluation of expansion joints of in-service concrete bridges

Abstract

This invention proposes a method and system for evaluating the maintenance of expansion joints in in-service concrete bridges, relating to the field of engineering testing technology. The method includes: acquiring the structural functional parameters of the expansion joint to be tested; quantifying the structural functional parameters based on engineering standards to obtain corresponding structural functional indicators; using an improved NSGA-II optimization algorithm to construct a one-dimensional hybrid encoding string from the initial weights and initial structural safety correction coefficients of each structural functional indicator, embedding it into engineering constraints to complete numerical mapping; within the same non-dominated frontier layer, selecting individuals in descending order of dynamic weighted crowding, and adaptively adjusting the crossover and mutation probabilities based on the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weight of each indicator; using the optimized weights to perform weighted fusion of each structural functional indicator to obtain the construction and maintenance functional indicators; and combining the benefit-cost indicators and the optimized structural safety correction coefficient to determine the maintenance and control level of the expansion joint to be tested.

Description

Technical Field

[0001] This invention relates to the field of engineering testing technology, and in particular to a method and system for evaluating the maintenance of expansion joints in in-service concrete bridges. Background Technology

[0002] Bridge expansion joints are core components for beam end deformation coordination, vehicle transition, and waterproofing protection. They are subjected to the coupled effects of vehicle reciprocating loads, temperature expansion and contraction, freeze-thaw salt corrosion, and uneven foundation settlement over a long period of time. They are prone to various defects such as deformation failure, anchorage loosening, sealing leakage, vehicle bouncing, and material aging, which directly affect the safety of the main bridge structure and the level of road traffic service.

[0003] Currently, bridge operation and maintenance generally faces industry pain points such as reliance on manual experience in assessing expansion joint maintenance, subjective assignment of indicator weights, insufficient consideration of structural safety correlations, and a lack of quantitative models for maintenance level classification. Existing evaluation methods, such as traditional analytic hierarchy process (AHP) and expert scoring, are often heavily influenced by subjective human experience in judging various influencing indicators, failing to balance the priority of structural safety with the suitability of engineering constraints. Their weights lack theoretical optimization basis and cannot comprehensively represent the overall service status of expansion joints, leading to difficulties in timely maintenance and even safety hazards. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for evaluating the maintenance of expansion joints in in-service concrete bridges.

[0005] The technical solution of this invention is implemented as follows: The first aspect of this invention provides a method for evaluating the maintenance of expansion joints in in-service concrete bridges, comprising: Obtain the structural functional parameters of the expansion joint to be tested; the structural functional parameters include parameters related to deformation adaptation, parameters related to anchoring reliability, parameters related to sealing and waterproofing, parameters related to driving smoothness, parameters related to maintenance convenience, and parameters related to environmental durability; Based on engineering standards, the structural functional parameters are quantified to obtain corresponding structural functional indicators. An improved NSGA-II optimization algorithm is then used to construct a one-dimensional hybrid encoding string containing the initial weights and initial structural safety correction coefficients of each structural functional indicator. Engineering constraints are embedded in this one-dimensional hybrid encoding string to complete numerical mapping. Within the same non-dominated frontier layer, individuals are selected in descending order of dynamic weighted crowding. Crossover and mutation probabilities are adaptively adjusted based on the population fitness value distribution to determine the optimized structural safety correction coefficients and the optimized weights of each structural functional indicator. The initial structural safety correction coefficients represent the correlation adjustment coefficients of overall structural operating conditions, service risks, and disease correlation. The structural function indicators are weighted and fused using the optimized weights to obtain the construction and maintenance function indicators. The maintenance and management level of the expansion joint to be tested is determined by combining the benefit-cost indicators and the optimized structural safety correction coefficient. The benefit-cost indicators characterize the input-output efficiency of the maintenance plan.

[0006] Based on the above technical solutions, preferably, the step of quantifying the structural functional parameters based on engineering standards to obtain corresponding structural functional indicators includes: Based on engineering standards, multiple parameter ranges of different types and sizes are defined, along with the corresponding structural and functional indicators for each parameter range. The corresponding structural function index is determined based on the parameter range in which each of the aforementioned structural function parameters falls.

[0007] Based on the above technical solutions, preferably, the step of using the improved NSGA-II optimization algorithm to construct a one-dimensional hybrid encoding string for the initial weights of each of the structural function indicators and the initial structural safety correction coefficients, and embedding engineering constraints into the one-dimensional hybrid encoding string to complete the numerical mapping, includes: Based on engineering constraints, the initial weights of each of the structural function indicators are normalized to determine the first numerical range to which each initial weight belongs, and the initial structural safety correction coefficient is mapped to a second numerical range; the second numerical range includes at least a portion of the neighborhood of the value 1.

