A Smart Particle-Based Method for Monitoring Rutting Deformation of Asphalt Pavement Structures
By constructing a three-dimensional model and a spatiotemporal correlation model using intelligent particle sensors, the real-time and accuracy issues of monitoring rutting deformation in asphalt pavements were resolved, enabling early warning of rutting depth and support for maintenance decisions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-30
- Publication Date
- 2026-03-10
AI Technical Summary
Existing technologies are insufficient for real-time monitoring of aggregate movement during rutting deformation of asphalt pavements, and embedded sensors suffer from problems such as complex maintenance and difficult data processing.
By employing intelligent particle sensors, a three-dimensional numerical model is constructed to obtain particle mechanical parameters, a feature partitioning model is established, and a spatiotemporal correlation model is constructed using grey relational analysis to achieve early warning of rut depth.
It enables real-time monitoring of aggregate movement during rut deformation, provides accurate early warning information, guides maintenance decisions, and avoids damage to the pavement structure caused by sensor installation.
Smart Images
Figure CN116050108B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of intelligent monitoring of asphalt pavement structure, more particularly, to a rut deformation monitoring method for asphalt pavement structure based on intelligent particles. BACKGROUND
[0002] The monitoring technology for the purpose of real-time monitoring of the internal health status of road structure is one of the important components of intelligent road construction. It dynamically obtains the internal state information of road structure in real time, evaluates the performance of road structure, and provides scientific decision-making for pavement construction, management and maintenance.
[0003] With the maturity of asphalt mixture and pavement construction technology, rut, a long-term disease, has gradually become the main disease affecting the service life of asphalt pavement. Therefore, it is necessary to obtain the key parameters of the performance change of pavement structure in the rut deformation behavior of asphalt pavement in real time and accurately, to realize real-time monitoring of rut disease, and to provide support for road management and maintenance decision-making and optimal design according to the measured data.
[0004] However, at present, the rut deformation monitoring of asphalt pavement structure has the following scientific problems to be solved urgently:
[0005] At present, the research mainly obtains the movement state of coarse aggregate particles after rut deformation by digital image method, but it is difficult to obtain the movement state of aggregate in the rut deformation process, which cannot realize real-time monitoring of the rut deformation process.
[0006] The sensing technology applied to pavement monitoring mainly includes non-buried type and buried type. The non-buried type is easily affected by environment and weather, while the buried type sensing technology can be used to monitor the dynamic response of pavement under the coupling of vehicle cyclic load and various environmental factors. Most of the widely used buried sensors are wired transmission sensors, which have the disadvantages of complex maintenance and replacement procedures, pavement structure damage during sensor installation process, huge amount of on-site data collection, and difficulty in real-time data processing.
[0007] The intelligent particle sensor has the characteristics of small size, high precision, rich monitoring data type, non-damage to the original structure of pavement, self-power supply and self-adaptation. Based on the mechanism that rut is gradually accumulated due to the mutual embedding and extrusion of mineral aggregates under repeated load, the intelligent particles are applied to the rut monitoring, which has better applicability.
[0008] However, the intelligent particles are mainly used in the research of track ballast particle settlement and the evaluation of asphalt mixture compaction degree, and there are few studies on the monitoring of particle movement under the action of asphalt mixture load. SUMMARY
[0009] In order to solve at least one of the above technical problems, according to an aspect of the present application, there is provided a smart particle-based asphalt pavement structure rutting deformation monitoring method, comprising the following steps:
[0010] S1, a three-dimensional numerical model of smart particles is constructed, the mechanical parameters of the smart particles at the mesoscale are obtained and calibrated, and the effectiveness of the smart particles for monitoring the skeleton behavior of the mixture is verified;
[0011] S2, a characteristic partition model of the asphalt layer under the rutting deformation of the asphalt pavement is constructed according to the stress-deformation state (vertical tensile and compressive strain ε m , shear deformation γ t ), and the analytic hierarchy process is used to obtain the sensitive characteristic response parameters of the smart particles, so as to realize the monitoring matching identification under the characteristic partition;
[0012] S3, on the basis of judging the characteristic partition, a time-space correlation model of the smart particle monitoring information and the rut depth is constructed by using the grey correlation analysis method;
[0013] S4, the actual pavement burying scheme of the smart particles is determined, the actual monitoring pose evolution information under the rutting behavior of the pavement is obtained, the characteristic response parameters determined in S2 are compared based on the actual measured pose information of the smart particles, the characteristic partition in which the smart particles are located is judged, and finally the time-space correlation model established in S3 is used to give an early warning to the rut depth, so as to guide the maintenance decision and determine the rut treatment method.
