A System and Method for Performance Evaluation and Maintenance Optimization of Ultra-High Performance Concrete Bridges Throughout Their Service Life

Abstract

This application discloses a system and method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, specifically relating to the field of bridge maintenance technology. The method includes acquiring bridge evaluation data and a set of bridge images for N bridge regions; selecting M damaged bridge images from the image set; obtaining N bridge performance scores based on the bridge evaluation data and the M damaged bridge images; determining the bridge regions to be maintained first based on the bridge performance scores; and determining a bridge maintenance plan based on the bridge regions to be maintained first. This invention achieves accurate bridge performance evaluation by comprehensively analyzing bridge evaluation data and bridge damage images, and scientifically determines the bridge regions to be maintained first based on the bridge performance scores. This combination of data-driven and image analysis comprehensively covers key indicators such as dynamic stress damage scoring and chloride ion penetration depth, effectively avoiding the shortcomings of traditional single-indicator or subjective evaluation methods.

Description

Technical Field

[0001] This application relates to the field of bridge maintenance technology, specifically to a system and method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life. Background Technology

[0002] With the development of infrastructure construction, ultra-high performance concrete (UHPC) has been widely used in bridge construction due to its superior mechanical properties, durability, and fatigue resistance. UHPC bridges have a long service life, but the lack of scientific performance evaluation has led to problems such as bridge performance degradation, increased maintenance costs, and even safety hazards.

[0003] Existing methods, such as Chinese patent application CN112035919A, disclose a method and system, storage medium, and device for assessing the in-service performance safety of bridges. This method includes identifying multiple risk events affecting the in-service performance of a bridge and assigning weights to each risk event; identifying the risk sub-events included in each risk event and scoring the performance of each risk sub-event; calculating the bridge's in-service performance score based on the weights of the risk events and the performance scores of each risk sub-event; and determining the bridge's safety assessment level based on the performance score. While the above methods can improve the accuracy of bridge in-service performance safety assessment to some extent, research and application of these methods and existing technologies have revealed at least the following shortcomings:

[0004] The assessment only focuses on risk events and sub-events that affect the in-service performance of bridges, without comprehensively considering the impact of environmental erosion (such as chloride ion erosion) and fatigue crack propagation on bridges, which reduces the accuracy of performance assessment.

[0005] To this end, this application provides a system and method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, this application provides a system and method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, this application provides the following technical solution:

[0008] Firstly, this application provides a method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, including:

[0009] Acquire bridge assessment data and bridge image sets for N bridge regions;

[0010] M images of damaged bridges were selected from the bridge image set.

[0011] Based on bridge assessment data and M images of bridge damage, N bridge performance scores are obtained, and the bridge areas to be maintained first are determined based on the bridge performance scores.

[0012] The bridge maintenance plan is determined based on the area of ​​the bridge that is to be maintained first.

[0013] As a further description of the above technical solution, the bridge assessment data includes stress dynamic damage score and chloride ion penetration depth;

[0014] The method for obtaining the stress dynamic damage score includes:

[0015] The calculation was performed by combining the stress amplitude of the bridge area, the bridge's wear life, and the fatigue sensitivity of the concrete material.

[0016] The method for obtaining the chloride ion penetration depth includes:

[0017] Step a1: Obtain a standard sample with a known chloride ion concentration using an X-ray fluorescence analyzer for calibration, establish the correspondence between chloride ion concentration and X-ray fluorescence signal, and obtain a calibrated X-ray fluorescence analyzer;

[0018] Step a2: Point the probe of the calibrated X-ray fluorescence analyzer at the bridge area for continuous measurement, and obtain the chloride ion concentration according to the chloride ion penetration depth formula.

[0019] As a further description of the above technical solution, the bridge image set is obtained by controlling the UAV to collect images of the bridge area according to the optimal time period;

[0020] The method for determining the optimal time period includes:

[0021] Step b1: Obtain W preset time periods and extract bridge dynamic data and weather data for each time period; the bridge dynamic data includes the average daily traffic flow and vehicle body area; the weather data includes the average daily temperature, average humidity, average rainfall, average air particulate matter concentration, and average light intensity, etc.

[0022] Step b2: Input the bridge dynamic data for each time period into the first LSTM model for predicting traffic flow data for analysis to obtain the traffic flow data for each time period; and input the weather data into the second LSTM model for predicting bridge visibility for analysis to obtain the bridge visibility for each time period.

