Ultra-high performance concrete bridge service life cycle performance evaluation and maintenance optimization system and method

Abstract

The invention discloses an ultra-high performance concrete bridge service life cycle performance evaluation and maintenance optimization system and method, and particularly relates to the technical field of bridge maintenance, and the method comprises the steps: obtaining bridge evaluation data and a bridge image set of N bridge regions; screening out M bridge damage images from the bridge image set; obtaining N bridge performance scores based on the bridge evaluation data and the M bridge damage images, and determining a first maintenance bridge area based on the bridge performance scores; according to the method, the bridge performance is accurately evaluated by integrating the bridge evaluation data and the bridge damage image, and the first-maintained bridge area is scientifically determined by taking the bridge performance score as a core basis. According to the combination mode based on data driving and image analysis, key indexes such as bridge stress dynamic damage scores and chloride ion invasion depth can be comprehensively covered, and the defects of a traditional single index or subjective evaluation method are effectively overcome.

Description

Technical Field

[0001] The present application relates to the field of bridge maintenance technology, and specifically to a system and method for evaluating the performance of ultra-high performance concrete bridges over their entire service life cycle and optimizing their maintenance. Background Art

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

[0003] Among the existing methods, for example, the Chinese patent application with publication number CN112035919A discloses a method and system, storage medium and equipment for safety assessment of in-service performance of bridges, which includes determining multiple risk events that affect the in-service performance of bridges, and assigning weights to each risk event; determining the risk sub-events included in each risk event, and scoring the performance of each risk sub-event; calculating the performance score of the in-service performance of the bridge according to the weight of the risk event and the performance score of each risk sub-event; and determining the safety assessment level of the bridge according to the performance score of the in-service performance of the bridge. Although the above method can improve the accuracy of the safety assessment of the in-service performance of bridges to a certain extent, it is found through research and application of the above method and the existing technology that the above method and the existing technology have at least the following defects:

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

[0005] To this end, the present application provides a system and method for evaluating the performance of ultra-high performance concrete bridges over their entire service life cycle and optimizing their maintenance. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present application provides a system and method for evaluating the performance of ultra-high performance concrete bridges throughout their service life cycle and optimizing maintenance to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above purpose, this application provides the following technical solutions:

[0008] In the first aspect, the present application provides a method for evaluating the performance of ultra-high performance concrete bridges during their entire service life cycle and optimizing their maintenance, including:

[0009] Obtain bridge assessment data and bridge image sets for N bridge areas;

[0010] Selecting M damaged bridge images from the bridge image set;

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

[0012] Determine the bridge maintenance plan based on the bridge areas that need 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] It is calculated by combining the stress amplitude in the bridge area, the bridge wear life and the fatigue sensitivity of the concrete material;

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

[0017] Step a1: obtaining a standard sample with a known chloride ion concentration through an X-ray fluorescence analyzer for calibration, establishing a corresponding relationship between the chloride ion concentration and the X-ray fluorescence signal, and obtaining a calibrated X-ray fluorescence analyzer;

[0018] Step a2: Aim 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 a drone to collect the bridge area according to an optimal time period;

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

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

[0022] Step b2: inputting the bridge dynamic data of each time period into the first LSTM model preset for predicting traffic flow data for analysis, thereby obtaining the traffic flow data of each time period; and inputting the weather data into the second LSTM model for predicting bridge visibility for analysis, thereby obtaining the bridge visibility of each time period;

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

[0024] Step b4: Traverse the traffic flow sequence table and the visibility sequence table to 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 of 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 coordinates of the starting point, the end point, and the coordinates of X aerial photography hovering points; X is an integer greater than zero;

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

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

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

[0030] If no occlusion is detected, the bridge image L is captured by the second image acquisition device carried by the drone, and then the drone is controlled to move to the coordinates of the next aerial photography hovering point, and the process returns to step c3;

[0031] If occlusion is detected, T=T+q is set, and the bridge image A and the bridge image B are acquired again through the first image acquisition device and the third image acquisition device, and the process returns to step c4; q is an integer greater than zero.

