Method and system for evaluating residual bearing capacity of thin overlay of road surface and storage medium

Abstract

The invention relates to the field of road engineering, in particular to an in-service asphalt pavement thin overlay residual bearing capacity evaluation method and system based on a conversion coefficient and a storage medium, and the method comprises the steps: obtaining the accumulated axle load action frequency at the traffic lane wheel track of an asphalt pavement; the modulus of each structural layer of the asphalt pavement is obtained through FWD inversion; according to the modulus of each structural layer, constructing a mechanical analysis model through a finite element method to obtain the maximum tensile strain of the overlay; acquiring the maximum stress of the finishing coat according to the maximum tensile strain of the finishing coat; performing an indoor splitting fatigue test by adopting the coring test piece to obtain a fatigue life prediction equation of the overlay; obtaining a conversion coefficient according to the overlay fatigue life prediction equation; and obtaining the residual bearing capacity of the thin overlay according to the maximum stress of the overlay and the conversion coefficient. According to the embodiment of the invention, scientific evaluation and accurate prediction of the residual bearing capacity of the thin overlay of the in-service asphalt pavement are realized through a method of fusing the field measured data and the conversion coefficient of the indoor model.

Description

Technical Field

[0001] This invention relates to the field of road engineering, and more specifically to a method, system, and storage medium for evaluating the remaining bearing capacity of thin overlays of in-service asphalt pavements based on conversion coefficients. Background Technology

[0002] In recent years, highway traffic volume has continued to grow at a high level, and heavy-load traffic has become increasingly common. Coupled with environmental factors such as temperature cycling and rain soaking, in-service asphalt pavements are prone to surface defects such as rutting, cracking, and spalling, leading to accelerated performance degradation. Thin-layer overlays have become the mainstream preventative maintenance method for in-service asphalt pavements due to their convenient construction, minimal traffic disruption, controllable cost, and ability to quickly repair surface defects and improve pavement smoothness and skid resistance. However, in practical applications, the load-bearing capacity of thin-layer overlays is easily affected by the support condition of the original pavement base layer, the bonding quality between the overlay and the original pavement interface, and the aging of the materials themselves. If the remaining load-bearing capacity cannot be accurately evaluated, it can easily lead to premature damage or over-maintenance of the overlay, increasing the total life-cycle cost. Traditional maintenance decision-making models based on apparent defects are no longer sufficient to meet the needs of refined and predictive maintenance of thin-layer overlays. Therefore, scientifically evaluating their remaining load-bearing capacity is of crucial theoretical and practical significance for developing economical and efficient maintenance strategies and extending the service life of thin-layer overlays.

[0003] Current assessments of the remaining load-bearing capacity of thin-layer overlays primarily rely on manual observation or simple equipment to observe surface defects, depending on defect severity or Pavement Condition Index (PCI). Some studies supplement this with RQI and SRI, but the core focus remains on the essence of remaining load-bearing capacity, only considering surface cracks and spalling, indirectly inferring load-bearing capacity without direct quantitative evidence. Even those studies attempting to quantify load-bearing capacity often depend on laboratory fatigue equations. However, laboratory loading differs significantly from complex on-site traffic loads and environments. Directly applying laboratory fatigue equations results in errors exceeding 20%, making it difficult to accurately quantify remaining load-bearing capacity, reliably predict lifespan, amplify maintenance deviations, and easily miss optimal maintenance opportunities, leading to wasted resources.

[0004] To address the shortcomings of the aforementioned methods for evaluating the remaining bearing capacity of thin asphalt pavement overlays, a more intelligent and precise evaluation method specifically designed for thin asphalt pavement overlays is needed. This would enable the development of economical and efficient maintenance strategies, provide accurate guidance for the scientific maintenance of thin overlays, and extend their service life. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and storage medium for evaluating the remaining bearing capacity of thin overlays in in-service asphalt pavements based on conversion coefficients, in order to solve the technical problems of inaccurate quantification, unreliable prediction, and lack of precise basis for maintenance decisions caused by the reliance on apparent indicators and laboratory fatigue equations in the evaluation of the remaining bearing capacity of thin overlays.