[0023] Step b3: Sort the traffic flow data and bridge visibility for each time period in chronological order to obtain the traffic flow sequence table and the visibility sequence table;

[0024] Step b4: Iterate through the traffic flow sequence table and the visibility sequence table, find the same time period with the minimum traffic flow data and the maximum bridge visibility, and use it as the optimal time period.

[0025] As a further description of the above technical solution, the method for controlling the drone to collect data on the bridge area includes:

[0026] Step c1: Determine the flight path of the UAV in the bridge area, including the starting point coordinates, the ending point coordinates, and the coordinates of X aerial hovering points; X is an integer greater than zero;

[0027] Step c2: Control the drone to fly sequentially from the starting coordinates to the coordinates of the aerial photography hovering point according to the set path;

[0028] Step c3: After the drone reaches the aerial photography hovering point, control it to hover, and at a preset time point T, acquire bridge image A and bridge image B respectively through the first image acquisition device and the third image acquisition device on board, where T is an integer greater than zero;

[0029] Step c4: Perform occlusion analysis on the acquired bridge images A and B:

[0030] If no obstruction is detected, the second image acquisition device on the drone is used to acquire the bridge image L, then the drone is controlled to move to the coordinates of the next aerial hovering point and return to step c3;

[0031] If occlusion is detected, let T = T + q, and re-acquire bridge image A and bridge image B through the first image acquisition device and the third image acquisition device, and return to step c4; q is an integer greater than zero.

[0032] As a further description of the above technical solution, the method for selecting M images of bridge damage includes:

[0033] Step d1: Extract the bridge image L in the bridge image set at time S in chronological order, where S is an integer greater than zero;

[0034] Step d2: Analyze the bridge image L to determine whether there is any damage to the bridge in the bridge image L; if there is no damage, let S=S+1 and return to step d1; if there is damage, then the bridge image L is taken as the bridge damage image.

[0035] Step d3: Repeat steps d1 to d2 until S=Z, then end the loop and finally output M images of bridge damage; Z is the total number of bridge images in the set.

[0036] As a further description of the above technical solution, the method for analyzing the bridge image L includes:

[0037] Step e1: Extract the standard bridge image and divide both the standard bridge image and the bridge image L into several regions according to a unified rule;

[0038] Step e2: Perform preprocessing on the bridge image L, and compare the pixels at the same position in the standard bridge image and the bridge image L one by one to mark the difference areas between the two.

[0039] Step e3: Calculate the difference for each distinguishing region using the mean square error to obtain the Y image distinguishability scores;

[0040] Step e4: Compare the image discrimination of each image with the preset discrimination threshold. If the image discrimination of at least one region is greater than the preset discrimination threshold, it is determined that the corresponding bridge image L is damaged. If the image discrimination of all regions is less than or equal to the preset discrimination threshold, it is determined that the corresponding bridge image L is not damaged.

[0041] As a further description of the above technical solution, the method for obtaining the bridge performance score includes:

[0042] The initial performance score is obtained by calculating the stress dynamic damage score and chloride ion penetration depth.

[0043] The corresponding damage area scores are extracted from M images of bridge damage, and the initial performance scores are corrected to obtain the bridge performance scores.

[0044] As a further description of the above technical solution, the method for determining the bridge area to be maintained first based on the bridge performance score includes:

[0045] Sort the N bridge areas in order of bridge performance score from low to high, with the lower the performance score, the higher the priority.

[0046] Set a performance score threshold. If the performance score of the bridge in the nth bridge area is less than the performance score threshold, then the nth bridge area is marked as the bridge area to be maintained first.

[0047] If all bridge areas are greater than or equal to the performance score threshold, they are sorted from low to high, and the bridge area with the lowest bridge performance score is selected as the first bridge area to be maintained.

[0048] As a further description of the above technical solution, the method for determining the bridge maintenance scheme includes:

[0049] Step f1: Randomly generate P maintenance plans, calculate the objective function of each maintenance plan, and obtain the fitness;

[0050] Step f2: Select superior individuals as parents based on fitness; the fitness selection method is roulette wheel selection.

[0051] Step f3: Cross over the selected parent chromosomes to generate new offspring chromosomes; the crossover method is to randomly select two parent chromosomes, select a crossover point, and exchange the genes before and after the crossover point to generate two offspring chromosomes.