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

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

[0034] Step d2: Analyze the bridge image L to determine whether the bridge in the bridge image L is damaged; if not, set S=S+1 and return to step d1; if damaged, use the bridge image L as a bridge damage image;

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

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

[0037] Step e1: extracting the standard bridge image, and dividing the standard bridge image and the bridge image L into a number of regions according to a unified rule;

[0038] Step e2: preprocessing the bridge image L, and comparing the pixels at the same position between the standard bridge image and the bridge image L one by one, marking the difference areas between the two;

[0039] Step e3: Calculate the difference of each distinguished area using mean square error to obtain Y image distinctions;

[0040] Step e4: Compare the image distinction of each image with the preset distinction threshold. If there is at least one area whose image distinction is greater than the preset distinction threshold, it is determined that the corresponding bridge image L is damaged; if the image distinctions of all areas are less than or equal to the preset distinction threshold, it is determined that the corresponding bridge image L is not damaged.

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

[0042] The stress dynamic damage score and chloride ion penetration depth were calculated to obtain the initial performance score;

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

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

[0045] Sort the N bridge areas by bridge performance scores from low to high, with the lower the performance score, the higher the priority;

[0046] A performance score threshold is set, and if the bridge performance score of the nth bridge area is less than the performance score threshold, the nth bridge area is marked as the first bridge area to be maintained;

[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 plan 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 excellent individuals as parents according to fitness; the selection method is roulette selection;

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

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

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

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

[0055] In a second aspect, the present application provides a system for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle; a system for implementing the above-mentioned method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle, comprising: a data acquisition module for acquiring bridge evaluation data and a bridge image set of N bridge areas;

[0056] A screening module, used for screening out M damaged bridge images from the bridge image set;

[0057] A performance analysis module is used to obtain N bridge performance scores based on the bridge assessment data and M bridge damage images, and determine the bridge area to be maintained first based on the bridge performance scores;

[0058] The determination module is used to determine the bridge maintenance plan according to the bridge area to be maintained first.

[0059] Technical effects and advantages of this application:

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

[0061] 2. The present invention uses drones to collect high-quality bridge images at optimal time periods, further improving the accuracy of the assessment. Through priority sorting and performance score threshold screening, limited maintenance resources are concentrated on the bridge areas that need them most, maximizing resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of the process flow of the ultra-high performance concrete bridge service life cycle performance evaluation and maintenance optimization method of Example 1;

[0063] Figure 2 This is a flow chart of a method for controlling a drone to collect data on a bridge area according to Example 1;

[0064] Figure 3 This is a schematic flow chart of a method for screening out M images of damaged bridges in Example 1;

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

[0066] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0067] Example 1

[0068] like Figure 1 As shown, this embodiment discloses a method for evaluating the performance of an ultra-high performance concrete bridge during its entire service life cycle and optimizing its maintenance, including:

[0069] Step 1: Obtain bridge assessment data and bridge image sets of N bridge areas;

[0070] In the specific implementation process, the bridge area is obtained according to the division of the bridge structure; the bridge structure includes the abutment area, the mid-span area, the support area and the edge area, etc. Various monitoring sensors are set in the bridge area, including strain gauges, X-ray fluorescence analyzers and multiple image acquisition devices, which are used to monitor the life cycle of the bridge area, obtain bridge evaluation data and establish a system database.

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

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

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

[0074] It should be noted that: the stress amplitude is half of the difference between the maximum stress value and the minimum stress value obtained by the stress sheet; the bridge wear life is the maximum number of cycles that the concrete material can withstand at the preset stress amplitude until fatigue failure occurs; the fatigue sensitivity refers to the sensitivity of the concrete material to the change of stress amplitude under dynamic stress conditions;

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

[0076] R ω =CL×ω -pl ;

[0077] Among them, YL ω is the stress dynamic damage score under the cumulative stress amplitude ω; XH r is the number of cycles of the rth stress amplitude; R ω is the fatigue life of the bridge; CL is the concrete material constant, pl is the fatigue sensitivity of the concrete material; r is the index of the stress amplitude, r = 1, 2, ..., R; R is the total number of cycles of the stress amplitude; is the total number of stress cycles; it indicates the total amount of fatigue loading the bridge is subjected to;

[0078] It should be noted that fatigue sensitivity is a characteristic parameter of concrete material, which is used to reflect its sensitivity to changes in stress amplitude. When the fatigue sensitivity is small, the increase in stress amplitude has little effect on the fatigue life of the bridge, that is, the rate of decline in fatigue life is slow; when the fatigue sensitivity is large, 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 the concrete material during the fatigue process, and its value is determined by technical personnel in this field through experimental data and mathematical model fitting.

