Method and device for quickly calculating crack life of asphalt pavement layer of steel bridge deck

Abstract

The invention discloses a method and device for rapidly calculating the crack life of a steel bridge deck asphalt pavement layer in the technical field of bridge engineering, and the method comprises the steps: determining the characteristic data of a crack, the characteristic data comprises the crack length, the crack width and environmental parameters; dynamically collecting the feature data and constructing a feature data set; based on the feature data set, a crack evolution time-varying model is constructed by adopting a multiple regression analysis method; based on the crack evolution time-varying model, calculating the time for the crack to reach a safety threshold to obtain the crack life; in addition, the method further comprises the steps that the asphalt pavement layer of the steel bridge deck is divided into a plurality of areas, and vulnerable areas are recognized; on the basis of a crack evolution time-varying model, calculating the time for the crack of each vulnerable area to reach a safety threshold, and obtaining the crack life of each vulnerable area; according to the method, the evolution trend of the crack can be accurately predicted, the easy-to-damage area of the bridge can be quickly identified, and the residual life of the crack can be calculated, so that the efficiency and safety of bridge maintenance can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of bridge engineering, and in particular to a method and a device for quickly calculating the crack life of an asphalt pavement layer on a steel bridge deck. Background Art

[0002] Steel bridge deck pavement is widely used due to its excellent load-bearing capacity, but it is prone to cracks under long-term dynamic loads and environmental factors. Cracks are more likely to appear in concentrated areas, especially in areas where heavy vehicles frequently pass. If not repaired in time, cracks will cause further deterioration of the pavement and even threaten the overall structural safety of the bridge. Therefore, identifying vulnerable areas and monitoring cracks are the key to bridge maintenance.

[0003] Existing technologies mainly rely on manual inspection and surface detection equipment, which cannot accurately predict the evolution trend and remaining life of cracks, and it is difficult to effectively identify vulnerable areas, resulting in delayed maintenance strategies. Traditional crack repair is usually carried out after the cracks have expanded, which increases maintenance costs and shortens the service life of bridges. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method and device for quickly calculating the life of cracks in an asphalt pavement layer on a steel bridge deck, which can accurately predict the evolution trend of cracks, quickly identify vulnerable areas of the bridge and calculate the remaining life of cracks, thereby helping to improve the efficiency and safety of bridge maintenance.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides a method for quickly calculating the crack life of an asphalt pavement layer on a steel bridge deck, comprising: Determining characteristic data of the crack, wherein the characteristic data includes crack length, crack width and environmental parameters; Dynamically collecting the feature data and constructing a feature data set; Based on the characteristic data set, a time-varying model of crack evolution is constructed by using a multivariate regression analysis method; Based on the time-varying model of crack evolution, the time when the crack reaches the safety threshold is calculated to obtain the crack life.

[0006] Optionally, the environmental parameters include ambient temperature and vehicle axle number.

[0007] Optionally, the method of constructing a time-varying model of crack evolution using a multivariate regression analysis method includes: Using crack width as the benchmark for rapid calculation of crack life, a time-varying model of crack width evolution is constructed. : ; In the formula, Separately for the moment crack width, ambient temperature, and vehicle axle number; is the initial crack width, is the regression coefficient; The characteristic data set is substituted into the time-varying model of crack width evolution, and the regression coefficient is fitted to complete the model construction.

[0008] Optionally, the method of constructing a time-varying model of crack evolution using a multivariate regression analysis method further includes: The constructed time-varying model of crack width evolution is cross-validated to obtain the model accuracy. If the model accuracy does not meet the preset standard, the regression coefficient is refitted until the model accuracy meets the preset standard.

[0009] Optionally, calculating the time when the crack reaches a safety threshold to obtain the crack life includes: Get the current crack width , and calculate its difference with the safety threshold The difference : ; According to the time-varying model of crack width evolution, the growth rate of crack width is predicted from the current moment. : ; According to the difference and growth rate , calculate the time when the crack reaches the safety threshold : ; Time As the crack lifetime from the current moment.

[0010] Optionally, the fast calculation method further includes: The asphalt pavement of the steel bridge deck is divided into multiple areas, and multiple sub-areas that are most susceptible to load are selected in each area to calculate the crack condition index of each area. : ; ; In the formula, is the regional fracture evolution risk parameter, is the total area of ​​all sub-regions in the region, For all sub-areas in the region The crack length and crack width of each crack; Sort the areas from small to large according to the crack condition index, and take the areas with a preset proportion as vulnerable areas; Based on the time-varying crack evolution model, the time when the cracks in each vulnerable area reach the safety threshold is calculated to obtain the crack life of each vulnerable area.