[0008] Based on the above technical solutions, preferably, within the same non-dominated frontier layer, individuals are screened in descending order of dynamic weighted crowding, and the crossover and mutation probabilities are adaptively adjusted according to the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weight of each of the structural functional indicators, including: The initial weights of each of the structural function indicators are calculated using the target space normalized distance summation to determine the individual crowding degree. The individual crowding is dynamically weighted based on the priority of each of the aforementioned structural function indicators to obtain a corresponding dynamically weighted crowding. The target-related parameters and non-target-related parameters are then divided based on the dynamically weighted crowding. The dynamically weighted crowding of the target-related parameters is greater than that of the non-target-related parameters. Within the same non-dominated frontier layer, the neighborhood screening decision interval for target-related parameters is reduced, while the neighborhood screening decision interval for non-target-related parameters is increased. Individual screening is then completed by combining this with a preset threshold.

[0009] Based on the above technical solutions, preferably, the target-related parameters include anchoring reliability-related parameters and sealing and waterproofing-related parameters, and the non-target-related parameters include deformation adaptation-related parameters, driving smoothness-related parameters, maintenance convenience-related parameters, and environmental durability-related parameters; the step of reducing the neighborhood screening judgment interval for target-related parameters and increasing the neighborhood screening judgment interval for non-target-related parameters includes: Narrow the neighborhood selection intervals corresponding to the anchoring reliability parameters and the sealing and waterproofing parameters, and increase the neighborhood selection intervals corresponding to the deformation adaptation parameters, the driving smoothness parameters, the maintenance convenience parameters, and the environmental durability parameters.

[0010] Based on the above technical solutions, preferably, the step of screening individuals in descending order of dynamic weighted crowding degree within the same non-dominated front layer, and adaptively adjusting the crossover probability and mutation probability according to the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weight of each of the structural function indicators, further includes: A global strength mutation operator is introduced into the NSGA-II optimization algorithm to increase the mutation probability when the NSGA-II optimization algorithm gets stuck in a local optimum and to increase the crossover probability when the population tends to converge.

[0011] Based on the above technical solutions, preferably, the step of using the optimized weights to weight and fuse the various structural functional indicators to obtain the construction and maintenance functional indicators, and combining the benefit-cost indicators and the optimized structural safety correction coefficient to determine the maintenance and management level of the expansion joint to be tested, includes: The evaluation index for the entire time domain is determined by multiplying the construction and maintenance function index, the benefit-cost index, and the optimized structural safety correction coefficient. The maintenance and management level of the expansion joint to be tested is determined based on the range of the full-time evaluation index within the preset specification requirements.

[0012] Furthermore, a second aspect of the present invention provides an evaluation system for the maintenance of expansion joints in in-service concrete bridges, comprising: a parameter acquisition module, a weight optimization module, and a grade determination module; wherein, The parameter acquisition module is configured to acquire the structural functional parameters of the expansion joint to be tested; the structural functional parameters include deformation adaptation related parameters, anchoring reliability related parameters, sealing and waterproofing related parameters, driving smoothness related parameters, maintenance convenience related parameters, and environmental durability related parameters. The weight optimization module is configured to quantify the structural function parameters based on engineering standards to obtain corresponding structural function indicators. It then uses an improved NSGA-II optimization algorithm to construct a one-dimensional hybrid encoding string containing the initial weights and initial structural safety correction coefficients for each structural function indicator. Engineering constraints are embedded into this one-dimensional hybrid encoding string to complete the numerical mapping. Within the same non-dominated frontier layer, individuals are selected in descending order of dynamic weighted crowding. The crossover and mutation probabilities are adaptively adjusted based on the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weights for each structural function indicator. The initial structural safety correction coefficient represents the correlation adjustment coefficient of the overall structural condition, service risk, and disease correlation. The level determination module is configured to use the optimized weights to perform weighted fusion on each of the structural function indicators to obtain the construction and maintenance function indicators, and combine the benefit-cost indicators and the optimized structural safety correction coefficient to determine the maintenance and control level of the expansion joint to be tested; the benefit-cost indicators characterize the input-output efficiency of the maintenance plan.

[0013] More preferably, a third aspect of the present invention provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the maintenance and evaluation method for expansion joints of in-service concrete bridges as described in the first aspect.

[0014] More preferably, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the maintenance and evaluation method for expansion joints of in-service concrete bridges as described in the first aspect.