[0006] To achieve the above objectives, this invention provides a method for evaluating the remaining bearing capacity of thin-layer overlays in in-service asphalt pavements based on conversion coefficients, comprising: Obtain the cumulative number of axle load applications at the wheel tracks of the asphalt pavement lane; The modulus of each structural layer of asphalt pavement is obtained by FWD inversion; Based on the modulus of each structural layer, a mechanical analysis model is constructed using the finite element method to obtain the maximum tensile strain of the cover layer. The maximum stress of the cover layer is obtained based on the maximum tensile strain of the cover layer. Indoor splitting fatigue tests were conducted using core samples to obtain the fatigue life prediction equation for the cover layer. The conversion coefficients are obtained based on the fatigue life prediction equation of the surface layer; The remaining load-bearing capacity of the thin-layer cover is obtained based on the maximum stress and conversion factor of the cover layer.

[0007] Optionally, the cumulative number of axle load applications at the wheel tracks of the asphalt pavement lane is obtained, including: Traffic data is standardized and wheel track distribution is analyzed to obtain key load parameters acting on the wheel track zone.

[0008] Optionally, the modulus of each structural layer of the asphalt pavement is obtained through FWD inversion, including: At the wheel track strip and shoulder of the test section, the deflection basin data of the road surface are detected using a falling weight deflectometer at preset intervals. Obtain the thickness information of each structural layer of the asphalt pavement; Set initial estimated values ​​for the modulus of each structural layer and their reasonable search boundary range; Based on the theory of elastic layered systems, the deflection basin data and the thickness information of each structural layer are used as inputs. The difference between the theoretical deflection basin and the measured deflection basin is minimized through iterative back calculation, and the dynamic modulus value of each structural layer is obtained.

[0009] Optionally, a mechanical analysis model is constructed based on the modulus of each structural layer, and the maximum tensile strain of the cover layer is obtained using the finite element method, including: Establish a three-dimensional finite element model and define its spatial orientation and dimensions; Set the boundary constraints for the model; Differentiated meshing strategies are adopted for each structural layer along the depth direction, and local meshing is performed in the load-bearing area; The dynamic modulus values ​​of each structural layer obtained by FWD inversion are used as parameters input into the model, and standard axle loads are added for calculation to obtain the corresponding mechanical response data. The maximum tensile strain of the thin-film cover layer along the depth direction is obtained based on the corresponding mechanical response data.

[0010] Optionally, obtaining the maximum stress of the cover layer based on the maximum tensile strain of the cover layer includes: The maximum stress of the overlay layer is obtained according to formula (1): (1) in, The maximum stress of the cover layer, To measure the modulus of the core specimen, Poisson's ratio, This represents the maximum tensile strain of the cover layer.

[0011] Optionally, indoor splitting fatigue tests are conducted using core samples to obtain the fatigue life prediction equation for the overlay layer, including: Cyclic loads with different stress levels were applied to the specimens to obtain the fatigue life of the specimens corresponding to each stress level. Formula (2) was fitted using the obtained data on the relationship between different stress levels and fatigue life. (2) in, For the fatigue life of the coating layer, The maximum stress of the cover layer, For conversion factors, These are the fitting parameters; Based on the fitting results, the fatigue life prediction equation for the roadway overlay layer is obtained: (3) in, For the fatigue life of the roadway overlay, This represents the maximum stress of the cover layer; Based on the fitting results, the fatigue life prediction equation for the shoulder cover surface layer is obtained: (4) in, For the fatigue life of the shoulder overlay, This represents the maximum stress of the cover layer.