[0011] Optionally, dividing the asphalt pavement layer of the steel bridge deck into a plurality of areas includes dividing the asphalt pavement layer of the steel bridge deck using a single lane width and a preset length as the width and length of the area.

[0012] In a second aspect, the present invention provides a device for quickly calculating the crack life of an asphalt pavement layer on a steel bridge deck, the device comprising: A data determination module is configured to determine characteristic data of a crack, wherein the characteristic data includes crack length, crack width, and environmental parameters; A data collection module is configured to dynamically collect the feature data and construct a feature data set; A model building module is configured to build a time-varying model of fracture evolution using a multivariate regression analysis method based on the characteristic data set; The life calculation module is configured to calculate the time when the crack reaches the safety threshold based on the crack evolution time-varying model to obtain the crack life.

[0013] In a third aspect, the present invention provides an electronic device, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps according to the above method.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned method when executed by a processor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method and device for quickly calculating the crack life of an asphalt pavement layer on a steel bridge deck. The method collects crack feature data and adopts a multivariate regression analysis method to construct a time-varying model of crack evolution, thereby accurately predicting the time-varying crack evolution; accurately calculates the crack life based on the predicted structure; thereby helping to improve the efficiency and safety of bridge maintenance; in order to further improve the efficiency of crack life calculation, by identifying vulnerable areas and quickly calculating the crack life of the vulnerable areas, each area can be sorted according to the remaining time, and the maintenance and repair of cracks in vulnerable areas of the asphalt bridge deck can be reasonably arranged. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for quickly calculating crack life of an asphalt pavement layer on a steel bridge deck provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a time-varying curve of the evolution of crack width generated by the model provided in an embodiment of the present invention; Figure 3 It is a schematic diagram of the areas and sub-areas in which the asphalt pavement layer of a steel bridge deck is divided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0018] Embodiment 1: like Figure 1 As shown, an embodiment of the present invention provides a method for quickly calculating the crack life of an asphalt pavement layer on a steel bridge deck, comprising the following steps: Step S1, determining characteristic data of a crack, wherein the characteristic data includes crack length, crack width and environmental parameters.

[0019] Specifically in this embodiment, the environmental parameters include the ambient temperature and the vehicle axle number. In other optional embodiments, other characteristic data may also be selected as needed.

[0020] Step S2: dynamically collect feature data and construct a feature data set.

[0021] The crack length and crack width can be directly collected manually through a distance measuring sensor or a tape measure, and the unit is mm; the ambient temperature can be collected through a temperature sensor, and the unit is ℃.

[0022] The calculation formula for vehicle axle times is as follows: ; ; In the formula, is the average number of vehicles passing through the steel bridge per day, is the traffic growth coefficient, is the annual traffic growth rate, For the preset period, is the lane allocation coefficient, which is generally 0.7-0.9. For the The percentage of traffic volume of vehicles of this type, For the The equivalent conversion factor for this type of vehicle converts various axle loads into a standard axle load (usually 8.2 tons).

[0023] The crack length, crack width, ambient temperature and vehicle axle frequency are aligned in time to obtain characteristic data , data should be collected multiple times over a period of time. For bridge cracks, data should be collected at least once a month for one year; all collected data should be regularly stored in a structured database and kept updated for subsequent analysis and prediction.

[0024] Step S3: Based on the characteristic data set, a multivariate regression analysis method is used to construct a time-varying model of crack evolution.

[0025] The formation and expansion of cracks in asphalt bridge decks are affected by many factors such as the properties of asphalt materials, the design of asphalt mixtures, repeated loads, temperature changes, etc. The calculation methods provided in this embodiment are all aimed at bridges that have been put into use, and only consider two key factors: ambient temperature and vehicle axle frequency.

[0026] Specifically in this embodiment, the time-varying model of crack evolution is constructed by using a multivariate regression analysis method, including: Using crack width as the benchmark for rapid calculation of crack life, a time-varying model of crack width evolution is constructed. : ; In the formula, Separately for the moment crack width, ambient temperature, and vehicle axle number; is the initial crack width, is the regression coefficient; Substitute the characteristic data set into the time-varying model of crack width evolution, fit the regression coefficients, and complete the model construction.

[0027] During the model construction process, the constructed time-varying model of crack width evolution is cross-validated to obtain the model accuracy. If the model accuracy does not meet the preset standard, the regression coefficient is refitted until the model accuracy meets the preset standard. Figure 2 As shown, a schematic diagram of the time-varying curve of the crack width evolution generated by the time-varying model of the crack width evolution is given.

[0028] In other optional implementations, the above method can also be used to construct a time-varying model of the evolution of crack length. .

[0029] Step S4: Based on the time-varying model of crack evolution, the time when the crack reaches the safety threshold is calculated to obtain the crack life.