[0014] As a preferred technical scheme of the present application: in S1, firstly, a three-dimensional virtual model of smart particles is established by using a linear constitutive model in PFC3D software, the particle arrangement mode is quincunx arrangement (hexagonal arrangement), and the mesoscopic contact parameters are converted according to formula (1)-(4). Secondly, the macroscopic mechanical parameters are obtained through the uniaxial compression and direct tensile numerical test of the smart particles: uniaxial compressive strength C u , elastic modulus E, Poisson's ratio v, uniaxial tensile strength T u , and finally the acquisition and calibration of the mesoscopic contact parameters of the smart particles: particle effective modulus E * , particle stiffness ratio k n / k s (k n is the normal contact stiffness, k s is the tangential contact stiffness), particle friction coefficient μ p are realized.
[0015]
[0016] E * = 0.5E (1)
[0017] v = 1 / 4 (1-K s / K n ) (2)
[0018]
[0019]
[0020]
[0021] Formula (1) wherein: X b , intergranular fracture displacement; F S0 , intergranular shear resistance; d, particle diameter; μ p , intergranular friction coefficient; μ i , intrinsic friction coefficient.
[0022] As a preferred technical solution of the present application: in S1, the grading skeleton calculation theory is proposed, and the upper and lower limit calculation formula of the skeleton grading with good interlocking state is obtained, as shown in formula (5). According to formula (5), three groups of gradings are calculated and designed, and AC-13, SMA-13 median gradings are additionally selected, a total of five groups. In the PFC3D software, the aggregate skeleton of the five groups of gradings is generated for simulation and simulation, and virtual intelligent particle models are placed in all aggregate skeletons, and the mesoscopic contact response of intelligent particles in different skeleton interlocking states is extracted, including: average coordination number and contact ratio. On this basis, by comparing the state of each group of skeletons and the mesoscopic contact response of intelligent particles, the average coordination number and contact ratio are used to verify the superiority of the skeleton designed according to formula (5), and the effectiveness of the intelligent particles applied to monitor the behavior of different aggregate skeletons is verified.
[0023]
[0024] Formula (2) wherein: D avg , weighted average particle size of two groups of aggregates, mm; average particle size of aggregates on the larger and smaller screen, mm.
[0025] As a preferred technical solution of the present application: in S2, first, according to the different stress-deformation states (vertical tensile and compressive strain ε m , shear deformation γ t ), based on the elastic layered system theory, the asphalt layer is divided into the following zones under the action of single circular uniform vertical load: compression zone (ε m <0, mainly compression), dense flow zone (ε m <0, γ t >0, mainly shear flow), flow and expansion zone (ε m >0, γ t >0, shear expansion flow), expansion zone (ε m>0, mainly due to shear expansion) 4 deformation regions were identified and corresponding asphalt layer feature zoning models were constructed under asphalt pavement rutting deformation. The pose evolution of smart particles embedded in the 4 feature deformation regions during the rutting behavior was simulated and the response analysis was carried out. The measured information (stress, strain, rotation angle) of smart particles under the rutting deformation behavior was obtained. The sensitive feature response parameters were determined based on the monitoring information, and finally the monitoring matching and identification under the feature zoning was realized.
[0026] As a preferred technical solution of the present invention: In S2, the characteristic response parameters are determined by the hierarchical analysis method. First, a layer is established with the deformation zone as the highest layer, and the vertical tensile and compressive strain ε m Shear deformation γ t As an intermediate layer, stress, strain, and the rotation angle of the intelligent particle form the lowest-level hierarchical model. Next, quantified relative importance is used to represent the importance of different elements, and judgment matrices are constructed accordingly. Then, the eigenvalue method is applied to determine the maximum eigenvalue and its corresponding eigenvector of each judgment matrix, and the calculated eigenvectors are normalized to obtain our weights. Simultaneously, a consistency check is performed; when the consistency ratio CR < 0.1, the matrix is considered to have satisfactory consistency. Based on this, the importance of each layer's elements to the overall system is calculated from top to bottom: after calculating the weights of vertical tensile and compressive strain and shear deformation, the weights between stress, strain, and the rotation angle of the intelligent particle are calculated. The magnitude of the weight indicates the sensitivity of the intelligent particle's characteristic response parameters, ultimately achieving monitoring, matching, and identification under feature partitioning.