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

[0080] Step a1: obtaining a standard sample with a known chloride ion concentration through an X-ray fluorescence analyzer for calibration, establishing a corresponding relationship between the chloride ion concentration and the X-ray fluorescence signal, and obtaining a calibrated X-ray fluorescence analyzer;

[0081] Specifically, measurements are performed on standard samples of different concentrations, the intensity of the chloride ion characteristic spectral line and the corresponding chloride ion concentration are recorded, and a standard curve is established for converting the measured X-ray fluorescence signal intensity into chloride ion concentration.

[0082] Step a2: Aim 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] Where, Lq(SD, t) represents the chloride ion concentration at the depth SD of the concrete at time t; C s is the chloride ion concentration in the bridge area, erf is the error function, SD is the depth from the concrete surface, t is the preset detection period, and KS is the chloride ion diffusion coefficient.

[0085] It should be noted that the continuous measurement refers to repeated measurement of each measuring point in the bridge area within a preset detection period, calculating each measurement result using the chloride ion penetration depth formula, and taking the average value as the chloride ion concentration of the measuring point to improve the accuracy and reliability of the data. The reference value of the preset detection period is 10 to 60 seconds.

[0086] It should be noted that the bridge image set is obtained by controlling the drone to collect the bridge area according to the optimal time period.

[0087] Among them, the optimal time period refers to the optimal time for the drone to collect the bridge image set of the bridge area. Specifically, it refers to the time period that is least affected by moving vehicles and weather factors, so as to ensure the efficiency and completeness of data collection.

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

[0089] Step b1: Obtain preset W time periods, and extract bridge dynamic data and weather data for each time period; the bridge dynamic data includes the average daily traffic volume and vehicle type footprint; the weather data includes the average temperature, humidity, rainfall, air particulate matter concentration, and light intensity of the day;

[0090] It should be noted that: the average daily traffic volume is obtained by using a camera installed above the bridge to capture the bridge situation in real time, and by using video image processing technology to identify and count the vehicles in the video; the area occupied by the vehicle model is measured and obtained by a laser rangefinder, and the weather data is derived from the weather forecast released by the meteorological platform; the preset W time periods are determined by technicians in this field by dividing the collection time into equal parts; illustratively; taking 24 hours as the preset collection time, the collection time is divided into intervals of 2 hours, which can be divided into 12 time periods, and according to demand, one time period is determined as the optimal time period among the 12 time periods.

[0091] Step b2: inputting the bridge dynamic data of each time period into the first LSTM model preset for predicting traffic flow data for analysis, thereby obtaining the traffic flow data of each time period; and inputting the weather data into the second LSTM model for predicting bridge visibility for analysis, thereby obtaining the bridge visibility of each time period;

[0092] It should be noted that: the generation method of the first LSTM model is: collect historical bridge dynamic data sets, including the average daily traffic volume, vehicle type footprint and corresponding traffic flow data (K>0) in K historical periods, divide the data sets into a traffic training set and a traffic test set, use the average daily traffic volume and vehicle type footprint of K historical periods in the traffic training set as the input of the regression network, and the corresponding traffic flow data as the output, train the regression network, obtain the initial model, verify the initial model with the traffic test set, and output the model that meets the preset accuracy requirements as the first LSTM model. The regression network is an LSTM model (Long Short-Term Memory Network).

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

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

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

[0096] Step b4: Traverse the traffic flow sequence table and the visibility sequence table to 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 exemplary explanation based on the above steps: Based on the above assumptions, if there are 12 time periods, the data obtained is as follows:

[0098]

[0099] By traversing the above table, it can be seen that the time period of w8 with the minimum traffic flow data and the maximum bridge visibility is the optimal time period.

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

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

[0102] It should be noted that the coordinates of the starting point, the end point and the hovering point need to be obtained by technical personnel in this field through reasonable calibration in combination with the actual situation of the bridge area, and are not specifically limited here.

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

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

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

[0106] If no occlusion is detected, the bridge image L is captured by the second image acquisition device carried by the drone, and then the drone is controlled to move to the coordinates of the next aerial photography hovering point, and the process returns to step c3;

[0107] If occlusion is detected, T=T+q is set, and the bridge image A and the bridge image B are acquired again through the first image acquisition device and the third image acquisition device, and the process returns to step c4; q is an integer greater than zero.