[0052] Step f4: Perform a low-probability mutation on the offspring chromosomes to generate new individuals;

[0053] Step f5: Replace the parent chromosome with the offspring chromosome to form a new population;

[0054] Step f6: Repeat steps f1-f5 until the maximum number of iterations is reached, then output the maintenance plan.

[0055] Secondly, this application provides a system for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life; the system for implementing the above-mentioned method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life includes: a data acquisition module for acquiring bridge evaluation data and bridge image sets for N bridge areas;

[0056] The filtering module is used to filter out M images of bridge damage from the bridge image set;

[0057] The performance analysis module is used to obtain N bridge performance scores based on bridge assessment data and M images of bridge damage, and to determine the bridge areas that need to be maintained first based on the bridge performance scores.

[0058] The determination module is used to determine the bridge maintenance plan based on the bridge area that will be maintained first.

[0059] The technical effects and advantages of this application are as follows:

[0060] 1. This invention achieves accurate assessment of bridge performance by integrating bridge evaluation data and images of bridge damage, and scientifically determines the bridge areas to be maintained first based on the bridge performance score. This combination of data-driven and image analysis comprehensively covers key indicators such as dynamic stress damage scoring and chloride ion penetration depth, effectively avoiding the shortcomings of traditional single-indicator or subjective assessment methods.

[0061] 2. This invention utilizes drones to acquire high-quality bridge images at optimal time periods, further improving the accuracy of the assessment. By prioritizing and filtering based on performance score thresholds, limited maintenance resources are concentrated on the most critical bridge areas, maximizing resource utilization efficiency. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the process for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, as described in Example 1.

[0063] Figure 2 This is a schematic diagram of the method for controlling a drone to collect data on a bridge area in Example 1;

[0064] Figure 3 This is a schematic flowchart of the method for selecting M images of bridge damage in Example 1;

[0065] Figure 4 This is a schematic diagram of the ultra-high performance concrete bridge service life cycle performance evaluation and maintenance optimization system of Example 2. Detailed Implementation

[0066] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0067] Example 1

[0068] like Figure 1 As shown in the figure, this embodiment discloses a method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, including:

[0069] Step 1: Obtain bridge evaluation data and bridge image sets for N bridge areas;

[0070] In the specific implementation process, the bridge area is determined based on the bridge structure; the bridge structure includes the abutment area, mid-span area, bearing area, and edge area, etc. Various monitoring sensors are installed in the bridge area, including strain gauges, X-ray fluorescence analyzers, and multiple image acquisition devices, to monitor the life cycle of the bridge area, acquire bridge assessment data, and establish a system database.

[0071] The bridge assessment data includes stress dynamic damage scores and chloride ion penetration depth.

[0072] In one embodiment, the method for obtaining the stress dynamic damage score includes:

[0073] Obtain the stress amplitude, bridge wear life, and fatigue sensitivity of concrete materials in the bridge area within a preset time period;

[0074] It should be noted that: the stress amplitude is half the difference between the maximum and minimum stress values ​​obtained by the stress gauge; the bridge fatigue life is the maximum number of cycles that the concrete material can withstand under the preset stress amplitude until fatigue failure occurs; the fatigue sensitivity refers to the degree to which the concrete material is sensitive to changes in stress amplitude under dynamic stress conditions.

[0075] The stress dynamic damage score is calculated based on the stress amplitude in the bridge area, the bridge's wear life, and the fatigue sensitivity of the concrete material. The calculation formula is as follows:

[0076] ; ;

[0077] in, Cumulative stress amplitude Stress dynamic damage score; Let r be the number of cycles for the r-th stress amplitude; For bridge fatigue life; Let be a constant of concrete material. denoted as the fatigue sensitivity of concrete material; r is the index of stress amplitude, r = 1, 2, ..., R; R is the total number of cycles of stress amplitude. This represents the total number of stress cycles; indicating the total fatigue load experienced by the bridge.

[0078] It should be noted that fatigue sensitivity is a characteristic parameter of concrete materials, used to reflect their sensitivity to changes in stress amplitude. When fatigue sensitivity is low, the increase in stress amplitude has little impact on the fatigue life of the bridge, that is, the rate of decline in fatigue life is slow. When fatigue sensitivity is high, the increase in stress amplitude will significantly accelerate the decline in the fatigue life of the bridge. The concrete material constant refers to a parameter that reflects the inherent fatigue resistance of concrete materials during the fatigue process, and its value is determined by those skilled in the art through experimental data and mathematical model fitting.