[0015] The maintenance evaluation method and system for expansion joints of in-service concrete bridges of the present invention have the following advantages over the prior art: 1. By uniformly collecting six categories of structural functional parameters—deformation adaptation, anchoring reliability, sealing and waterproofing, driving smoothness, maintenance convenience, and environmental durability—and relying on engineering standards to complete the standardized index quantification, a unified conversion of multi-dimensional service parameters from measured data to standardized functional indicators is achieved. Combined with the improved NSGA-II algorithm, the initial weights of the six indicators and the structural safety correction coefficient are used to construct a one-dimensional hybrid encoding string, which is embedded with engineering constraints for numerical mapping. By employing same-layer dynamic weighted crowding descending order screening, adaptive crossover mutation probability, and key subspace differentiated population distribution control, the system intelligently solves for optimized weights and optimized safety correction coefficients that meet engineering rules and conform to structural safety priorities, thus obtaining construction and maintenance functional indicators. At the same time, benefit-cost indicators are introduced, and the optimized structural safety correction coefficients are coupled to jointly determine the maintenance and management level. This eliminates the reliance on expert subjective scoring, objectively and accurately obtains the overall service status of the expansion joint, and reduces safety hazards.

[0016] 2. By introducing and optimizing the structural safety correction coefficient, the adjustment effect of the overall structural working condition, service load level, risk of chain spread of defects, and environmental corrosion level of the bridge is quantitatively characterized. This realizes the correction mapping of the evaluation of local indicators of expansion joint components to the safety risk of the entire bridge structure, making up for the defect of only evaluating components without considering the overall structure, and ensuring the reliability of the evaluation results.

[0017] 3. Individuals are screened in descending order of dynamic weighted crowding at the same non-dominated front layer. The screening interval is reduced and the retention threshold is relaxed for core subspaces with reliable anchoring and waterproof sealing, and the population density distribution is differentially controlled. The crossover and mutation probabilities are adaptively adjusted in combination with the population fitness distribution to improve the algorithm's global search and local fine optimization capabilities, ensuring the engineering adaptability and diversity of Pareto front solutions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for evaluating the maintenance of expansion joints in in-service concrete bridges, provided as an embodiment of the present invention. Figure 2 A schematic diagram of a maintenance evaluation system for expansion joints of in-service concrete bridges provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] In some embodiments, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for evaluating the maintenance of expansion joints in in-service concrete bridges, provided by an embodiment of the present invention. The method includes: S110, obtain the structural functional parameters of the expansion joint to be tested; the structural functional parameters include parameters related to deformation adaptation, parameters related to anchoring reliability, parameters related to sealing and waterproofing, parameters related to driving smoothness, parameters related to maintenance convenience, and parameters related to environmental durability.

[0022] S120 quantifies various structural function parameters based on engineering standards to obtain corresponding structural function indicators. An improved NSGA-II optimization algorithm is then used to construct a one-dimensional hybrid encoding string containing the initial weights and initial structural safety correction coefficients for each structural function indicator. Engineering constraints are embedded in this one-dimensional hybrid encoding string to complete numerical mapping. Within the same non-dominated frontier layer, individuals are selected in descending order of dynamic weighted crowding. Crossover and mutation probabilities are adaptively adjusted based on the population fitness value distribution to determine the optimized structural safety correction coefficients and the optimized weights for each structural function indicator. The initial structural safety correction coefficients characterize the correlation adjustment coefficients of overall structural operating conditions, service risks, and disease correlation.

[0023] S130 uses optimized weights to weight and fuse various structural function indicators to obtain construction and maintenance function indicators. Combined with benefit-cost indicators and optimized structural safety correction coefficients, the maintenance and control level of the expansion joint to be tested is determined. The benefit-cost indicators characterize the input-output efficiency of the maintenance plan.

[0024] In this embodiment, deformation adaptation parameters may include expansion and contraction, displacement adaptation capacity, and temperature deformation adaptation range; anchoring reliability parameters may include anchoring depth, corrosion degree of connectors, anchoring loosening rate, and concrete bonding state; sealing and waterproofing parameters may include sealing strip aging degree, monthly seepage frequency, sealing gap, and waterproofing structural integrity; driving smoothness parameters may include expansion joint height difference, road surface smoothness, and vehicle impact coefficient; maintenance convenience parameters may include disassembly and assembly difficulty, component standardization degree, and maintenance accessibility; environmental durability parameters may include corrosion resistance, aging resistance, adaptability to temperature, humidity, and acid / alkali environments, and fatigue service life. Based on current highway / bridge engineering standards, the collected structural functional parameters can be converted into standardized, dimensionless structural functional indicators.

[0025] The initial weights of each structural function index and the initial structural safety correction factors representing the structural working conditions, service risks, and disease correlation degrees are combined to construct a one-dimensional hybrid coding string. Engineering constraint conditions such as engineering specification limits, parameter value ranges, weights, and constraints are embedded in the coding string to complete the parameter numerical mapping. Combining with the improved NSGA-II optimization algorithm, the initial weights and the initial structural safety correction factors are optimized. For the traditional NSGA-II optimization algorithm, its fast non-dominated sorting performs Pareto stratification on the expansion joint weight solution set according to the multi-objective superiority and inferiority, classifies the high-quality weights that are not dominated by other solutions into the optimal front layer, and realizes the automatic hierarchical screening of the weight solutions from good to bad. The elite retention combines the high-quality weights of the parent generation and the new weights of the offspring and selects the best uniformly, forcing the high-quality solutions in the optimal front layer to be retained without loss, ensuring the stable convergence of the weight optimization and not falling into the local optimum.