[0012] Optionally, the conversion coefficient is obtained based on the fatigue life prediction equation of the cover layer, including: The conversion coefficients are obtained according to formula (5): (5) in, This represents the cumulative number of axle load applications on the shoulder before core sampling. This represents the cumulative number of axle loads applied to the thin-layer cover before core extraction. For the splitting fatigue life of the shoulder specimen, The remaining splitting fatigue life of the roadway specimen. , The fitting parameters for the fatigue equation of the core specimen taken from the road shoulder are... , The fitting parameters for the fatigue equation of the core specimen taken from the roadway are... The tensile stress is at the center of the specimen.

[0013] Optionally, obtaining the remaining load-bearing capacity of the thin-layer cover based on the conversion coefficient includes: The remaining load-bearing capacity of the thin-layer cover at other locations can be obtained according to formula (6): (6) in, For the remaining load-bearing capacity of the thin-layer cover in other locations, For conversion factors, To estimate the remaining fatigue life of core specimens in the laboratory, This represents the maximum stress of the cover layer.

[0014] On the other hand, the present invention also provides an evaluation system for the remaining bearing capacity of thin overlays of in-service asphalt pavements based on conversion coefficients, the evaluation system comprising: The data acquisition module is used to collect historical road traffic volume and FWD deflection data; The data processing module is used to calculate the modulus of each structural layer of the road obtained by FWD inversion. The data calculation module is used to combine the aforementioned parameters to obtain the conversion coefficients; The evaluation module is used to evaluate the remaining load-bearing capacity of the thin-layer cover. A processor for connecting a data acquisition module, a data processing module, a data calculation module, and an evaluation module, the processor being configured to perform any of the methods described above.

[0015] In another aspect, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement any of the methods described above.

[0016] The beneficial effects of this invention are: This invention, through a conversion coefficient method that integrates field measured data and indoor models, achieves a scientific evaluation and accurate prediction of the remaining bearing capacity of thin-layer overlays on in-service asphalt pavements. Specifically, it includes: This invention effectively solves the evaluation error problem caused by the difference between indoor and outdoor conditions in traditional methods by establishing a conversion coefficient between field-measured deflection data and indoor fatigue equations. Based on deflection basin data detected by a falling weight deflectometer and combined with pavement structure layer thickness information, this method constructs a mechanical analysis model that reflects the actual working state of the pavement by inverting the modulus of each structural layer through a theoretical model. The mechanical response at key locations of the thin-layer overlay is calculated using the finite element method, particularly the tensile strain at the bottom of the layer, establishing a remaining life prediction model adapted to actual traffic loads and environmental conditions.

[0017] Compared to traditional methods that rely on apparent defects or directly apply indoor equations, this invention significantly improves the accuracy of residual bearing capacity assessment and avoids prediction biases caused by differences between indoor and outdoor conditions. Furthermore, this method can be implemented based on conventional pavement inspection data, eliminating the need for complex indoor tests, thus greatly reducing evaluation costs and improving engineering applicability. By regularly updating inspection data and correcting conversion coefficients, continuous tracking of the service status of thin-layer overlays can be achieved, providing a reliable basis for preventative maintenance decisions, effectively extending pavement service life and optimizing maintenance resource allocation. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart of a method for evaluating the remaining bearing capacity of thin overlays in in-service asphalt pavements based on a conversion factor, according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a finite element model according to one embodiment of the present invention; Figure 3 This is a depth-dependent distribution diagram of the maximum strain of the cover layer according to an embodiment of the present invention. Figure 4 A flowchart illustrating the equation for predicting the fatigue life of a cover layer according to an embodiment of the present invention; Figure 5 A schematic diagram of a loading device used in an experiment according to an embodiment of the present invention; Figure 6 This is a graph showing the relationship between fatigue life and stress level for specimens at different layers and locations according to an embodiment of the present invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0020] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0021] This embodiment uses data from the Qingyuan section of the Longqing Expressway (K2732~K2753+482) as a reference to further illustrate the method of the present invention.