[0079] In one embodiment, the method for obtaining the chloride ion penetration depth includes:

[0080] Step a1: Obtain a standard sample with a known chloride ion concentration using an X-ray fluorescence analyzer for calibration, establish the correspondence between chloride ion concentration and X-ray fluorescence signal, and obtain a calibrated X-ray fluorescence analyzer;

[0081] Specifically, measurements were performed on standard samples of different concentrations, the intensity of chloride ion characteristic spectral lines and the corresponding chloride ion concentration were recorded, and a standard curve was established to convert the measured X-ray fluorescence signal intensity into chloride ion concentration.

[0082] Step a2: Point the probe of the calibrated X-ray fluorescence analyzer at the bridge area for continuous measurement, and obtain the chloride ion concentration according to the chloride ion penetration depth formula; the chloride ion penetration depth formula is:

[0083] ;

[0084] In the formula, This represents the chloride ion concentration at time t at a concrete depth SD. This represents the chloride ion concentration in the bridge area. Let SD be the depth inward from the concrete surface, and t be the preset detection time period. is the chloride ion diffusion coefficient.

[0085] It should be noted that the continuous measurement refers to repeatedly measuring each measurement point in the bridge area multiple times within a preset detection period. The results of each measurement are calculated using the chloride ion penetration depth formula, and the average value is taken as the chloride ion concentration at that measurement point, thereby improving the accuracy and reliability of the data. The reference value for the preset detection period is 10 to 60 seconds.

[0086] It should be noted that the bridge image set was obtained by controlling the UAV to collect images of the bridge area at the optimal time period.

[0087] The optimal time period refers to the best time for the drone to collect images of the bridge area. Specifically, it refers to the time period with the least impact from vehicles and weather factors, thereby ensuring data collection efficiency and completeness.

[0088] In one implementation, the method for determining the optimal time period includes:

[0089] Step b1: Obtain W preset time periods and extract bridge dynamic data and weather data for each time period; the bridge dynamic data includes the average daily traffic flow and vehicle body area; the weather data includes the average daily temperature, average humidity, average rainfall, average air particulate matter concentration, and average light intensity, etc.

[0090] It should be noted that: the average daily traffic flow is obtained by real-time capture of the bridge conditions by cameras installed above the bridge, and by using video image processing technology to identify and count the vehicles in the video; the vehicle area is obtained by measuring with a laser rangefinder; and the weather data comes from weather forecasts released by meteorological platforms. The preset W time periods are determined by those skilled in the art by dividing the collection time into equal parts. For example, with 24 hours as the preset collection time, the collection time is divided into 2-hour intervals, which can be divided into 12 time periods. According to the requirements, one time period is selected as the optimal time period from the 12 time periods.

[0091] Step b2: Input the bridge dynamic data for each time period into the first LSTM model for predicting traffic flow data for analysis to obtain the traffic flow data for each time period; and input the weather data into the second LSTM model for predicting bridge visibility for analysis to obtain the bridge visibility for each time period.

[0092] It should be noted that the generation method of the first LSTM model is as follows: A historical bridge dynamic dataset is collected, including average daily traffic flow, vehicle footprint, and corresponding traffic flow data for K historical time periods (K>0). The dataset is divided into a traffic flow training set and a traffic flow test set. The average daily traffic flow and vehicle footprint from the K historical time periods in the traffic flow training set are used as input to the regression network, and the corresponding traffic flow data are used as output. The regression network is trained to obtain an initial model. The initial model is validated using the traffic flow test set, and the model that meets the preset accuracy requirements is output as the first LSTM model. The regression network is an LSTM (Long Short-Term Memory) model.

[0093] The generation logic of the second LSTM model is similar to that of the first LSTM model, with the main difference being:

[0094] The input is historical weather data, including average temperature, average humidity, average rainfall, average air particulate matter concentration, and average light intensity for K historical time periods. The output is the bridge visibility for each future time period. The bridge visibility training set data can be generated using existing calculation formulas (such as the Koschmieder formula), which will not be repeated here.

[0095] Step b3: Sort the traffic flow data and bridge visibility for each time period in chronological order to obtain the traffic flow sequence table and the visibility sequence table;

[0096] Step b4: Iterate through the traffic flow sequence table and the visibility sequence table, find the same time period with the minimum traffic flow data and the maximum bridge visibility, and use it as the optimal time period.