[0026] Here, after non-dominated sorting and stratification, high-quality individuals are screened in descending order of dynamic weighted crowding degree within the same front layer to ensure population diversity. The crossover probability and mutation probability are adaptively adjusted according to the population fitness distribution to avoid premature convergence of the algorithm, and then the optimized weights corresponding to each structural function index and the optimized structural safety correction factors are output. The optimized weights are used to perform weighted summation and fusion on the six structural function indexes to generate the construction and maintenance function index. In addition, by measuring the maintenance input, traffic loss, and later maintenance cost of different maintenance plans, the benefit-cost index representing the input-output efficiency is quantitatively obtained. The construction and maintenance function index, the benefit-cost index, and the optimized structural safety correction factors are coupled and comprehensively evaluated, and according to the preset grading threshold, the maintenance control level of the待测 expansion joint is finally determined.

[0027] In some embodiments, based on engineering standards, each structural function parameter is quantified into an index to obtain the corresponding structural function index, including: Dividing multiple parameter ranges of different types and sizes based on engineering standards, and the corresponding structural function index for each parameter range; Determining the corresponding structural function index based on the parameter range where each structural function parameter is located.

[0028] Taking the deformation adaptation-related parameter as the expansion amount L (mm) as an example, when L ≤ 20mm, the corresponding structural function index is 9.0; when 20mm < L ≤ 40mm, the corresponding structural function index is 7.5; when 40mm < L ≤ 60mm, the corresponding structural function index is 5.0; when L > 60mm, the corresponding structural function index is 2.0. If the measured expansion amount L = 32mm falls within the interval 20mm < L ≤ 40mm, then the structural function index corresponding to the deformation adaptation-related parameter can be determined to be 7.5. It should be noted that if multiple deformation adaptation-related parameters are considered, the corresponding structural function index can be the index mean.

[0029] Taking the monthly seepage frequency N as an example of a sealing and waterproofing related parameter, N=0 corresponds to a structural performance index of 9.5; 1≤N≤2 corresponds to a structural performance index of 7.2; 3≤N≤5 corresponds to a structural performance index of 4.8; and N≥6 corresponds to a structural performance index of 2.2. If the measured monthly seepage frequency N=4 falls within the range of 3≤N≤5, then the structural performance index of the sealing and waterproofing related parameter can be determined to be 4.8. It should be noted that if multiple sealing and waterproofing related parameters are considered, the corresponding structural performance index can be the average of the indicators. The quantitative standards for other types of structural performance parameters are similar.

[0030] In some embodiments, the improved NSGA-II optimization algorithm is used to construct a one-dimensional hybrid encoding string using the initial weights of each structural function index and the initial structural safety correction coefficients. Engineering constraints are then embedded into this one-dimensional hybrid encoding string to complete the numerical mapping, including: Based on engineering constraints, the initial weights of each structural function index are normalized to determine the first numerical range to which each initial weight belongs, and the initial structural safety correction coefficient is mapped to the second numerical range; the second numerical range includes at least a part of the neighborhood of the value 1.

[0031] Weight normalization constraint formula: ; In the formula, These are the weighting coefficients for parameters related to deformation adaptation, anchoring reliability, sealing and waterproofing, driving smoothness, maintenance convenience, and environmental durability.

[0032] For the initial structural safety correction factor It can be constrained to the interval [0.9, 1.1], that is, 0.9 ≤ ≤1.1.

[0033] For example, , =0.95.

[0034] In some embodiments, within the same non-dominated front layer, individuals are screened in descending order of dynamically weighted crowding, and the crossover and mutation probabilities are adaptively adjusted according to the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weight of each structural function index, including: The initial weights of each structural function index are calculated using the target space normalized distance summation to determine the individual crowding degree. Individual crowding is dynamically weighted based on the priority of various structural and functional indicators to obtain the corresponding dynamic weighted crowding. The dynamic weighted crowding is then used to classify target-related parameters and non-target-related parameters. The dynamic weighted crowding of target-related parameters is greater than that of non-target-related parameters. Within the same non-dominated frontier layer, the neighborhood screening decision interval for target-related parameters is reduced, while the neighborhood screening decision interval for non-target-related parameters is increased. Individual screening is then completed by combining this with a preset threshold.