[0027] As a preferred technical solution of the present invention: In S3, a spatiotemporal correlation model between intelligent particle monitoring information and rut depth is established using the grey relational analysis method: First, intelligent particle pose information, stress, strain, and rotation angle evolution information are selected as the comparison sequence x. 1(k) x 2(k) x 3(k) The depth of rut deformation is selected as the reference sequence x. 0(k) Next, the comparison series is dimensionless according to equation (6), and the grey relational coefficient between the reference series and the comparison series is calculated according to equation (7). Finally, the correlation degree of each pose information parameter is calculated according to equation (8), and the monitoring information parameter with the highest correlation degree is selected to establish a relationship model with the rut depth.
[0028]
[0029]
[0030]
[0031] In the formula: x i(k) Each sequence is compared; n is the number of data points; Each comparison series mean; p, resolution coefficient, the smaller the p, the stronger the resolution ability; x 0(k) Reference series.
[0032] As a preferred technical solution of the present application: in S4, the intelligent particles are embedded at the bottom of each pavement layer, and the distance from the wheel track band to the center line is 1 cm, 2 cm, 3 cm, 4 cm and 5 cm respectively, the actual monitoring position evolution information of the intelligent particles under the rut behavior of the pavement is recorded by connecting the computer, and the center rut depth is recorded. On this basis, based on the feature response parameters determined in S2, the feature partition where the intelligent particles are located is judged, and finally the time and space correlation model established in S3 is used to warn the rut depth, so as to guide the maintenance decision and determine the rut treatment method.
[0033] According to another aspect of the present application, a computer readable storage medium is provided, which stores a computer program, the program being executed by a processor to implement the steps in the intelligent particle based asphalt pavement structure rut deformation monitoring method of the present application.
[0034] According to still another aspect of the present application, a computer device is provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the steps in the intelligent particle based asphalt pavement structure rut deformation monitoring method of the present application.
[0035] Compared with the prior art, the present application has at least the following beneficial effects:
[0036] The intelligent particle sensor device has the characteristics of small volume, high precision, rich monitoring data types, non-destructive to the original structure of the pavement, self-power supply and self-adaptation, and can obtain the motion state of the aggregate in the rut deformation process;
[0037] Through the method of multi-scale simulation, the rut deformation behavior is simulated at the three scales of particle-skeleton-pavement;
[0038] Based on the intelligent particle sensing parameters, the in-situ acquisition of monitoring data is realized, and according to the correlation model, the rut depth is warned to guide the subsequent maintenance decision. BRIEF DESCRIPTION OF DRAWINGS
[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments will be briefly introduced below, and obviously, the drawings described below only relate to some embodiments of the present application, but not limit the present application.
[0040] Figure 1 Flowchart of the present application;
[0041] Figure 2A three-dimensional numerical model of the intelligent particle of the present application is shown in the figure;
[0042] Figure 3 A partition diagram of the asphalt layer under the rut deformation of the present application is shown in the figure;
[0043] Figure 4 A hierarchical structure diagram of the present application is shown in the figure;
[0044] Figure 5 A schematic diagram of the intelligent particle pavement burying scheme is shown in the figure. DETAILED DESCRIPTION
[0045] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application.
[0046] Unless otherwise defined, the technical terms or scientific terms used herein should be understood as the usual meanings understood by those skilled in the art to which the present application belongs.
[0047] As shown in Figures 1-5 ,
[0048] Embodiment 1:
[0049] The rut deformation monitoring method for the asphalt pavement structure based on the intelligent particle of the present embodiment, as shown in Figure 1 , includes the following steps: S1, constructing a three-dimensional numerical model of the intelligent particle, obtaining and calibrating the mechanical parameters of the intelligent particle under the mesoscopic scale and verifying the effectiveness of the intelligent particle for monitoring the skeleton behavior of the mixture;
[0050] In S1, first, a three-dimensional virtual model of the intelligent particle is established in the PFC3D software by using a linear constitutive model and a quincunx arrangement (hexagonal arrangement) of the particles, and the mesoscopic contact parameters are converted according to formulas (1)-(4). Second, the macroscopic mechanical parameters: uniaxial compressive strength C u , elastic modulus E, Poisson's ratio v, uniaxial tensile strength T u , are obtained through the uniaxial compression and direct tensile numerical tests of the intelligent particle, and are compared with the actual mechanical test parameters. Finally, the intelligent particle mesoscopic contact parameters: particle effective modulus E * , particle stiffness ratio k n / k s (k n is the normal contact stiffness, k s is the tangential contact stiffness), particle friction coefficient μ p , are obtained and calibrated.