[0097] The following is an example illustrating the steps described above: Following the above assumptions, if there are 12 time periods, the data obtained is shown in the table below:

[0098] Time period w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 Traffic flow (vehicles / hour) 150 80 220 100 90 180 130 70 160 110 140 120 Bridge visibility (m) 120 150 100 180 200 160 220 250 190 210 230 240

[0099] By iterating through the above table, it can be seen that the time period with the lowest traffic flow and the highest bridge visibility (w8) is the optimal time period.

[0100] like Figure 2 As shown, in one embodiment, the method for controlling a drone to collect data on a bridge area includes:

[0101] Step c1: Determine the flight path of the UAV in the bridge area, including the starting point coordinates, the ending point coordinates, and the coordinates of X aerial hovering points; X is an integer greater than zero;

[0102] It should be noted that the coordinates of the starting point, ending point, and hovering point need to be obtained by those skilled in the art through reasonable calibration, taking into account the actual conditions of the bridge area; no specific limitations are made here.

[0103] Step c2: Control the drone to fly sequentially from the starting coordinates to the coordinates of the aerial photography hovering point according to the set path;

[0104] Step c3: After the drone reaches the aerial photography hovering point, control it to hover, and at a preset time point T, acquire bridge image A and bridge image B respectively through the first image acquisition device and the third image acquisition device on board, where T is an integer greater than zero;

[0105] Step c4: Perform occlusion analysis on the acquired bridge images A and B:

[0106] If no obstruction is detected, the second image acquisition device on the drone is used to acquire the bridge image L, then the drone is controlled to move to the coordinates of the next aerial hovering point and return to step c3;

[0107] If occlusion is detected, let T = T + q, and re-acquire bridge image A and bridge image B through the first image acquisition device and the third image acquisition device, and return to step c4; q is an integer greater than zero.

[0108] It should be noted that the UAV is equipped with a first image acquisition device, a second image acquisition device, and a third image acquisition device. The first and third image acquisition devices are arranged symmetrically. To further explain, the first and third image acquisition devices are used to detect whether there are vehicles on the road in the two traffic directions of the bridge area. If there are no vehicles, the second image acquisition device acquires the bridge image L. If there are vehicles, the second image acquisition device will not be able to acquire the bridge image L in a clear and interference-free state, which will easily affect the subsequent bridge analysis. Therefore, this embodiment uses an algorithm to determine and control the timing of acquiring the bridge image L, thereby helping to avoid the generation of useless image data.

[0109] Step c5: Repeat steps b3 to b4 until the coordinates of the aerial hovering point are the endpoint coordinates, then end the loop and obtain a bridge image set of the bridge area; the bridge image set includes Z bridge images L, where Z is a positive integer; the bridge images L in the bridge image set satisfy the time series.

[0110] Step 2: Select M images of damaged bridges and the corresponding damaged bridges from the bridge image set, where M and N are integers greater than zero;

[0111] like Figure 3 As shown, in one embodiment, the method for filtering out M images of bridge damage includes:

[0112] Step d1: Extract the bridge image L in the bridge image set at time S in chronological order, where S is an integer greater than zero;

[0113] Step d2: Analyze the bridge image L to determine whether there is any damage to the bridge in the bridge image L; if there is no damage, let S=S+1 and return to step d1; if there is damage, then the bridge image L is taken as the bridge damage image.

[0114] Step d3: Repeat steps d1 to d2 until S=Z, then end the loop and finally output M images of bridge damage; Z is the total number of bridge images in the set.

[0115] Specifically, the methods for analyzing bridge image L include:

[0116] Step e1: Extract the standard bridge image and divide both the standard bridge image and the bridge image L into several regions according to a unified rule;

[0117] It should be noted that the standard bridge images are pre-stored in the system database. The stored standard bridge images are undamaged standard bridge images, which are used for comparative analysis with bridge image L.

[0118] Step e2: Perform preprocessing on the bridge image L, and compare the pixels at the same position in the standard bridge image and the bridge image L one by one to mark the difference areas between the two.

[0119] It should be noted that the preprocessing operations include image flipping, image scaling, image cropping, and image denoising, so that it is in the same dimension and format as the standard bridge image.

[0120] Step e3: Calculate the difference for each distinguishing region using the mean square error to obtain the Y image distinguishability scores;

[0121] Step e4: Compare the image discrimination of each image with the preset discrimination threshold. If the image discrimination of at least one region is greater than the preset discrimination threshold, it is determined that the corresponding bridge image L is damaged. If the image discrimination of all regions is less than or equal to the preset discrimination threshold, it is determined that the corresponding bridge image L is not damaged.