[0035] Individual crowding is calculated using the sum of normalized distances in the target space. The basic formula is: ; in, Let M be the individual crowding level, and M be the target number for optimization. Let be the function value of the i-th individual on the m-th objective. , This represents the extreme value corresponding to the target.

[0036] A dynamic weighted coefficient for performance importance is introduced, assigning high weights to parameters relevant to the target and basic weights to secondary performance targets, thus constructing a dynamic weighted congestion degree: ; in, For dynamic weighting coefficients, the target-related parameters are taken as follows: Non-target related parameters are taken Engineering-oriented distance calculation is achieved through weighted correction.

[0037] In addition, within the same non-dominated frontier layer, all individuals are sorted from largest to smallest according to their dynamic weighted crowding degree; the larger the crowding degree, the sparser the individual's neighborhood, the stronger its representativeness, and the higher its engineering value, so it is given priority to be retained, while individuals with low crowding degree and redundant clusters are eliminated, thus completing elite selection and population truncation.

[0038] In some embodiments, target-related parameters include anchoring reliability-related parameters and sealing and waterproofing-related parameters, while non-target-related parameters include deformation adaptation-related parameters, driving smoothness-related parameters, maintenance convenience-related parameters, and environmental durability-related parameters; narrowing the neighborhood selection judgment interval for target-related parameters and increasing the neighborhood selection judgment interval for non-target-related parameters includes: Reduce the neighbor selection interval for anchoring reliability-related parameters and sealing and waterproofing-related parameters, and increase the neighbor selection interval for deformation adaptation-related parameters, driving smoothness-related parameters, maintenance convenience-related parameters and environmental durability-related parameters.

[0039] In this embodiment, considering the high importance of anchoring reliability and sealing / waterproofing parameters, the individual screening interval is reduced to make the individuals in the corresponding subspace more densely arranged, thus refining the optimal weight combination. For deformation adaptation, driving smoothness, maintenance convenience, and environmental durability parameters, the screening interval is increased and the retention threshold is tightened to reduce redundant individuals and avoid wasting population resources.

[0040] In some embodiments, within the same non-dominated front layer, individuals are screened in descending order of dynamically weighted crowding, and the crossover and mutation probabilities are adaptively adjusted according to the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimization weight of each structural function index, further comprising: A global strength mutation operator is introduced into the NSGA-II optimization algorithm to increase the mutation probability when the NSGA-II optimization algorithm gets stuck in a local optimum and to increase the crossover probability when the population tends to converge.

[0041] The global intensity mutation operator takes functional weights and structural safety correction coefficients as its computational objects, and takes the following form: ; In the formula, The weight of the i-th functional term before mutation; The weights after mutation; =1、 =0 represents the weight constraint boundary; To adapt the mutation probability, the value is set to... It is dynamically adjusted with the number of iterations; It is a global perturbation factor, dynamically determined to match the population convergence state.

[0042] Variation forms of structural safety correction factors: ; in, This is the structural safety correction factor before the mutation; 1.1 and 0.9 are the coefficients after variation; 1.1 and 0.9 are the upper and lower limits of the structural safety correction coefficients.

[0043] Based on the population fitness function value and the defect level (DefectLevel), when the algorithm converges and stalls, and the weighted solutions are concentrated in a local interval, the mutation probability is increased to the upper limit of 0.05 to enhance the global search; when the population tends to stabilize, the mutation probability is decreased to the lower limit of 0.01 to focus the search. and Fine-grained optimization. After mutation, weight normalization correction and safety factor interval truncation are automatically performed to ensure that all solutions strictly satisfy the constraints and that there are no invalid parameters output.

[0044] In an optional embodiment, the structural safety correction factor The solution formula is: ; Among them, DefectLevel is the level of defect in the expansion joint (1=mild, 2=moderate, 3=severe, 4=major). The value range is 0.9 to 1.1.

[0045] In some embodiments, the structural function indicators are weighted and fused using optimized weights to obtain the construction and maintenance function indicators. The maintenance and management level of the expansion joint under test is then determined by combining the benefit-cost indicators and the optimized structural safety correction coefficient, including: The evaluation index for the entire time domain is determined by multiplying the construction and maintenance function index, the benefit and cost index, and the optimized structural safety correction coefficient. The maintenance and management level of the expansion joint to be tested is determined based on the range of the evaluation indicators in the preset specifications.

[0046] In this embodiment, the construction and maintenance function indicators It can be represented as: ; in, The parameters are, in order: deformation adaptation, anchoring reliability, sealing and waterproofing, driving smoothness, maintenance convenience, and environmental durability. The weights of each parameter.

[0047] Benefit-cost indicators The input-output efficiency of a maintenance program is quantitatively characterized by the following formula: ; in, The performance improvement score reflects the degree of functional recovery of the expansion joint after maintenance, and the value ranges from 0 to 100. Costs of materials, labor, and machinery; Costs for traffic closures, vehicle delays, and road network disruptions: yuan; This represents an increase in subsequent maintenance costs across all time zones.