[0108] It should be noted that: the drone is equipped with a first image acquisition device, a second image acquisition device and a third image acquisition device, and the first image acquisition device and the third image acquisition device are axially symmetrically arranged. Further explanation is that the first image acquisition device and the third image acquisition device are used to detect whether there are vehicles on the roads in the two traffic directions of the bridge area. If there are no vehicles oncoming, the bridge image L is acquired by the second image acquisition device. If there are vehicles oncoming, the second image acquisition device will not be able to acquire the bridge image L in a clear and interference-free state, which may easily affect the subsequent bridge analysis. Therefore, this embodiment uses an algorithm to judge and control the acquisition timing of the bridge image L, which is conducive to avoiding the occurrence of useless image data.

[0109] Step c5: Repeat steps b3 to b4 until the coordinates of the aerial photography hovering point are the end coordinates, ending the loop and obtaining a bridge image set of the bridge area; the bridge image set includes Z bridge images L, where Z is an integer greater than zero; the bridge images L in the bridge image set satisfy the time series.

[0110] Step 2: Filter out M damaged bridge images and 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 screening out M bridge damage images includes:

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

[0113] Step d2: Analyze the bridge image L to determine whether the bridge in the bridge image L is damaged; if not, set S=S+1 and return to step d1; if damaged, use the bridge image L as a bridge damage image;

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

[0115] Specifically, the method for analyzing the bridge image L includes:

[0116] Step e1: extracting the standard bridge image, and dividing the standard bridge image and the bridge image L into a number of regions according to a unified rule;

[0117] It should be noted that: the standard bridge image is pre-stored in the system database. The stored standard bridge image is a standard bridge image without any damage, which is used for comparison and analysis with the bridge image L.

[0118] Step e2: preprocessing the bridge image L, and comparing the pixels at the same position between the standard bridge image and the bridge image L one by one, marking 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 the image is in the same dimension and format as the standard bridge image.

[0120] Step e3: Calculate the difference of each distinguished area using mean square error to obtain Y image distinctions;

[0121] Step e4: Compare the image distinction of each image with the preset distinction threshold. If there is at least one area whose image distinction is greater than the preset distinction threshold, it is determined that the corresponding bridge image L is damaged; if the image distinctions of all areas are less than or equal to the preset distinction threshold, it is determined that the corresponding bridge image L is not damaged.

[0122] Step 3: Based on the bridge assessment data and M bridge damage images, N bridge performance scores are obtained, and the bridge area to be maintained first is determined based on the bridge performance scores;

[0123] In one embodiment, a method for obtaining a bridge performance score includes:

[0124] The stress dynamic damage score and chloride ion penetration depth are calculated to obtain the initial performance score; the calculation formula is:

[0125]

[0126] In the formula, XF 0 is the initial performance score, YL ω is the stress dynamic damage score, Lq(SD, t) is the chloride ion penetration depth, py is the offset, P 1 and P 1 is an adjustment factor, which is set by those skilled in the art based on experience.

[0127] It should be noted that the offset is obtained by obtaining the value range of the initial performance score without the offset and continuously fitting it with the target value range. It is specifically set by those skilled in the art according to actual conditions and will not be described here.

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

[0129] XF Z =XF 0 -φ×SHP;

[0130] In the formula, XF z is the bridge performance score, XF 0 is the initial performance score, SHP is the loss area score, and φ is the adjustment factor, which is set by those skilled in the art based on experience.

[0131] It is worth noting that the loss area score is calculated based on the image distinction of each area in the bridge damage image, and is obtained by weighted averaging the image distinction of each area.

[0132] In one embodiment, a method for determining a bridge area to be maintained first based on a bridge performance score includes:

[0133] Sort the N bridge areas by bridge performance scores from low to high, with the lower the performance score, the higher the priority;

[0134] A performance score threshold is set, and if the bridge performance score of the nth bridge area is less than the performance score threshold, the nth bridge area is marked as the first bridge area to be maintained;

[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 embodiment, the method for determining a bridge maintenance plan includes:

[0138] Step f1: Randomly generate P maintenance plans and calculate the objective function of each maintenance plan. The calculation formula is: Gaining fitness

[0139] In the formula, SYD is the objective function value, CB is the total maintenance cost, XF Z is the bridge performance score, and YS is the maintenance time. 1 is the weight value of security risk, θ 2 The weight value is the maintenance time, and the weight value is adjusted by technicians in this field according to actual conditions.

