Methods and devices for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction

Abstract

This application discloses a method and apparatus for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, relating to the technical field of predicting the dynamic migration of coarse aggregates in asphalt mixtures for road engineering. The method includes: collecting the morphological characteristics of real coarse aggregates to generate a 3D aggregate model containing complex morphological features; establishing a rotary compaction model of asphalt mixtures based on the 3D aggregate model, and selecting multiple typical compaction states to represent the compaction process; determining the coordination number information of each coarse aggregate under each typical compaction state; based on the coordination number information, statistically analyzing the coordination number distribution of coarse aggregates in the initial compaction state, calculating the transition probability of aggregate coordination numbers between adjacent typical compaction states, constructing a multi-step transition matrix, and establishing a prediction model for the dynamic migration of coarse aggregate coordination numbers based on a Markov chain based on the multi-step transition matrix. This predicts the evaluation index of the skeleton structure during the compaction process, which can more accurately and reasonably describe the dynamic migration of coarse aggregates during compaction.

Description

Technical Field

[0001] This application relates to the field of dynamic migration prediction technology for coarse aggregates in asphalt mixtures for road engineering, and in particular to a method and device for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures. Background Technology

[0002] Compaction of asphalt mixtures is a crucial process in road construction. The quality of compaction directly determines the mechanical and road performance of the mixture, ultimately affecting the durability and driving comfort of the asphalt pavement.

[0003] The strength of asphalt pavement originates from the skeleton structure formed by aggregates. Current standards for compaction indices fail to reflect the evolution of this skeleton structure during the asphalt pavement compaction process. From a microscopic perspective, compaction is essentially a process of dynamic aggregate migration. Coordination number refers to the amount of aggregate in contact with a given aggregate particle. Changes in coordination number not only reflect the density of the asphalt mixture but also describe the strength of the skeleton structure.

[0004] Currently, research on aggregate migration in asphalt mixtures mainly utilizes discrete element method (DEM) software, 3D reconstruction techniques based on image processing technology, and 2D image processing techniques based on [other technologies]. However, these methods lack an accurate and reasonable mathematical model to describe the dynamic migration of coarse aggregates during compaction. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, which can more accurately and reasonably describe the dynamic migration of coarse aggregates during the compaction process.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a method for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, comprising the following steps:

[0008] Collect the morphological features of real coarse aggregates and generate 3D aggregate models containing complex morphological characteristics;

[0009] A rotary compaction model for asphalt mixtures was established based on the 3D aggregate model, and several typical compaction states were selected to represent the compaction process.

[0010] Determine the coordination number information of each coarse aggregate under each of the typical compaction states;

[0011] Based on the coordination number information, the coordination number distribution of coarse aggregates in the initial compaction state is statistically analyzed, the transition probability of aggregate coordination number between adjacent typical compaction states is calculated, a multi-step transition matrix is ​​constructed, and a prediction model for the dynamic migration of coarse aggregate coordination number based on Markov chain is established based on the multi-step transition matrix to predict the evaluation index of the skeleton structure during the compaction process; the evaluation index of the skeleton structure during the compaction process is the predicted value of the aggregate coordination number distribution of each typical compaction state during the compaction process.

[0012] Optionally, the method for predicting the dynamic migration of coarse aggregates during the compaction of asphalt mixtures further includes: verifying the reliability of the prediction model for the dynamic migration of the coordination number of coarse aggregates.

[0013] Optionally, the morphological features of real coarse aggregates are collected to generate a 3D aggregate model containing complex morphological characteristics, specifically including:

[0014] A 3D blue light scanner was used to scan real coarse aggregates, and the morphological features of the real coarse aggregates were collected to obtain several two-dimensional images.

[0015] All two-dimensional images are stitched together to generate a 3D aggregate model containing complex morphological characteristics; these complex morphological characteristics include different shapes, angularity, and surface texture.

[0016] Optionally, an asphalt mixture rotary compaction model is established based on the 3D aggregate model, and several typical compaction states are selected to represent the compaction process, specifically including:

[0017] Import the 3D aggregate model into discrete element software to generate numerical aggregates;

[0018] Based on the gradation of the target asphalt mixture, the amount of aggregate in each particle size is calculated using the equivalent spherical volume method.

[0019] Based on the Monte Carlo random algorithm, a rotary compaction model of asphalt mixture is generated by calling the corresponding number of numerical aggregates of each particle size.

[0020] Based on the rotary compaction model of asphalt mixture, multiple typical compaction states are selected at equal intervals to represent the compaction process.

[0021] Optionally, the formula for calculating the quantity of aggregates in each particle size fraction is as follows:

[0022] ;

[0023] ;

[0024] ;

[0025] In the formula, It is a particle size The equivalent sphere radius, It is a particle size Size limit, It is a particle size The lower limit of the size; It is a particle size The equivalent sphere volume; Pi; It is a particle size The amount of aggregate. It is a particle size The quality of aggregates, It is a particle size The aggregate density.