[0030] Specifically in this embodiment, calculating the time when the crack reaches the safety threshold to obtain the crack life includes: Get the current crack width , and calculate its difference with the safety threshold The difference : ; According to the time-varying model of crack width evolution, the growth rate of crack width is predicted from the current moment. : ; According to the difference and growth rate , calculate the time when the crack reaches the safety threshold : ; Time As the crack lifetime from the current moment.

[0031] In other optional implementations, the above method can also be used to calculate the time when the crack length reaches the corresponding safety threshold as the crack life, or the time when the crack length and the crack width reach the corresponding safety threshold can be calculated at the same time, and the smaller time is taken as the crack life.

[0032] In addition to calculating the entire asphalt pavement layer of the steel bridge deck, it is also possible to perform zone calculations, including: (1) If Figure 3 As shown in the figure, the asphalt pavement of the steel bridge deck is divided into multiple areas, and multiple sub-areas that are most susceptible to load are selected in each area to calculate the crack condition index of each area. : ; ; In the formula, is the regional fracture evolution risk parameter, is the total area of ​​all sub-regions in the region, For all sub-areas in the region The crack length and crack width of each crack; wherein, the asphalt pavement layer of the steel bridge deck is divided into multiple areas, including the width and length of the area based on the width of a single lane and a preset length (such as 10-20m), and the asphalt pavement layer of the steel bridge deck is divided.

[0033] (2) Sort the areas according to the crack condition index from small to large, and take the area with a preset proportion (such as 15%) as the vulnerable area. The vulnerable area is considered to be the part with the highest risk of crack expansion and needs priority maintenance.

[0034] (3) Based on the time-varying model of crack evolution, the time for the cracks in each vulnerable area to reach the safety threshold is calculated to obtain the crack life of each vulnerable area.

[0035] Furthermore, for each vulnerable area, corresponding characteristic data can be extracted from the characteristic data set, and a multivariate regression analysis method can be used to construct a time-varying model of crack evolution for each vulnerable area, thereby improving the model accuracy of each vulnerable area.

[0036] Each area can be ranked according to the length of time remaining, and the maintenance and repair of cracks in vulnerable areas of the asphalt bridge deck can be reasonably arranged.

[0037] In the actual application process, a reinforced concrete simply supported T-beam bridge built across a river in a certain city is taken as an example. The bridge is 200m long and 15m wide. The bridge deck is paved with 10cm asphalt concrete pavement. The load level is highway level I. It was officially put into use in 2022. The crack life of the vulnerable area of ​​the asphalt pavement layer of this bridge is studied. From January 2023, the characteristic data of the cracks in the bridge deck pavement layer are collected once a month for one year. The characteristic data are sorted according to the collection time to form a characteristic data set as shown in Table 1 (partial data are listed).

[0038] Table 1: Bridge deck crack characteristics data collection table

[0039] Based on the characteristic data set, a multivariate regression analysis method is used to construct a time-varying model of crack width evolution: ; The bridge is 200m long and is divided into 10 areas with a fixed length of 20m. In each area, 4 square sub-areas of 1m*1m in size that are prone to cracks are selected from the most vulnerable area to load to collect crack characteristic data. The data in each area are analyzed independently to identify the crack risk in the area. Data were collected from the sub-areas in these 10 areas in December. The collection results are shown in Table 2 (partial data are listed).

[0040] Table 2: Crack characteristic data collection table divided by region

[0041] Calculate the crack condition index for each area , the results are shown in Table 3.

[0042] Table 3: Calculation table of pavement crack condition index

[0043] According to the above table, the value, The smaller the value, the denser the cracks and the higher the risk. The values ​​are sorted from small to large, and the top 15% of the areas are selected as vulnerable areas. It can be concluded that areas 7 and 10 are considered to have the highest risk of crack propagation and require priority maintenance.

[0044] According to the specification, cracks in general asphalt pavement are considered to be severely damaged when their width reaches 5mm, so the limit width is: .

[0045] Referring to Table 2, the maximum crack width in region 7 is 3.35 mm, and the maximum crack width in region 10 is 3.51 mm, with differences of 1.65 mm and 1.49 mm, respectively.

[0046] The temperature in the region in January 2024 is , the vehicle axle number is Combined with the temperature and vehicle axle data in December 2023, the crack width evolution time-varying model is introduced: the crack widths corresponding to January and December are 0.31mm and 0.34mm; Based on the widths in December and January calculated above, the crack width growth rate can be calculated: ; The time required for the crack width to reach the limit value in each area 7 and area 10 is derived as follows: ; ; According to the above calculation, it takes 55 months, or 4.6 years, for vulnerable area 7 and 50 months, or 4.1 years, for vulnerable area 10 to reach the safety threshold.