[0048] Full-time domain evaluation indicators It can be represented as: .

[0049] Based on the full-time domain evaluation indicators Based on numerical values ​​and the current relevant specifications for highway bridge expansion joints, in-service expansion joints can be classified into four levels of maintenance and management: Grade I (OEI≥70): Excellent structural performance, minor defects, functional requirements met, and can be maintained by routine maintenance; Level II (55≤OEI<70): The structure is in good condition, but there are minor defects, requiring preventative maintenance. Level III (40≤OEI<55): Structural performance is moderate, with obvious defects and functional degradation, requiring specialized repairs or partial replacement; Level IV (OEI<40): Poor structural performance, with serious defects or failures, requiring complete replacement and re-selection.

[0050] In one example, the target of the inspection was an engineering information bridge, specifically a T-beam bridge with a width of 12m; the expansion joints were modular and had been in service for 13 years; the defects included cracked anchor concrete, obvious water seepage, and unevenness exceeding the standard.

[0051] Construction and Maintenance Function Index (FCI) Calculation: The weighting coefficients were determined by an improved NSGA-II algorithm and are consistent with typical values. FCI=0.22×65+0.20×60+0.18×55+0.16×58+0.12×72+0.12×63=14.3+12.0+9.9+9.28+8.64+7.56=61.68.

[0052] ECI (Effectiveness-Cost Index) calculation: =58, =36,800 yuan =18500 yuan, =9200 yuan, calculated based on the performance degradation law obtained by combining the improved algorithm.

[0053] Total cost = 64500 =58 / 64500≈0.000899.

[0054] Calculation of the OEI (Outstanding Evaluation Index) across the entire time domain: =0.95 (value optimized by improving the algorithm to suit medium defect safety requirements), OEI=61.68×0.000899×0.95≈0.0523, engineering scale-up judgment value: 52.3 Therefore, the conclusion is that the maintenance and management level is Level III, requiring special repairs or partial replacements: anchor repair, waterproofing redo, and leveling.

[0055] In some embodiments, please refer to Figure 2 , Figure 2 This is a structural schematic diagram of an evaluation system for the maintenance of expansion joints in in-service concrete bridges, provided in an embodiment of the present invention. The present invention provides an evaluation system 200 for the maintenance of expansion joints in in-service concrete bridges, comprising: a parameter acquisition module 210, a weight optimization module 220, and a grade determination module 230; wherein,

[0056] The parameter acquisition module 210 is configured to acquire the structural functional parameters of the expansion joint to be tested; the structural functional parameters include parameters related to deformation adaptation, parameters related to anchoring reliability, parameters related to sealing and waterproofing, parameters related to driving smoothness, parameters related to maintenance convenience, and parameters related to environmental durability. The weight optimization module 220 is configured to quantify various structural function parameters based on engineering standards to obtain corresponding structural function indicators. It then uses an improved NSGA-II optimization algorithm to construct a one-dimensional hybrid encoding string containing the initial weights of each structural function indicator and the initial structural safety correction coefficient. Engineering constraints are embedded in this one-dimensional hybrid encoding string to complete the numerical mapping. Within the same non-dominated frontier layer, individuals are selected in descending order of dynamic weighted crowding. The crossover and mutation probabilities are adaptively adjusted based on the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weights of each structural function indicator. The initial structural safety correction coefficient represents the correlation adjustment coefficient of the overall structural condition, service risk, and disease correlation. The level determination module 230 is configured to use optimized weights to perform weighted fusion of various structural function indicators to obtain construction and maintenance function indicators, and combine the benefit-cost indicators and the optimized structural safety correction coefficient to determine the maintenance and control level of the expansion joint to be tested; the benefit-cost indicators characterize the input-output efficiency of the maintenance plan.

[0057] In some embodiments, the weight optimization module 220 is specifically configured as follows: Based on engineering standards, multiple parameter ranges of different types and sizes are defined, along with the corresponding structural and functional indicators for each parameter range. The corresponding structural function indicators are determined based on the parameter range in which each structural function parameter falls.

[0058] In some embodiments, the weight optimization module 220 is specifically configured as follows: Based on engineering constraints, the initial weights of each structural function index are normalized to determine the first numerical range to which each initial weight belongs, and the initial structural safety correction coefficient is mapped to the second numerical range; the second numerical range includes at least a part of the neighborhood of the value 1.