[0026] Optionally, determining the coordination number information of each coarse aggregate under each of the typical compaction states specifically includes:

[0027] The coordination number information of each coarse aggregate under each typical compaction state is exported using the built-in fish language of the discrete element method software; the coordination number information includes the number of each coarse aggregate and its corresponding coordination number.

[0028] Optionally, the formula for calculating the transition probability of aggregate coordination number between adjacent typical compaction states is as follows:

[0029] ;

[0030] Where, in the formula The coordination number in the current typical compaction state is The aggregate is transferred to the next typical compaction state with a coordination number of . The probability, The coordination number in the current typical compaction state is The aggregate is transferred to the next typical compaction state with a coordination number of . Quantity, The coordination number in the current typical compaction state is The quantity of aggregate.

[0031] Optionally, typical compaction states are denoted as State 1, State 2, State 3, State 4, and State 5, respectively.

[0032] Optionally, the predictive model for the dynamic migration of the coordination number of coarse aggregates is expressed as:

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] in, The coordination number distribution of coarse aggregates in state 1; , , , These are the predicted values ​​of aggregate coordination number distribution for states 2, 3, 4, and 5, respectively. The transition matrix for the coordination number from state 1 to state 2; This is the transition matrix for the coordination number from state 2 to state 3; This is the transition matrix for the coordination number from state 3 to state 4; This is the transition matrix for the coordination number from state 4 to state 5.

[0038] Secondly, this application provides a device for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, comprising the following modules:

[0039] A 3D blue light scanner is used to collect the morphological features of real coarse aggregates and generate 3D aggregate models containing complex morphological characteristics.

[0040] The compaction module establishment and typical compaction state selection module is used to establish an asphalt mixture rotary compaction model based on the 3D aggregate model and select multiple typical compaction states to represent the compaction process.

[0041] The coordination number information determination module is used to determine the coordination number information of each coarse aggregate under each of the typical compaction states.

[0042] The coarse aggregate coordination number dynamic migration prediction module is used to statistically analyze the coarse aggregate coordination number distribution in the initial compaction state based on the coordination number information, calculate the transition probability of aggregate coordination number between adjacent typical compaction states, construct a multi-step transition matrix, and establish a prediction model for the dynamic migration of coarse aggregate coordination number based on the multi-step transition matrix, thereby predicting the evaluation index of the skeleton structure during the compaction process. The evaluation index of the skeleton structure during the compaction process is the predicted value of the aggregate coordination number distribution in each typical compaction state during the compaction process.

[0043] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0044] Previous studies on aggregate migration in asphalt mixtures have largely focused on qualitative analysis of single specimens, neglecting the randomness and time-varying nature of aggregate migration and lacking accurate and reasonable mathematical models to describe the dynamic migration of coarse aggregates during compaction. This application provides a method and apparatus for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction. By introducing a Markov chain model into the study of coarse aggregate migration patterns during asphalt mixture compaction, it not only reflects the randomness of coarse aggregate dynamic migration during compaction but also scientifically and reasonably simplifies the complex compaction process. This makes the established prediction model for the dynamic migration of coarse aggregate coordination numbers more consistent with reality and more accurately and reasonably describes the dynamic migration of coarse aggregates during compaction. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an application environment diagram of a method for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, as described in one embodiment of this application.

[0047] Figure 2 This is a flowchart illustrating a method for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, provided as an embodiment of this application.

[0048] Figure 3 This is a schematic diagram illustrating the specific process of a method for predicting the dynamic migration of coarse aggregates during the compaction of asphalt mixtures, provided in an embodiment of this application.

[0049] Figure 4 This is a schematic diagram comparing the measured and predicted values ​​of the aggregate coordination number in state 2 provided in an embodiment of this application.

[0050] Figure 5 This is a schematic diagram comparing the measured and predicted values ​​of the aggregate coordination number in state 3 provided in an embodiment of this application.

[0051] Figure 6 This is a schematic diagram comparing the measured and predicted values ​​of the aggregate coordination number in state 4 provided in an embodiment of this application.

[0052] Figure 7 This is a schematic diagram comparing the measured and predicted values ​​of the aggregate coordination number in state 5 provided in an embodiment of this application.