[0122] Step 3: Based on the bridge assessment data and M images of bridge damage, obtain N bridge performance scores, and determine the bridge areas that need to be maintained first based on the bridge performance scores.

[0123] In one implementation, the method for obtaining the bridge performance score includes:

[0124] The initial performance score is obtained by calculating the stress dynamic damage score and chloride ion penetration depth; the calculation formula is as follows:

[0125]

[0126] In the formula, The initial performance score, For stress dynamic damage scoring, This represents the depth of chloride ion penetration. This is the offset. and The adjustment factor is set by those skilled in the art based on experience.

[0127] It should be noted that the offset is obtained by continuously fitting the initial performance score range without offset to the target range. The specific setting is determined by those skilled in the art based on actual conditions and will not be described here.

[0128] The corresponding damage area scores are extracted from M images of bridge damage. The initial performance scores are then corrected to obtain the bridge performance score, and the correction formula is as follows:

[0129] ;

[0130] In the formula, The bridge performance score. The initial performance score, Score the area of ​​loss. The adjustment factor is set by those skilled in the art based on experience.

[0131] It is worth noting that the loss area score is obtained by calculating the image distinguishability of each region in the bridge damage image, and by weighted averaging of the image distinguishability of each region.

[0132] In one implementation, the method for determining the bridge area to be maintained first based on bridge performance scores includes:

[0133] Sort the N bridge areas in order of bridge performance score from low to high, with the lower the performance score, the higher the priority.

[0134] Set a performance score threshold. If the performance score of the bridge in the nth bridge area is less than the performance score threshold, then the nth bridge area is marked as the bridge area to be maintained first.

[0135] If all bridge areas are greater than or equal to the performance score threshold, they are sorted from low to high, and the bridge area with the lowest bridge performance score is selected as the first bridge area to be maintained.

[0136] Step 4: Determine the bridge maintenance plan based on the bridge area to be maintained first;

[0137] In one implementation, the method for determining a bridge maintenance plan includes:

[0138] Step f1: Randomly generate P maintenance plans, and calculate the objective function for each plan. The calculation formula is as follows: To gain fitness ;

[0139] In the formula, The objective function value, For total maintenance costs, The bridge performance score. For maintenance duration. As a weighted value for security risks, The weighting value for maintenance duration is adjusted by those skilled in the art based on the actual situation.

[0140] It should be noted that: the total maintenance cost includes material costs, equipment costs, labor costs, and construction-related costs; the bridge performance score reflects the performance level of the bridge during the maintenance period and subsequent use, as explained above, and will not be repeated here; the maintenance duration is the total time required to complete the maintenance task, including preparation, construction, and post-maintenance time.

[0141] It is worth noting that each maintenance regimen is represented by a chromosome, including the following genes: The maintenance materials used, For maintenance process, The maintenance time, maintenance materials, maintenance process, and maintenance time are pre-stored in the system database based on historical maintenance experience.

[0142] Step f2: Select superior individuals as parents based on fitness; the fitness selection method is roulette wheel selection.

[0143] Step f3: Perform crossover on the selected parent chromosomes to generate new offspring chromosomes; the crossover method is to randomly select two parent chromosomes, select a crossover point, and exchange the genes before and after the crossover point to generate two offspring chromosomes.

[0144] Step f4: Perform low-probability mutations on the offspring chromosomes to generate new individuals. The method for low-probability mutations is as follows: after randomly selecting a crossover point, exchange the complete gene blocks before and after the crossover point. The probability of mutation of each chromosome is controlled by the mutation probability Pm, and the value of Pm ranges from 0.01 to 0.1.

[0145] For example, after randomly selecting a crossover point, the complete gene blocks before and after the crossover point are swapped as follows:

[0146] Parent generation [ , , ]and[ , , The offspring after crossover are:

[0147] Offspring 1: [ , , ], offspring 2: [ , , ].

[0148] Step f5: Replace the parent chromosome with the offspring chromosome to form a new population.

[0149] Step f6: Repeat steps f1-f5 until the maximum number of iterations is reached, then output the maintenance plan. The maximum number of iterations is set by those skilled in the art based on experience.

[0150] The optimal maintenance scheme is to minimize the total maintenance cost, reduce safety risks, optimize maintenance implementation time, and use a genetic algorithm to find the most suitable scheme among many maintenance options, thereby ensuring the effectiveness and economy of bridge maintenance.