[0022] like Figure 1 The diagram shows a flowchart of a method for evaluating the remaining bearing capacity of thin overlays in in-service asphalt pavements based on a conversion factor, according to an embodiment of the present invention. Figure 1 In this evaluation method, the steps may include: In step S10, the cumulative number of axle load applications at the wheel tracks of the asphalt pavement lane is obtained; In step S11, the modulus of each structural layer of the asphalt pavement is obtained by FWD inversion; In step S12, a mechanical analysis model is constructed using the finite element method based on the modulus of each structural layer to obtain the maximum tensile strain of the cover layer; In step S13, the maximum stress of the cover layer is obtained based on the maximum tensile strain of the cover layer; In step S14, an indoor splitting fatigue test is conducted using core samples to obtain the fatigue life prediction equation for the cover layer. In step S15, the conversion coefficient is obtained according to the fatigue life prediction equation of the cover layer; In step S16, the remaining load-bearing capacity of the thin-layer cover is obtained based on the maximum stress of the cover layer and the conversion factor.

[0023] In such Figure 1 In the method shown, step S10 is used to obtain the cumulative number of axle load applications at the wheel tracks on the asphalt pavement lanes. Specifically, in this example, a detailed analysis of traffic volume detection data from the Qingyuan section of the Longqing Expressway from 2020 to 2024 can be performed, mainly to determine the cumulative number of axle load applications. The cumulative number of axle load applications is calculated after conversion using direction coefficients, lane coefficients, and equivalent axle load conversion factors, based on the map and the measurement location during the detection process. In this embodiment, the direction coefficient can be 0.5, and the lane coefficient can be 0.8. Further, the number of applications at the wheel tracks accounts for 76.2% of the total cumulative axle load applications, i.e., the number of applications at the wheel tracks is 6,878,743.

[0024] Step S11 is used to obtain the modulus of each structural layer of the asphalt pavement through FWD inversion. Specifically, in this embodiment, the deflection basin data of the pavement surface can be detected at 50m intervals using a falling weight deflectometer at selected lane wheel track strips and shoulders of the test section, where the load level is 50KN. Next, based on the deflection basin data measured by FWD and combined with the thickness information of each structural layer of the pavement, iterative back-calculation is performed through a theoretical analysis model to finally determine the modulus of each structural layer. The FWD has a total of 9 deflection sensors, and their specific positions are divided into radial distances (unit: cm): 0, 20, 30, 45, 60, 90, 120, 142.5, and 165. To ensure data stability, in this embodiment, the initial value of the subgrade dynamic modulus is uniformly set to 298 MPa, the lower boundary is set to 30 MPa, and the upper boundary is set to 400 MPa during the inversion. The initial value of the asphalt layer dynamic modulus is uniformly set to 5000 MPa, the lower boundary is set to 100 MPa, and the upper boundary is set to 25000 MPa. A representative value is searched within the set dynamic modulus range. Finally, based on the theory of elastic layered systems, using deflection basin data and the thickness of each layer as input, the difference between the theoretical deflection basin and the measured deflection basin is minimized through iterative inversion to obtain the dynamic modulus values ​​of each structural layer of the Qingyuan section of the Longqing Expressway. Some modulus values ​​after FWD inversion are shown in Table 1. Table 1. Partial Modulus Values ​​After FWD Inversion