[0051]
[0052] E * =0.5E (1)
[0053] ν=1 / 4(1-K s / K n (2)
[0054]
[0055]
[0056]
[0057] In the formula: X b Interparticle fracture displacement; F S0 Interparticle shear resistance; d, particle diameter; μ p The coefficient of friction between particles; μ i , inherent coefficient of friction.
[0058] Based on the gradation skeleton theory, the effectiveness of good mutual contact between adjacent aggregates is examined. Multiple aggregate mixtures with different gradation amounts are calculated and designed according to formula (5). These mixtures are then simulated using PFC3D software, with smart particles embedded within them. The microscopic contact parameters of the smart particles under different skeleton states are extracted: average coordination number and contact ratio. Based on this, the effectiveness of using smart particles to monitor skeleton behavior is verified by comparing the skeleton states and the microscopic contact parameters of the smart particles.
[0059]
[0060] In the formula: D avg The weighted average particle size of the two aggregate grades, in mm; The average particle size of the aggregate on the larger and smaller screens, in mm.
[0061] S2, First, based on the stress-deformation state (vertical tensile / compressive strain ε) m Shear deformation γ t Based on the theory of elastic layered systems, under a single circular uniformly distributed vertical load, the asphalt layer of a multi-layered pavement structure is divided into compaction zones (ε). m <0, mainly compaction), dense flow domain (ε) m <0,γ t >0, shear flow is dominant), flow expansion region (ε) m >0,γ t >0, shear dilatation flow is equally important), dilatation domain (ε mfour deformation zones and build the corresponding asphalt pavement rut deformation under the asphalt layer feature zoning model, simulate the pose evolution of intelligent particles embedded in the four feature deformation zones during the rutting behavior occurrence and perform response analysis, obtain the measured information (stress, strain, rotation angle) of intelligent particles under the rutting deformation behavior, determine the sensitive feature response parameters according to the monitoring information, and finally realize the monitoring matching identification under the feature zoning.
[0062] The analytic hierarchy process is used to determine the feature response parameters:
[0063] 1) Establish a hierarchical structure model
[0064] The judgment of the four deformation zones is taken as the decision target, which is the highest layer; the vertical tensile and compressive strain ε m , shear deformation γ t are considered as the indexes, which are the middle layer; the stress, strain and rotation angle are taken as the selection scheme, which is the lowest layer. According to the hierarchical structure diagram
[0065] The judgment of the four deformation zones is taken as the decision target, which is the highest layer; the vertical tensile and compressive strain ε m , shear deformation γ t are considered as the indexes, which are the middle layer; the stress, strain and rotation angle are taken as the selection scheme, which is the lowest layer. According to the hierarchical structure diagram
[0066] 2) Construct the judgment matrix
[0067] After the hierarchical model is constructed, for a certain layer, when comparing the importance of the i-th element and the j-th element with respect to a certain factor in the previous layer, the quantitative relative importance is used to represent. Assuming that there are n elements participating in the comparison, the matrix A is called the judgment matrix.
[0068]
[0069] The judgment matrices A-B (Table 1), B1-C (Table 2) and B2-C (Table 3) are constructed respectively.
[0070]
[0071] 3) Determine the weight by the eigenvalue method:
[0072] First step: find the maximum eigenvalue of the judgment matrix and its corresponding eigenvector
[0073] Second step: normalize the obtained eigenvector to obtain our weight.
[0074] At the same time, consistency check: find the consistency index: CI=0 means complete consistency, and the larger the CI, the less consistent; the average random consistency index RI is obtained by averaging the random simulation, or directly using the RI table:
[0075] Matrix order 3 4 5 6 7 8 …
[0076] Calculate the consistency ratio: When the consistency ratio CR<0.1, it is considered that the matrix has satisfactory consistency; otherwise, the judgment matrix is considered to be modified.
[0077] After obtaining the importance degree between each element of the same level, the scheme can be sorted, that is, the intelligent particle characteristic response parameters are selected.
[0078] On this basis, the importance of each layer element to the whole is calculated from top to bottom: after calculating the vertical tensile and compressive strain, shear deformation weight, the weight between stress, strain and intelligent particle rotation angle is calculated, and the weight size is the sensitivity of intelligent particle characteristic response parameters, and finally the monitoring matching recognition under the feature partition is realized.