[0059] In some embodiments, the weight optimization module 220 is specifically configured as follows: The initial weights of each structural function index are calculated using the target space normalized distance summation to determine the individual crowding degree. Individual crowding is dynamically weighted based on the priority of various structural and functional indicators to obtain the corresponding dynamic weighted crowding. The dynamic weighted crowding is then used to classify target-related parameters and non-target-related parameters. The dynamic weighted crowding of target-related parameters is greater than that of non-target-related parameters. Within the same non-dominated frontier layer, the neighborhood screening decision interval for target-related parameters is reduced, while the neighborhood screening decision interval for non-target-related parameters is increased. Individual screening is then completed by combining this with a preset threshold.

[0060] In some embodiments, target-related parameters include anchoring reliability-related parameters and sealing and waterproofing-related parameters, while non-target-related parameters include deformation adaptation-related parameters, driving smoothness-related parameters, maintenance convenience-related parameters, and environmental durability-related parameters; the weight optimization module 220 is specifically configured as follows: Reduce the neighbor selection interval for anchoring reliability-related parameters and sealing and waterproofing-related parameters, and increase the neighbor selection interval for deformation adaptation-related parameters, driving smoothness-related parameters, maintenance convenience-related parameters and environmental durability-related parameters.

[0061] In some embodiments, the weight optimization module 220 is specifically configured as follows: A global strength mutation operator is introduced into the NSGA-II optimization algorithm to increase the mutation probability when the NSGA-II optimization algorithm gets stuck in a local optimum and to increase the crossover probability when the population tends to converge.

[0062] In some embodiments, the grade determination module 230 is specifically configured as follows: The evaluation index for the entire time domain is determined by multiplying the construction and maintenance function index, the benefit and cost index, and the optimized structural safety correction coefficient. The maintenance and management level of the expansion joint to be tested is determined based on the range of the evaluation indicators in the preset specifications.

[0063] It should be noted that the in-service concrete bridge expansion joint maintenance evaluation system and the in-service concrete bridge expansion joint maintenance evaluation method provided in this application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned in-service concrete bridge expansion joint maintenance evaluation method, and the repeated parts will not be described again.

[0064] In some embodiments, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 300 provided in this embodiment includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program, when executed by the processor, implements the aforementioned method for evaluating the maintenance of expansion joints in in-service concrete bridges.

[0065] Specifically, processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. Processor 310 may also include onboard memory for caching purposes. Processor 310 may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0066] The memory 320 may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, the memory 320 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, apparatuses, or propagation media. Specific examples of the memory 320 include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and may also be random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0067] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned method for evaluating the maintenance of expansion joints in in-service concrete bridges. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0068] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0069] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. A method for maintenance evaluation of a concrete bridge expansion joint in service, characterized by, include: Obtain the structural functional parameters of the expansion joint to be tested; the structural functional parameters include parameters related to deformation adaptation, parameters related to anchoring reliability, parameters related to sealing and waterproofing, parameters related to driving smoothness, parameters related to maintenance convenience, and parameters related to environmental durability; Based on engineering standards, the structural function parameters are quantified to obtain corresponding structural function indicators. An improved NSGA-II optimization algorithm is used to construct a one-dimensional hybrid encoding string with the initial weights and initial structural safety correction coefficients of each structural function indicator. Engineering constraints are embedded in the one-dimensional hybrid encoding string to complete numerical mapping. Within the same non-dominated frontier layer, individuals are selected in descending order of dynamic weighted crowding. The crossover probability and mutation probability are adaptively adjusted according to the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weights of each structural function indicator. The initial structural safety correction coefficient is a correlation adjustment coefficient representing the overall structural working condition, service risk, and degree of disease correlation. The structural function indicators are weighted and fused using the optimized weights to obtain the construction and maintenance function indicators. The maintenance and management level of the expansion joint to be tested is determined by combining the benefit-cost indicators and the optimized structural safety correction coefficient. The benefit-cost indicators characterize the input-output efficiency of the maintenance plan.

2. The method of maintenance evaluation of expansion joints of in-service concrete bridges according to claim 1, characterized in that, The structural functional parameters are quantified based on engineering standards to obtain corresponding structural functional indicators, including: Based on engineering standards, multiple parameter ranges of different types and sizes are defined, along with the corresponding structural and functional indicators for each parameter range. The corresponding structural function index is determined based on the parameter range in which each of the aforementioned structural function parameters falls.

3. The method of maintenance evaluation of expansion joints of in-service concrete bridges according to claim 1, characterized in that, The improved NSGA-II optimization algorithm is used to construct a one-dimensional hybrid encoding string from the initial weights of each of the structural function indicators and the initial structural safety correction coefficients. Engineering constraints are then embedded into this one-dimensional hybrid encoding string to complete the numerical mapping, including: Based on engineering constraints, the initial weights of each of the structural function indicators are normalized to determine the first numerical range to which each initial weight belongs, and the initial structural safety correction coefficient is mapped to a second numerical range; the second numerical range includes at least a portion of the neighborhood of the value 1.