[0025] Step S12 is used to construct a mechanical analysis model using the finite element method based on the modulus of each structural layer to obtain the maximum tensile strain of the overlay layer. Specifically, in this embodiment, the mechanical analysis model can be a static analysis model, with boundary conditions set to restrict normal displacement in the X and Z directions along the horizontal direction, and completely fixed in the Y direction. Each structural layer is meshed along the depth direction according to a preset size, with the asphalt layer meshed at 1cm, the base layer at 4cm, and the soil subgrade mesh gradually becoming sparser along the depth direction, resulting in a total of 20 meshes. The load application area is divided into 10 meshes along the driving direction and perpendicular to the driving direction, respectively, according to a preset number. To improve computational efficiency, the mesh in the load area is denser, gradually becoming sparser further away from the load area. The finite element model constructed based on the above data is as follows: Figure 2As shown. Next, the modulus of each layer is determined. The Poisson's ratio values ​​for the surface layer, water-stabilized crushed stone base course, subbase course, and subgrade are taken from the standard values, while the modulus values ​​are taken from the modulus values ​​calculated by FWD. Finally, the dynamic modulus values ​​of each structural layer obtained from FWD inversion are input as material parameters into the finite element model, and a standard axle load is added for calculation, outputting the mechanical response inside the pavement structure to obtain the maximum tensile strain of the overlay layer along the depth direction. Using the finite element method, the maximum strain of the overlay layer is determined with a maximum value every 0.5 cm starting from the top of the layer. The distribution of the maximum strain of the overlay layer along the depth is shown in the figure. Figure 3 As shown, tensile strain is positive and compressive strain is negative.

[0026] Step S13 is used to obtain the maximum stress of the cover layer based on the maximum tensile strain of the cover layer. Specifically, since the tensile stress of the thin cover layer cannot be obtained by direct measurement, in this embodiment, the maximum stress of the cover layer can be indirectly obtained by formula (1) based on the maximum tensile strain of the cover layer obtained by finite element calculation, Poisson's ratio, and core specimen modulus. (1) in, The maximum stress of the cover layer, To measure the modulus of the core specimen, Poisson's ratio, This represents the maximum tensile strain of the cover layer.

[0027] Analysis of the finite element model constructed in step S12 shows that the maximum strain of the overlay layer is 99.01 με, located at the road surface. Based on the strain value, the maximum stress value of the overlay layer corresponding to the specimen with the maximum strain can be obtained according to formula (2): (2) Step S14 involves conducting an indoor splitting fatigue test using core samples to obtain a fatigue life prediction equation for the overcoat layer. In this embodiment, the specific method for obtaining the fatigue life prediction equation for the overcoat layer in step S14 can be of various forms known to those skilled in the art. In one example of the present invention, step S14 may include, for example… Figure 4 The steps shown are described in this. Figure 4 In this context, step S14 may include: In step S20, cyclic loads of different stress levels are applied to the specimen to obtain the fatigue life of the specimen corresponding to each stress level; In step S21, the fatigue life prediction equation is fitted using the obtained data on the correspondence between different stress levels and fatigue life. In step S22, fatigue life prediction equations for the roadway and shoulder overlay layers are obtained based on the results of the indoor splitting fatigue test.

[0028] In such Figure 4 In the method shown, step S20 is used to apply cyclic loads of different stress levels to the specimen and obtain the fatigue life of the specimen corresponding to each stress level. Specifically, in this embodiment, core sampling can be performed at the wheel tracks and shoulder locations of the highway driving lane. Core sampling is performed on the right wheel track, which is frequently subjected to vehicle loads, to obtain the pavement material most significantly affected by the load. Core sampling from the shoulder is used for comparison. A cutting machine is used to separate the core sample into layers and the overlay layer, and the thickness of the overlay layer core sample after cutting is measured using vernier calipers. Next, two displacement sensors are placed in the transverse direction of the specimen to record the horizontal radial deformation of the specimen in real time during loading. The vertical plane under the loading center is subjected to tensile stress, the direction of which is perpendicular to the loading direction, eventually resulting in cracks in this plane. The loading device used in the experiment is a UTM-30, which is described as follows... Figure 5 As shown. Finally, the core samples of the cover layer drilled on site were processed into standard specimens and grouped. A material testing system was used to apply cyclic loads of different stress levels to each group of specimens, and the number of loads applied at the time of specimen failure corresponding to each stress level was accurately recorded, i.e., fatigue life.

[0029] Step S21 is used to fit the fatigue life prediction equation using the obtained data on the correspondence between different stress levels and fatigue life. Specifically, in this embodiment, the relationship between fatigue life and stress level for specimens at different layers and locations can be determined using the SN method based on fatigue life. Figure 6 As shown, the obtained data on the relationship between different stress levels and fatigue life were fitted to formula (3): (3) in, For the fatigue life of the coating layer, The maximum stress of the cover layer, For conversion factors, These are the fitting parameters.