[0151] Assuming the initial population contains different maintenance schemes, after a series of iterative optimizations, the genetic algorithm will eventually output an optimal solution, as shown in the following example:

[0152] Optimal maintenance plan:

[0153] Maintenance materials: High-durability concrete repair materials;

[0154] Maintenance area: Mid-span area of ​​the bridge;

[0155] Maintenance process: spraying waterproof coating + crack grouting;

[0156] Maintenance implementation time: 1 month.

[0157] By optimizing maintenance strategies using genetic algorithms, the optimal solution can be found among multiple options, ensuring that bridge maintenance work is more efficient, scientific, and economical in practice.

[0158] This embodiment achieves accurate bridge performance assessment by integrating bridge evaluation data and bridge damage images, and scientifically determines the bridge areas to be maintained first based on the bridge performance score. This combination of data-driven and image analysis comprehensively covers key indicators such as bridge stress dynamic damage scoring and chloride ion penetration depth, effectively avoiding the shortcomings of traditional single-indicator or subjective assessment methods.

[0159] This embodiment utilizes drones to acquire high-quality bridge images at optimal time periods, further improving the accuracy of the assessment. By prioritizing and filtering based on performance score thresholds, limited maintenance resources are concentrated on the most critical bridge areas, maximizing resource utilization efficiency.

[0160] Example 2

[0161] like Figure 4 As shown in the figure, this embodiment provides a system for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life. The system includes: a data acquisition module, a screening module, a performance analysis module, and a determination module; each module is connected via wired and / or wireless means to realize data transmission between modules.

[0162] The data acquisition module is used to acquire bridge assessment data and bridge image sets for N bridge areas;

[0163] The filtering module is used to filter out M images of bridge damage from the bridge image set;

[0164] The performance analysis module is used to obtain N bridge performance scores based on bridge assessment data and M images of bridge damage, and to determine the bridge areas that need to be maintained first based on the bridge performance scores.

[0165] The determination module is used to determine the bridge maintenance plan based on the bridge area that will be maintained first.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0167] Finally: The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, characterized in that... include: Acquire bridge assessment data and bridge image sets for N bridge regions; The bridge image set was obtained by controlling the UAV to collect images of the bridge area at the optimal time period; The method for determining the optimal time period includes: Step b1: Obtain W preset time periods and extract bridge dynamic data and weather data for each time period; the bridge dynamic data includes the average daily traffic flow and vehicle body area; the weather data includes the average daily temperature, average humidity, average rainfall, average air particulate matter concentration and average light intensity. Step b2: Input the bridge dynamic data for each time period into the first LSTM model for predicting traffic flow data for analysis to obtain the traffic flow data for each time period; and input the weather data into the second LSTM model for predicting bridge visibility for analysis to obtain the bridge visibility for each time period. Step b3: Sort the traffic flow data and bridge visibility for each time period in chronological order to obtain the traffic flow sequence table and the visibility sequence table; Step b4: Iterate through the traffic flow sequence table and the visibility sequence table, find the same time period with the minimum traffic flow data and the maximum bridge visibility, and use it as the optimal time period; M images of bridge damage were selected from the bridge image set; Based on bridge assessment data and M images of bridge damage, N bridge performance scores are obtained, and the bridge areas to be maintained first are determined based on the bridge performance scores. The bridge maintenance plan is determined based on the area of ​​the bridge that is to be maintained first.

2. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 1, characterized in that, The bridge assessment data includes stress dynamic damage scores and chloride ion penetration depth; The method for obtaining the stress dynamic damage score includes: The calculation was performed by combining the stress amplitude of the bridge area, the bridge's wear life, and the fatigue sensitivity of the concrete material. The method for obtaining the chloride ion penetration depth includes: Step a1: Obtain a standard sample with a known chloride ion concentration using an X-ray fluorescence analyzer for calibration, establish the correspondence between chloride ion concentration and X-ray fluorescence signal, and obtain a calibrated X-ray fluorescence analyzer; Step a2: Point the probe of the calibrated X-ray fluorescence analyzer at the bridge area for continuous measurement, and obtain the chloride ion concentration according to the chloride ion penetration depth formula.

3. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 1, characterized in that, Methods for controlling drones to collect data on bridge areas include: Step c1: Determine the flight path of the UAV in the bridge area, including the starting point coordinates, the ending point coordinates, and the coordinates of X aerial hovering points; X is an integer greater than zero; Step c2: Control the drone to fly sequentially from the starting coordinates to the coordinates of the aerial photography hovering point according to the set path; Step c3: After the drone reaches the aerial photography hovering point, control it to hover, and at a preset time point T, acquire bridge image A and bridge image B respectively through the first image acquisition device and the third image acquisition device on board, where T is an integer greater than zero; Step c4: Perform occlusion analysis on the acquired bridge images A and B: If no obstruction is detected, the second image acquisition device on the drone is used to acquire the bridge image L, then the drone is controlled to move to the coordinates of the next aerial hovering point and return to step c3; If occlusion is detected, let T = T + q, and re-acquire bridge image A and bridge image B through the first image acquisition device and the third image acquisition device, and return to step c4; q is an integer greater than zero.

4. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 3, characterized in that, Methods for selecting M images of bridge damage include: Step d1: Extract the bridge image L in the bridge image set at time S in chronological order, where S is an integer greater than zero; Step d2: Analyze the bridge image L to determine whether there is any damage to the bridge in the bridge image L; if there is no damage, let S=S+1 and return to step d1; if there is damage, then the bridge image L is taken as the bridge damage image. Step d3: Repeat steps d1 to d2 until S=Z, then end the loop and finally output M images of bridge damage; Z is the total number of bridge images in the set.

5. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 4, characterized in that, Methods for analyzing bridge image L include: Step e1: Extract the standard bridge image and divide both the standard bridge image and the bridge image L into several regions according to a unified rule; Step e2: Perform preprocessing on the bridge image L, and compare the pixels at the same position in the standard bridge image and the bridge image L one by one to mark the difference areas between the two. Step e3: Calculate the difference for each distinguishing region using the mean square error to obtain the Y image distinguishability scores; Step e4: Compare the image discrimination of each image with the preset discrimination threshold. If the image discrimination of at least one region is greater than the preset discrimination threshold, it is determined that the corresponding bridge image L is damaged. If the image discrimination of all regions is less than or equal to the preset discrimination threshold, it is determined that the corresponding bridge image L is not damaged.

6. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 5, characterized in that, Methods for obtaining bridge performance scores include: The initial performance score is obtained by calculating the stress dynamic damage score and chloride ion penetration depth. The corresponding damage area scores are extracted from M images of bridge damage, and the initial performance scores are corrected to obtain the bridge performance scores.

7. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 6, characterized in that, Methods for determining the bridge areas requiring initial maintenance based on bridge performance scores include: Sort the N bridge areas in order of bridge performance score from low to high, with the lower the performance score, the higher the priority. Set a performance score threshold. If the performance score of the bridge in the nth bridge area is less than the performance score threshold, then the nth bridge area is marked as the bridge area to be maintained first. If all bridge areas are greater than or equal to the performance score threshold, they are sorted from low to high, and the bridge area with the lowest bridge performance score is selected as the first bridge area to be maintained.

8. The method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life as described in claim 7, characterized in that, Methods for determining bridge maintenance plans include: Step f1: Randomly generate P maintenance plans, calculate the objective function for each maintenance plan, and obtain the fitness; each maintenance plan is represented by a chromosome; Step f2: Select superior individuals as parents based on fitness; the fitness selection method is roulette wheel selection. Step f3: Perform crossover on the selected parent chromosomes to generate new offspring chromosomes; the crossover method is to randomly select two parent chromosomes, select a crossover point, and exchange the genes before and after the crossover point to generate two offspring chromosomes. Step f4: Perform a low-probability mutation on the offspring chromosomes to generate new individuals; Step f5: Replace the parent chromosome with the offspring chromosome to form a new population; Step f6: Repeat steps f1-f5 until the maximum number of iterations is reached, then output the maintenance plan.

9. A system for performance evaluation and maintenance optimization of ultra-high performance concrete bridges throughout their service life, used to implement the method for performance evaluation and maintenance optimization of ultra-high performance concrete bridges according to any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire bridge assessment data and bridge image sets for N bridge areas; The filtering module is used to filter out M images of bridge damage from the bridge image set; The performance analysis module is used to obtain N bridge performance scores based on bridge assessment data and M images of bridge damage, and to determine the bridge areas that need to be maintained first based on the bridge performance scores. The determination module is used to determine the bridge maintenance plan based on the bridge area that will be maintained first.

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