[0053] Figure 8 This is a schematic diagram of a device for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, provided in one embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send requests to be processed to server 104. After receiving the requests, server 104 collects the morphological characteristics of real coarse aggregates, generates a 3D aggregate model containing complex morphological characteristics, establishes an asphalt mixture rotary compaction model based on the 3D aggregate model, selects multiple typical compaction states to represent the compaction process, determines the coordination number information of each coarse aggregate under each typical compaction state, statistically analyzes the coordination number distribution of coarse aggregates in the initial compaction state based on the coordination number information, calculates the transition probability of aggregate coordination numbers between adjacent typical compaction states, constructs a multi-step transition matrix, and establishes a prediction model for the dynamic migration of coarse aggregate coordination numbers based on a Markov chain based on the multi-step transition matrix, predicting the evaluation index of the skeleton structure during the compaction process. Server 104 can feed back the obtained evaluation index of the skeleton structure during the compaction process to terminal 102. In addition, in some embodiments, the method for predicting the dynamic migration of coarse aggregate during the compaction process of asphalt mixture can also be implemented by server 104 or terminal 102 alone. For example, terminal 102 can directly perform dynamic migration prediction of coarse aggregate for the request to be processed, or server 104 can obtain the request to be processed from the data storage system and perform dynamic migration prediction of coarse aggregate for the request to be processed.

[0057] The terminal 102 can be, but is not limited to, various desktop computers and laptops. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0058] In one exemplary embodiment, such as Figure 2 As shown, a method for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204.

[0059] Step 201: Collect the morphological features of real coarse aggregates and generate a 3D aggregate model containing complex morphological characteristics.

[0060] Step 202: Based on the 3D aggregate model, establish an asphalt mixture rotary compaction model and select multiple typical compaction states to represent the compaction process.

[0061] Step 203: Determine the coordination number information of each coarse aggregate under each of the typical compaction states.

[0062] Step 204: Based on the coordination number information, statistically analyze the coordination number distribution of coarse aggregates in the initial compaction state, calculate the transition probability of aggregate coordination number between adjacent typical compaction states, construct a multi-step transition matrix, and establish a prediction model for the dynamic migration of coarse aggregate coordination number based on Markov chain based on the multi-step transition matrix to predict the evaluation index of the skeleton structure during compaction; the evaluation index of the skeleton structure during compaction is the predicted value of the aggregate coordination number distribution of each typical compaction state during compaction.

[0063] By implementing steps 201 to 204 above, and introducing the Markov chain model into the study of coarse aggregate migration law during asphalt mixture compaction, not only can the randomness of the dynamic migration of coarse aggregate during asphalt mixture compaction be reflected, but the complex compaction process can also be simplified scientifically and reasonably. This makes the established prediction model for the dynamic migration of coarse aggregate coordination number more consistent with the actual situation and can more accurately and reasonably describe the dynamic migration of coarse aggregate during compaction.

[0064] like Figure 3As shown, the method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction provided in this application may further include the following steps: using a 3D blue light scanner to collect the morphological characteristics of the aggregates and generating a 3D aggregate model containing the morphological characteristics; establishing an asphalt mixture rotary compaction model considering the actual aggregate morphology based on PFC 3D; selecting five typical compaction states; exporting the code of each coarse aggregate and its corresponding coordination number information through the fish language built into PFC 3D; calculating the transition probability of the coordination number of aggregates between states, obtaining a four-step transition matrix, establishing a prediction model for the dynamic migration of coarse aggregates during asphalt mixture compaction and an evaluation index for the skeleton structure; and verifying the reliability of the established prediction model for the dynamic migration of coarse aggregates.

[0065] In step 201 above, the morphological features of real coarse aggregate are collected to generate a 3D aggregate model containing complex morphological characteristics. Specifically, this includes: scanning the real coarse aggregate using a 3D blue light scanner to collect the morphological features of the real coarse aggregate and obtaining several two-dimensional images; stitching together all the two-dimensional images to generate a 3D aggregate model containing complex morphological characteristics; the complex morphological characteristics include different shapes, angularity, and surface texture.

[0066] The morphological features of the aggregates are collected by a 3D blue light scanner, a 3D aggregate model containing complex morphological characteristics is generated, and an STL file is exported for discrete element modeling.

[0067] Complex morphological characteristics refer to different shapes, angularities, and surface textures. In this embodiment, more than 3,000 representative aggregates with different morphological features were selected from 7 material yards in Province X for 3D blue light scanning.

[0068] The 3D blue light scanner, manufactured by GOM GmbH in Germany, utilizes the ATOS Core 135 3D blue light scanning system to scan the morphological characteristics of real coarse aggregates. The scanner comprises a scanning system and a data processing system, enabling rapid acquisition of 3D morphological data. It provides a 0.03 mm resolution 3D mesh for the aggregates, and triangular elements are used as the basic unit for scanning all coarse aggregate 3D meshes to improve the accuracy of aggregate reconstruction. The operating procedures include: calibrating the 3D blue light scanner under different environmental conditions to ensure the accuracy of the scanned images; before acquisition, controlling the distance between the aggregate and the scanner lens to approximately 0.3 m, and adjusting the aperture size according to the indoor lighting intensity; when acquiring aggregate morphological features, 10 aggregates are placed on the fixture at a time, and a 2D image of the aggregate is captured every 30° rotation, and the 2D images are stitched together to form an STL file corresponding to the 3D aggregate model.