[0030] Step S22 is used to obtain fatigue life prediction equations for the driveway and shoulder overlay layers based on the results of indoor splitting fatigue tests. Specifically, in this embodiment, the relationship between fatigue life and maximum stress of the overlay layer can be fitted by combining different results of indoor fatigue tests, and the fatigue life prediction equation for the driveway overlay layer can be obtained based on the fitting results. (4) in, For the fatigue life of the roadway overlay, This represents the maximum stress of the cover layer; Based on the fitting results, the fatigue life prediction equation for the shoulder cover surface layer is obtained: (5) in, For the fatigue life of the shoulder overlay, This represents the maximum stress of the cover layer.

[0031] Step S15 is used to obtain the conversion coefficient based on the fatigue life prediction equation of the overlay layer. The conversion coefficient is used to convert the indoor splitting fatigue test results with the actual pavement damage. Specifically: In this embodiment, since the overlay layer core samples obtained from the shoulder and the driving lane belong to the same road segment, and the materials, gradation, and mix proportions are completely consistent, the environmental impact can be considered to be basically the same. Therefore, the conversion coefficient is obtained according to formula (6): (6) in, This represents the cumulative number of axle load applications on the shoulder before core sampling. This represents the cumulative number of axle loads applied to the thin-layer cover before core extraction. For the splitting fatigue life of the shoulder specimen, The remaining splitting fatigue life of the roadway specimen. , The fitting parameters for the fatigue equation of the core specimen taken from the road shoulder are... , The fitting parameters for the fatigue equation of the core specimen taken from the roadway are... The tensile stress is at the center of the specimen.

[0032] According to formula (5), after obtaining the annual traffic volume data since the road began operation and the distribution pattern of axle load in different lanes, the equivalent standard axle load frequency difference can be calculated. Furthermore, different stress levels correspond to different differences in residual splitting fatigue life. Therefore, it is confirmed. The key lies in determining the corresponding stress level. This application defines the conversion factor as: under the same stress level, the ratio of the number of equivalent standard axle load cycles to the indoor splitting fatigue life of the pavement from the initial state to the failure state. Therefore, as long as the indoor splitting fatigue stress level equivalent to the stress state of the pavement under standard axle load is known, the conversion factor can be calculated. .

[0033] Substitute the maximum stress value and the cumulative number of axial loads into formula (6), and calculate the conversion factor of the overlay layer according to formula (7): (7) The conversion factor is calculated according to formula (7). The value of 158.19 indicates that a single indoor splitting fatigue load at a stress level equivalent to the standard axle load is equivalent to the overlay being subjected to 158.19 cycles of the standard axle load.

[0034] Step S16 is used to obtain the remaining bearing capacity of the thin overlay according to the conversion factor. Specifically: in this embodiment, the remaining bearing capacity of the thin overlay at locations other than the core sample in the road surface can be calculated according to formula (8): (8) in, For the remaining load-bearing capacity of the thin-layer cover in other locations, For conversion factors, To estimate the remaining fatigue life of core specimens in the laboratory, This represents the maximum stress of the cover layer.

[0035] Substitute the conversion factor of the overlay layer into formula (8) to obtain the predicted remaining bearing capacity of the thin overlay layer on this road section: (9) Therefore, it can be concluded that the thin-layer overlay of this road section, under its current condition, is expected to withstand approximately 8,233,178 cycles of standard axle load. This result not only considers the loading modes of the indoor tests but also the differences in complex traffic loads and environmental factors present on-site, avoiding the significant errors that arise from directly using indoor fatigue equations, thus improving the accuracy and rationality of the prediction.