[0079] S3, on the basis of judging the feature partition, a time-space correlation model of intelligent particle monitoring information and rut depth is constructed by using gray correlation analysis method;
[0080] In S3, the time-space correlation model of intelligent particle monitoring information and rut depth is established by using gray correlation analysis method: first, the intelligent particle pose information: stress, strain, rotation angle evolution information is selected as the comparison series x 1(k) , x 2(k) , x 3(k) , and the rut deformation depth is selected as the reference series x 0(k) . Secondly, the comparison series is dimensionless according to formula (6), and the gray correlation coefficient of the reference series and the comparison series is calculated according to formula (7). Finally, the correlation degree of each pose information parameter is calculated according to formula (8), and the monitoring information parameter with the largest correlation degree is selected to establish the relationship model with the rut depth.
[0081]
[0082]
[0083]
[0084] In the formula: x i(k) , each comparison series; n, data number; The average of each comparison series; p, resolution coefficient, the smaller the resolution coefficient, the stronger the resolution ability; x 0(k) , reference series.
[0085] S4, determining the actual pavement burying scheme of the intelligent particles, burying the intelligent particles at the bottom of each pavement layer, and respectively at a distance of 1 cm, 2 cm, 3 cm, 4 cm and 5 cm from the center line distance of the pavement wheel track, recording the actual monitoring position evolution information of the intelligent particles under the pavement rut behavior by connecting the computer with the intelligent particles, and recording the center rut depth. On this basis, based on the feature response parameters determined in step 2, the feature partition where the intelligent particles are located is judged, and finally based on the time and space correlation model established in step 3, the rut depth is warned to guide the maintenance decision and determine the rut treatment method. Embodiment 2:
[0086] The computer readable storage medium of the embodiment stores a computer program, which is executed by a processor to implement the steps in the intelligent particle based asphalt pavement structure rut deformation monitoring method of embodiment 1.
[0087] The computer readable storage medium of the embodiment can be an internal storage unit of a terminal, such as a hard disk or a memory of the terminal; the computer readable storage medium of the embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card and the like equipped on the terminal; further, the computer readable storage medium can include both the internal storage unit and the external storage device of the terminal.
[0088] The computer readable storage medium of the embodiment is used to store a computer program and other programs and data required by the terminal, and the computer readable storage medium can also be used to temporarily store data that has been output or will be output.
[0089] Embodiment 3:
[0090] The computer device of the embodiment includes a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the steps in the intelligent particle based asphalt pavement structure rut deformation monitoring method of embodiment 1.
[0091] In the embodiment, the processor can be a central processing unit, and can also be other general-purpose processors, digital signal processors, application specific integrated circuits, ready programmable gate arrays or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., and the general-purpose processor can be a microprocessor or the processor can be any conventional processor, etc.; the memory can include read only memory and random access memory, and provide instructions and data to the processor, and a part of the memory can also include non-volatile random access memory, for example, the memory can also store device type information.
[0092] Those skilled in the art will understand that the content disclosed in the embodiments can be provided as a method, system, or computer program product. Therefore, this solution can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this solution can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage) containing computer-usable program code.
[0093] This solution is described with reference to flowchart illustrations and / or block diagrams of methods and computer program products according to embodiments of this solution. It should 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 device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0094] 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.
[0095] 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.
[0096] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0097] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.