[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 of the bridge during the maintenance period and its subsequent use period, which has been explained in the above content and will not be repeated here. The maintenance duration is the total time required to complete the maintenance task, including preparation, construction and post-processing time.

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

[0142] Step f2: Select excellent individuals as parents according to fitness; the selection method is roulette selection;

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

[0144] Step f4: Perform a small probability mutation on the offspring chromosomes to generate new individuals. The method of small probability mutation is: randomly select a crossover point, exchange the complete gene blocks before and after the crossover point, and control the probability of mutation of each chromosome through the mutation probability Pm, and the value range of Pm is 0.01-0.1.

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

[0146] Parent[H 1 ,G 1 ,Q 1 ] and [H 2 ,G 2 ,Q 2 ] The offspring after crossover are:

[0147] Offspring 1: [H 1 ,G 2 ,Q 2 ], offspring 2: [H 2 ,G 1 ,Q 1 ].

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

[0149] Step f6: Repeat steps f1-f5 until the maximum number of iterations is reached and 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 plan is: minimizing the total maintenance cost, reducing safety risks, optimizing the maintenance implementation time, and using genetic algorithms to find the most suitable plan among many maintenance plans to ensure the effectiveness and economy of bridge maintenance.

[0151] Assuming that the initial population contains different maintenance plans, after a series of iterative optimizations, the genetic algorithm will eventually output an optimal plan, as shown below:

[0152] Optimal maintenance plan:

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

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

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

[0156] Maintenance implementation time: 1 month.

[0157] By optimizing maintenance strategies through genetic algorithms, the best solution can be found from multiple options, ensuring that bridge maintenance work is more efficient, scientific and economical in actual implementation.

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

[0159] This embodiment uses drones to collect high-quality bridge images at optimal times, further improving the accuracy of the assessment. Through priority sorting and performance score threshold screening, limited maintenance resources are concentrated on the bridge areas that need them most, maximizing resource utilization efficiency.

[0160] Example 2

[0161] like Figure 4 As shown, this embodiment provides a system for evaluating and optimizing the performance of ultra-high performance concrete bridges throughout their service life cycle, the system comprising: a data acquisition module, a screening module, a performance analysis module and a determination module; each module is connected by wire and / or wireless means to achieve data transmission between modules;

[0162] A data acquisition module, used to acquire bridge assessment data and bridge image sets of N bridge areas;

[0163] A screening module, used for screening out M damaged bridge images from the bridge image set;

[0164] A performance analysis module is used to obtain N bridge performance scores based on the bridge assessment data and M bridge damage images, and determine the bridge area to be maintained first based on the bridge performance scores;

[0165] The determination module is used to determine the bridge maintenance plan according to the bridge area to be maintained first.

[0166] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

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

Claims

1. A method for evaluating the performance of ultra-high performance concrete bridges during their entire service life cycle and optimizing their maintenance, characterized in that: include: Obtain bridge assessment data and bridge image sets for N bridge areas; Selecting M damaged bridge images from the bridge image set; Based on the bridge assessment data and M bridge damage images, N bridge performance scores are obtained, and the bridge area to be maintained first is determined based on the bridge performance scores; Determine the bridge maintenance plan based on the bridge areas that need to be maintained first.

2. The method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 1 is characterized in that: The bridge assessment data include stress dynamic damage score and chloride ion intrusion depth; The method for obtaining the stress dynamic damage score includes: It is calculated by combining the stress amplitude in the bridge area, the bridge wear life and the fatigue sensitivity of the concrete material; The method for obtaining the chloride ion penetration depth comprises: Step a1: obtaining a standard sample with a known chloride ion concentration through an X-ray fluorescence analyzer for calibration, establishing a corresponding relationship between the chloride ion concentration and the X-ray fluorescence signal, and obtaining a calibrated X-ray fluorescence analyzer; Step a2: Aim 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 evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 2 is characterized in that: The bridge image set is obtained by controlling the drone to collect the bridge area according to the optimal time period; The method for determining the optimal time period includes: Step b1: Obtain preset W time periods, and extract bridge dynamic data and weather data for each time period; the bridge dynamic data includes the average daily traffic volume and vehicle type area; the weather data includes the average temperature, humidity, rainfall, air particulate matter concentration and light intensity of the day; Step b2: inputting the bridge dynamic data of each time period into the first LSTM model preset for predicting traffic flow data for analysis, thereby obtaining the traffic flow data of each time period; and inputting the weather data into the second LSTM model for predicting bridge visibility for analysis, thereby obtaining the bridge visibility of each time period; Step b3: sorting the traffic flow data and bridge visibility of each time period in chronological order to obtain a traffic flow sequence table and a visibility sequence table; Step b4: Traverse the traffic flow sequence table and the visibility sequence table to find the same time period with the minimum traffic flow data and the maximum bridge visibility, and use it as the optimal time period.