[0036] On the other hand, this invention also provides an evaluation system for the remaining bearing capacity of thin-layer asphalt pavement overlays based on conversion coefficients. The evaluation system includes a data acquisition module, a data processing module, a data calculation module, an evaluation module, and a processor. The data acquisition module collects historical traffic volume and FWD deflection data to obtain the cumulative axle load counts at the wheel tracks and the modulus of each structural layer of the road. The data processing module, based on the road structural layer moduli obtained from FWD inversion, establishes a mechanical analysis model reflecting the actual pavement condition using the finite element method and calculates the mechanical response of the thin-layer asphalt pavement overlay under standard axle loads. The data calculation module calculates the conversion coefficients using the aforementioned parameters. The evaluation module evaluates the remaining bearing capacity of the thin-layer overlay by multiplying the calculated conversion coefficients by the estimated fatigue life obtained from indoor splitting tests on the thin-layer overlay specimens. The processor is connected to the data acquisition module, data processing module, data calculation module, and evaluation module, and is configured to execute any of the methods described above.

[0037] In another aspect, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement any of the evaluation methods described above.

[0038] The beneficial effects of this invention are: This invention, through a conversion coefficient method that integrates field measured data and indoor models, achieves a scientific evaluation and accurate prediction of the remaining bearing capacity of thin-layer overlays on in-service asphalt pavements. Specifically, it includes: This invention effectively solves the evaluation error problem caused by the difference between indoor and outdoor conditions in traditional methods by establishing a conversion coefficient between field-measured deflection data and indoor fatigue equations. Based on deflection basin data detected by a falling weight deflectometer and combined with pavement structure layer thickness information, this method constructs a mechanical analysis model that reflects the actual working state of the pavement by inverting the modulus of each structural layer through a theoretical model. The mechanical response at key locations of the thin-layer overlay is calculated using the finite element method, particularly the tensile strain at the bottom of the layer, establishing a remaining life prediction model adapted to actual traffic loads and environmental conditions.

[0039] Compared to traditional methods that rely on apparent defects or directly apply indoor equations, this invention significantly improves the accuracy of residual bearing capacity assessment and avoids prediction biases caused by differences between indoor and outdoor conditions. Furthermore, this method can be implemented based on conventional pavement inspection data, eliminating the need for complex indoor tests, thus greatly reducing evaluation costs and improving engineering applicability. By regularly updating inspection data and correcting conversion coefficients, continuous tracking of the service status of thin-layer overlays can be achieved, providing a reliable basis for preventative maintenance decisions, effectively extending pavement service life and optimizing maintenance resource allocation.

[0040] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0041] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0042] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0043] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0044] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0045] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0046] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0047] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0048] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for evaluating the remaining bearing capacity of a thin-layer pavement overlay, characterized in that, The evaluation methods include: Obtain the cumulative number of axle load applications at the wheel tracks of the asphalt pavement lane; The modulus of each structural layer of asphalt pavement is obtained by FWD inversion; Based on the modulus of each structural layer, a mechanical analysis model is constructed using the finite element method to obtain the maximum tensile strain of the cover layer. The maximum stress of the cover layer is obtained based on the maximum tensile strain of the cover layer. Indoor splitting fatigue tests were conducted using core samples to obtain the fatigue life prediction equation for the cover layer. The conversion coefficients are obtained based on the fatigue life prediction equation of the surface layer; The remaining load-bearing capacity of the thin-layer cover is obtained based on the maximum stress and conversion factor of the cover layer.

2. The evaluation method according to claim 1, characterized in that, Obtain the cumulative number of axle load applications at the wheel tracks of the asphalt pavement lane, including: Traffic data is standardized and wheel track distribution is analyzed to obtain key load parameters acting on the wheel track zone.