Claims
1. A smart granule based asphalt pavement structure rutting deformation monitoring method, characterized in that, Comprise the following steps: S1, construct the three-dimensional numerical model of intelligent particles, obtain and calibrate the mechanical parameters of intelligent particles at the mesoscale, and verify the effectiveness of intelligent particles for monitoring the behavior of the mixture skeleton; S2, according to the stress-deformation state, build a characteristic partition model of the asphalt layer under the rutting deformation of the asphalt pavement, and use the analytic hierarchy process to obtain the sensitive characteristic response parameters of the intelligent particles, and realize the monitoring matching identification under the characteristic partition; S3, on the basis of judging the characteristic partition, a time-space correlation model of intelligent particle monitoring information and rut depth is established by using the grey correlation analysis method; S4, determine the actual pavement embedding scheme of the intelligent particles, obtain the actual monitoring pose evolution information under the rutting behavior of the pavement, judge the characteristic partition where the intelligent particles are located based on the characteristic response parameters determined in S2, and finally give an early warning to the rut depth based on the time-space correlation model established in S3, to guide the maintenance decision and determine the rut treatment method. In S1, firstly, the intelligent particle three-dimensional virtual model is established in PFC3D software by using linear constitutive model and hexagonal arrangement of particle arrangement, and the mesoscopic contact parameters are converted according to formula (1)-(4); secondly, the macroscopic mechanical parameters, i.e. uniaxial compressive strength C u , elastic modulus E, Poisson's ratio v, uniaxial tensile strength T u , are obtained through the numerical test of intelligent particle uniaxial compression and direct tension; finally, the intelligent particle mesoscopic contact parameters, i.e. particle effective modulus E * , particle stiffness ratio k n / k s , k n , k s , particle friction coefficient μ p , are obtained and calibrated by comparing with the actual mechanical test parameters. E * = 0.5E (1) v = 1 / 4(1 - K s / K n ) (2) where: X b , inter-particle fracture displacement; F S0 , inter-particle shear resistance; d, particle diameter; μ p , inter-particle friction coefficient; μ i , intrinsic friction coefficient; In S1, the gradation skeleton calculation theory is proposed, and the upper and lower limit calculation formula of the skeleton gradation with good embedding and extrusion state is obtained, as shown in formula (5). According to formula (5), three groups of gradations are calculated and designed, and in addition, AC-13 and SMA-13 median gradations are selected, a total of five groups. In the PFC3D software, the aggregate skeletons of the five groups are generated for simulation and simulation, and virtual intelligent particle models are placed in all aggregate skeletons to extract the mesoscopic contact response of intelligent particles under different skeleton embedding and extrusion states, including average coordination number and contact ratio. On this basis, the average coordination number and contact ratio are verified according to the state of each group of skeletons and the mesoscopic contact response of intelligent particles, and the effectiveness of the application of intelligent particles to monitor the behavior of different aggregate skeletons is verified. wherein: D avg D50, weighted average particle size of the two fractions, mm; D50, average particle size of the aggregate retained on the larger and smaller sieves, mm.
2. The method of claim 1, wherein, In S2, first of all, according to the different stress-deformation state, based on the elastic layered system theory, the multi-layer pavement structure under the action of single circular uniform vertical load, the asphalt layer is divided into compression domain: ε m <0, mainly compression, dense flow domain: ε m <0, γ t >0, mainly shear flow, flow loose domain: ε m >0, γ t >0, shear dilatancy flow and heavy, loose domain: ε m >0, mainly shear loose, four deformation areas and the corresponding asphalt pavement rut deformation asphalt layer feature partition model is constructed, the pose evolution of the intelligent particles embedded in the four characteristic deformation areas during the occurrence of rutting behavior is simulated and response analysis is carried out, the measured information of the intelligent particles under the occurrence of rutting deformation behavior is obtained, the sensitive feature response parameters are determined according to the monitoring information, and finally the monitoring matching identification under the feature partition is realized.
3. The method of claim 1, wherein, In S3, the time-space correlation model of intelligent particle monitoring information and rut depth is established by using grey correlation analysis method: firstly, the intelligent particle pose information: stress, strain, rotation angle evolution information are selected as comparison series x 1(k) , x 2(k) , x 3(k) , and the rut deformation depth is selected as reference series x 0(k) ; secondly, the comparison series is dimensionless processed according to formula (6), and the grey correlation coefficient of the reference series and the comparison series is calculated according to formula (7); finally, the correlation degree of each pose information parameter is calculated according to formula (8), and the monitoring information parameter with the largest correlation degree is selected to establish the relationship model with the rut depth; wherein: x i(k) , each comparison series; n, number of data points; mean of each comparison series; p, resolution coefficient, the smaller p is, the stronger the resolution is; x 0(k) — reference series.
4. The method of claim 1, wherein, In S4, the intelligent particles are embedded at the bottom of each pavement layer, and the distance from the center line of the pavement wheel track is 1cm, 2cm, 3cm, 4cm and 5cm respectively. The actual monitoring pose evolution information of the intelligent particles under the rutting behavior of the pavement is recorded by connecting the computer with the intelligent particles, and the center rut depth is recorded. On this basis, the characteristic partition where the intelligent particles are located is judged based on the measured pose information of the intelligent particles and the characteristic response parameters determined in S2, and finally the early warning of the rut depth is given based on the time-space correlation model established in S3, to guide the maintenance decision and determine the rut treatment method.
5. A computer readable storage medium having stored thereon a computer program, characterized in that: The program is executed by the processor to realize the steps in the intelligent particle-based asphalt pavement structure rutting deformation monitoring method of any one of claims 1-4.
6. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps in the intelligent particle-based asphalt pavement structure rutting deformation monitoring method of any one of claims 1-4.