4. The method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 3 is characterized in that: Methods for controlling drones to collect data in bridge areas include: Step c1: Determine the flight path of the UAV in the bridge area, including the coordinates of the starting point, the end point, and the coordinates of X aerial photography hovering points; X is an integer greater than zero; Step c2: Control the drone to fly from the starting point coordinates to the coordinate position of the aerial photography hovering point according to the set path; Step c3: After the drone reaches the aerial photography hovering point, it is controlled to hover, and at a preset time point T, the first image acquisition device and the third image acquisition device carried thereon respectively acquire the bridge image A and the bridge image B, where T is an integer greater than zero; Step c4: Perform occlusion analysis on the acquired bridge image A and bridge image B: If no occlusion is detected, the bridge image L is captured by the second image acquisition device carried by the drone, and then the drone is controlled to move to the coordinates of the next aerial photography hovering point, and the process returns to step c3; If occlusion is detected, T=T+q is set, and the bridge image A and the bridge image B are acquired again through the first image acquisition device and the third image acquisition device, and the process returns to step c4; q is an integer greater than zero.

5. The method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 4 is characterized in that: The method of screening out M damaged bridge images includes: Step d1: extracting the bridge image L in the Sth time sequence from the bridge image set in chronological order, where S is an integer greater than zero; Step d2: Analyze the bridge image L to determine whether the bridge in the bridge image L is damaged; if not, set S=S+1 and return to step d1; if damaged, use the bridge image L as a bridge damage image; Step d3: Repeat steps d1 to d2 until S=Z, then end the loop and finally output M images of damaged bridges; Z is the total number of bridge images.

6. The method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 5 is characterized in that: The method for analyzing the bridge image L includes: Step e1: extracting the standard bridge image, and dividing the standard bridge image and the bridge image L into a number of regions according to a unified rule; Step e2: preprocessing the bridge image L, and comparing the pixels at the same position between the standard bridge image and the bridge image L one by one, marking the difference areas between the two; Step e3: Calculate the difference of each distinguished area using mean square error to obtain Y image distinctions; Step e4: Compare the image distinction of each image with the preset distinction threshold. If there is at least one area whose image distinction is greater than the preset distinction threshold, it is determined that the corresponding bridge image L is damaged; if the image distinctions of all areas are less than or equal to the preset distinction threshold, it is determined that the corresponding bridge image L is not damaged.

7. The method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 6 is characterized in that: The bridge performance score is obtained by: The stress dynamic damage score and chloride ion penetration depth were calculated to obtain the initial performance score; The corresponding damaged area scores are extracted from the M damaged bridge images, and the initial performance scores are corrected to obtain the bridge performance scores.

8. The method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle according to claim 7 is characterized in that: Methods for determining bridge areas for priority maintenance based on bridge performance scores include: Sort the N bridge areas by bridge performance scores from low to high, with the lower the performance score, the higher the priority; A performance score threshold is set, and if the bridge performance score of the nth bridge area is less than the performance score threshold, the nth bridge area is marked as the first bridge area to be maintained; 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.

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

10. A system for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle, used to implement the method for evaluating and optimizing the performance of ultra-high performance concrete bridges during their entire service life cycle as claimed in any one of claims 1 to 9, characterized in that: include: A data acquisition module, used to acquire bridge assessment data and bridge image sets of N bridge areas; A screening module, used for screening out M damaged bridge images from the bridge image set; A performance analysis module is used to obtain N bridge performance scores based on the bridge assessment data and M bridge damage images, and determine the bridge area to be maintained first based on the bridge performance scores; The determination module is used to determine the bridge maintenance plan according to the bridge area to be maintained first.

Images