3. The evaluation method according to claim 1, characterized in that, The modulus of each structural layer of asphalt pavement is obtained through FWD inversion, including: At the wheel track strip and shoulder of the test section, the deflection basin data of the road surface are detected using a falling weight deflectometer at preset intervals. Obtain the thickness information of each structural layer of the asphalt pavement; Set initial estimated values ​​for the modulus of each structural layer and their reasonable search boundary range; Based on the theory of elastic layered systems, the deflection basin data and the thickness information of each structural layer are used as inputs. The difference between the theoretical deflection basin and the measured deflection basin is minimized through iterative back calculation, and the dynamic modulus value of each structural layer is obtained.

4. The evaluation method according to claim 1, characterized in that, Based on the modulus of each structural layer, a mechanical analysis model is constructed using the finite element method to obtain the maximum tensile strain of the cover layer, including: Establish a three-dimensional finite element model and define its spatial orientation and dimensions; Set the boundary constraints for the model; Differentiated meshing strategies are adopted for each structural layer along the depth direction, and local meshing is performed in the load-bearing area; The dynamic modulus values ​​of each structural layer obtained by FWD inversion are used as parameters input into the model, and standard axle loads are added for calculation to obtain the corresponding mechanical response data. The maximum tensile strain of the thin-film cover layer along the depth direction is obtained based on the corresponding mechanical response data.

5. The evaluation method according to claim 1, characterized in that, The maximum stress of the cover layer is obtained based on the maximum tensile strain of the cover layer, including: The maximum stress of the overlay layer is obtained according to formula (1): ，（1） in, The maximum stress of the cover layer, To measure the modulus of the core specimen, Poisson's ratio, This represents the maximum tensile strain of the cover layer.

6. The evaluation method according to claim 1, characterized in that, Indoor splitting fatigue tests were conducted using core samples to obtain the fatigue life prediction equation for the overlay layer, including: Cyclic loads with different stress levels were applied to the specimens to obtain the fatigue life of the specimens corresponding to each stress level. Formula (2) was fitted using the obtained data on the relationship between different stress levels and fatigue life. ，（2） in, For the fatigue life of the coating layer, The maximum stress of the cover layer, For conversion factors, These are the fitting parameters; Based on the fitting results, the fatigue life prediction equation for the roadway overlay layer is obtained: ，（3） in, For the fatigue life of the roadway overlay, This represents the maximum stress of the cover layer; Based on the fitting results, the fatigue life prediction equation for the shoulder cover surface layer is obtained: ，（4） in, For the fatigue life of the shoulder overlay, This represents the maximum stress of the cover layer.

7. The evaluation method according to claim 1, characterized in that, Based on the fatigue life prediction equation of the surface layer, the conversion coefficients are obtained, including: The conversion coefficients are obtained according to formula (5): ，（5） in, This represents the cumulative number of axle load applications on the shoulder before core sampling. This represents the cumulative number of axle loads applied to the thin-layer cover before core extraction. For the splitting fatigue life of the shoulder specimen, The remaining splitting fatigue life of the roadway specimen. , The fitting parameters for the fatigue equation of the core specimen taken from the road shoulder are... , The fitting parameters for the fatigue equation of the core specimen taken from the roadway are... The tensile stress is at the center of the specimen.

8. The evaluation method according to claim 1, characterized in that, The remaining load-bearing capacity of the thin-layer cover is obtained based on the conversion coefficient, including: The remaining load-bearing capacity of the thin-layer cover at other locations can be obtained according to formula (6): ，（6） in, For the remaining load-bearing capacity of the thin-layer cover in other locations, For conversion factors, To estimate the remaining fatigue life of core specimens in the laboratory, This represents the maximum stress of the cover layer.

9. A system for evaluating the remaining bearing capacity of a thin-layer road overlay, characterized in that, The evaluation system includes: The data acquisition module is used to collect historical road traffic volume and FWD deflection data; The data processing module is used to obtain the modulus of each structural layer of the road based on the FWD inversion. The data calculation module is used to obtain the conversion coefficients; The evaluation module is used to evaluate the remaining load-bearing capacity of the thin-layer cover. A processor for connecting a data acquisition module, a data processing module, a data calculation module, and an evaluation module, the processor being configured to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 8.

Images