[0047] Embodiment 2: The embodiment of the present invention provides a device for quickly calculating the crack life of an asphalt pavement layer on a steel bridge deck, the device comprising: A data determination module is configured to determine characteristic data of the crack, the characteristic data including crack length, crack width and environmental parameters; A data collection module is configured to dynamically collect feature data and construct a feature data set; A model building module is configured to build a time-varying model of fracture evolution using a multivariate regression analysis method based on a characteristic data set; The life calculation module is configured to calculate the time when the crack reaches the safety threshold based on the time-varying model of crack evolution to obtain the crack life.

[0048] Embodiment three:

[0049] Based on the method for quickly calculating the crack life of the asphalt pavement layer of the steel bridge deck provided in the first embodiment, the embodiment of the present invention provides an electronic device, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps according to the above method.

[0050] Embodiment 4:

[0051] Based on the method for quickly calculating the crack life of the asphalt pavement layer on the steel bridge deck provided in Example 1, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, which implements the steps of the above method when executed by a processor.

[0052] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for quickly calculating the crack life of asphalt pavement on a steel bridge deck, characterized in that: include: Determining characteristic data of the crack, wherein the characteristic data includes crack length, crack width and environmental parameters; Dynamically collecting the feature data and constructing a feature data set; Based on the characteristic data set, a time-varying model of crack evolution is constructed by using a multivariate regression analysis method; Based on the time-varying model of crack evolution, the time when the crack reaches the safety threshold is calculated to obtain the crack life.

2. The method for rapid calculation of crack life of asphalt pavement layer on steel bridge deck according to claim 1 is characterized in that: The environmental parameters include ambient temperature and vehicle axle number.

3. The method for rapidly calculating crack life of asphalt pavement layer on steel bridge deck according to claim 2 is characterized in that: The method of constructing a time-varying model of crack evolution using a multivariate regression analysis method includes: Using crack width as the benchmark for rapid calculation of crack life, a time-varying model of crack width evolution is constructed. : ； In the formula, Separately for the moment crack width, ambient temperature, and vehicle axle number; is the initial crack width, is the regression coefficient; The characteristic data set is substituted into the time-varying model of crack width evolution, and the regression coefficient is fitted to complete the model construction.

4. The method for rapidly calculating crack life of asphalt pavement layer on steel bridge deck according to claim 3 is characterized in that: The method of constructing a time-varying model of crack evolution using a multivariate regression analysis method also includes: The constructed time-varying model of crack width evolution is cross-validated to obtain the model accuracy. If the model accuracy does not meet the preset standard, the regression coefficient is refitted until the model accuracy meets the preset standard.

5. The method for rapidly calculating crack life of asphalt pavement layer on steel bridge deck according to claim 3 is characterized in that: Calculating the time when the crack reaches the safety threshold to obtain the crack life includes: Get the current crack width , and calculate its difference with the safety threshold The difference : ； According to the time-varying model of crack width evolution, the growth rate of crack width is predicted from the current moment. : ； According to the difference and growth rate , calculate the time when the crack reaches the safety threshold : ； Time As the crack lifetime from the current moment.

6. The method for rapid calculation of crack life of asphalt pavement layer on steel bridge deck according to claim 1 is characterized in that: The fast calculation method also includes: The asphalt pavement of the steel bridge deck is divided into multiple areas, and multiple sub-areas that are most susceptible to load are selected in each area to calculate the crack condition index of each area. : ； ； In the formula, is the regional fracture evolution risk parameter, is the total area of ​​all sub-regions in the region, For all sub-areas in the region The crack length and crack width of each crack; Sort the areas from small to large according to the crack condition index, and take the areas with a preset proportion as vulnerable areas; Based on the time-varying crack evolution model, the time when the cracks in each vulnerable area reach the safety threshold is calculated to obtain the crack life of each vulnerable area.

7. The method for rapidly calculating crack life of asphalt pavement layer on steel bridge deck according to claim 6 is characterized in that: The dividing the asphalt pavement layer of the steel bridge deck into a plurality of areas includes dividing the asphalt pavement layer of the steel bridge deck using a single lane width and a preset length as the width and length of the area.

8. A device for quickly calculating crack life of asphalt pavement layer on steel bridge deck, characterized in that: The fast calculation device comprises: A data determination module is configured to determine characteristic data of a crack, wherein the characteristic data includes crack length, crack width, and environmental parameters; A data collection module is configured to dynamically collect the feature data and construct a feature data set; A model building module is configured to build a time-varying model of fracture evolution using a multivariate regression analysis method based on the characteristic data set; The life calculation module is configured to calculate the time when the crack reaches the safety threshold based on the crack evolution time-varying model to obtain the crack life.

9. An electronic device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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