4. The method of maintenance evaluation of expansion joints of in-service concrete bridges according to claim 1, characterized in that, Within the same non-dominated front layer, individuals are screened in descending order of dynamic weighted crowding, and the crossover and mutation probabilities are adaptively adjusted according to the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weight of each of the aforementioned structural function indicators, including: The initial weights of each of the structural function indicators are calculated using the target space normalized distance summation to determine the individual crowding degree. The individual crowding is dynamically weighted based on the priority of each of the aforementioned structural function indicators to obtain a corresponding dynamically weighted crowding. The target-related parameters and non-target-related parameters are then divided based on the dynamically weighted crowding. The dynamically weighted crowding of the target-related parameters is greater than that of the non-target-related parameters. Within the same non-dominated frontier layer, the neighborhood screening decision interval for target-related parameters is reduced, while the neighborhood screening decision interval for non-target-related parameters is increased. Individual screening is then completed by combining this with a preset threshold.

5. The method of maintenance evaluation of expansion joints of in-service concrete bridges according to claim 4, characterized in that, The target-related parameters include anchoring reliability-related parameters and sealing and waterproofing-related parameters, while the non-target-related parameters include deformation adaptation-related parameters, driving smoothness-related parameters, maintenance convenience-related parameters, and environmental durability-related parameters. The process of narrowing the neighborhood filtering decision interval for target-related parameters and increasing the neighborhood filtering decision interval for non-target-related parameters includes: Narrow the neighborhood selection intervals corresponding to the anchoring reliability parameters and the sealing and waterproofing parameters, and increase the neighborhood selection intervals corresponding to the deformation adaptation parameters, the driving smoothness parameters, the maintenance convenience parameters, and the environmental durability parameters.

6. The method for evaluating the maintenance of expansion joints in in-service concrete bridges as described in claim 1, characterized in that, Within the same non-dominated front layer, individuals are screened in descending order of dynamic weighted crowding, and crossover and mutation probabilities are adaptively adjusted based on the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimization weight of each of the aforementioned structural function indicators. This process also includes: A global strength mutation operator is introduced into the NSGA-II optimization algorithm to increase the mutation probability when the NSGA-II optimization algorithm gets stuck in a local optimum and to increase the crossover probability when the population tends to converge.

7. The method of maintenance evaluation of expansion joints of in-service concrete bridges according to claim 1, wherein The step of using the optimized weights to weight and fuse the structural function indicators to obtain the construction and maintenance function indicators, and combining the benefit-cost indicators and the optimized structural safety correction coefficient to determine the maintenance and management level of the expansion joint to be tested, includes: The evaluation index for the entire time domain is determined by multiplying the construction and maintenance function index, the benefit-cost index, and the optimized structural safety correction coefficient. The maintenance and management level of the expansion joint to be tested is determined based on the range of the full-time evaluation index within the preset specification requirements.

8. An in-service concrete bridge joint maintenance evaluation system, characterized by, include: The module consists of a parameter acquisition module, a weight optimization module, and a level determination module; among them, The parameter acquisition module is configured to acquire the structural functional parameters of the expansion joint to be tested; the structural functional parameters include deformation adaptation related parameters, anchoring reliability related parameters, sealing and waterproofing related parameters, driving smoothness related parameters, maintenance convenience related parameters, and environmental durability related parameters. The weight optimization module is configured to quantify the structural function parameters based on engineering standards to obtain corresponding structural function indicators. It then uses an improved NSGA-II optimization algorithm to construct a one-dimensional hybrid encoding string containing the initial weights and initial structural safety correction coefficients for each structural function indicator. Engineering constraints are embedded into this one-dimensional hybrid encoding string to complete the numerical mapping. Within the same non-dominated frontier layer, individuals are selected in descending order of dynamic weighted crowding. The crossover and mutation probabilities are adaptively adjusted based on the population fitness value distribution to determine the optimized structural safety correction coefficient and the optimized weights for each structural function indicator. The initial structural safety correction coefficient represents the correlation adjustment coefficient of the overall structural condition, service risk, and disease correlation. The level determination module is configured to use the optimized weights to perform weighted fusion on each of the structural function indicators to obtain the construction and maintenance function indicators, and combine the benefit-cost indicators and the optimized structural safety correction coefficient to determine the maintenance and control level of the expansion joint to be tested; the benefit-cost indicators characterize the input-output efficiency of the maintenance plan.

9. An electronic device, comprising: It includes a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the maintenance and evaluation method for expansion joints of in-service concrete bridges as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, comprising: It stores a computer program, wherein the computer program, when executed by a processor, implements the maintenance and evaluation method for expansion joints of in-service concrete bridges as described in any one of claims 1 to 7.

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