[0069] In step 202 above, an asphalt mixture rotary compaction model is established based on the 3D aggregate model, and multiple typical compaction states are selected to represent the compaction process, specifically including the following steps 301 to 304.

[0070] Step 301: Import the 3D aggregate model into the discrete element method software to generate numerical aggregates.

[0071] The discrete element method (DEM) software used is PFC 3D. The STL file corresponding to the generated 3D aggregate model is imported into PFC3D software, and the built-in "rblock template create" command is used to generate numerical aggregates for building a rotary compaction model of asphalt mixture that considers the actual aggregate morphology.

[0072] Step 302: Based on the gradation of the target asphalt mixture, calculate the amount of aggregate for each particle size using the equivalent spherical volume method.

[0073] The aggregate quantity of each particle size class in the numerical model of asphalt mixture gradation is calculated using the equivalent spherical volume method. The formula for calculating the aggregate quantity of each particle size class is as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] In the formula, It is a particle size The equivalent sphere radius, It is a particle size Size limit, It is a particle size The lower limit of the size; It is a particle size The equivalent sphere volume; Pi; It is a particle size The amount of aggregate. It is a particle size The quality of aggregates, It is a particle size The aggregate density.

[0078] In this embodiment, the asphalt mixture refers to a PAC-13 graded asphalt mixture with a diameter of 100 mm and a height of 100 mm. At the same time, fine aggregates below 2.36 mm, mineral powder, and asphalt binder are equivalent to spherical units with a diameter of 1 mm. The PAC-13 gradation and the amount of aggregates of each size are shown in Table 1.

[0079] Table 1. PAC-13 gradation and aggregate quantity of each size class

[0080]

[0081] Step 303: Based on the Monte Carlo random algorithm, call the corresponding number of numerical aggregates of each size to generate an asphalt mixture rotary compaction model.

[0082] A numerical model for rotary compaction of asphalt mixtures is generated by randomly selecting the corresponding aggregate quantity from the numerical aggregate dataset using the Monte Carlo random algorithm. The rotary compaction model involves first moving the vertex coordinates of the numerical model to the left until it forms a 1.16° angle with the vertical direction, then applying a rotation of 30 r / min to the vertex while simultaneously applying a pressure of 600 kPa to the loading plate at the top of the numerical model. The number of rotary compaction cycles is 75.

[0083] Step 304: Based on the asphalt mixture rotary compaction model, select multiple typical compaction states at equal intervals to represent the compaction process.

[0084] Based on the generated rotary compaction model of asphalt mixture (rotary compaction numerical model), typical compaction states are selected at equal intervals to represent the entire compaction process. This embodiment uses five typical compaction states, denoted as State 1, State 2, State 3, State 4, and State 5. Typical compaction states refer to: 5 rotary compactions for State 1, 25 for State 2, 45 for State 3, 65 for State 4, and 75 for State 5.

[0085] In step 203 above, determining the coordination number information of each coarse aggregate under each typical compaction state specifically includes: exporting the coordination number information of each coarse aggregate under each typical compaction state using the fish language built into the discrete element software; the coordination number information includes the number of each coarse aggregate and its corresponding coordination number.

[0086] Using the built-in "loop foreach" command in PFC3D, the coordination number information of five typical compacted coarse aggregates is output. Coarse aggregates refer to aggregates with a particle size greater than 2.36mm-16mm. The coordination number information includes the number of each coarse aggregate and its corresponding coordination number.

[0087] Import the output coarse aggregate coordination number information into Excel software. The coarse aggregate numbers are divided into 11 columns according to the coordination number distribution from 0 to 10. It should be noted that the coordination number information of two adjacent states of coarse aggregate should be placed in the same sheet.

[0088] Step 204 above includes steps 401 to 403.

[0089] Step 401: Statistically determine the coordination number distribution of coarse aggregate in the initial state, i.e., the initial state probability vector of the coordination number distribution of coarse aggregate in state 1. , ,in This represents the distribution of the coordination number of aggregates in state 1. These represent the quantities of aggregates corresponding to coordination numbers 0-10 in state 1.

[0090] In this embodiment, the initial distribution of the coordination number of coarse aggregate is as follows: .

[0091] Step 402: Calculate the transition probability of aggregate coordination number between adjacent states to obtain a multi-step transition matrix. In this embodiment, five typical compaction states are included, thus a four-step transition matrix is ​​obtained. The specific process is as follows: For any transition matrix They are all 11×11 square matrices.

[0092] ;

[0093] In the formula It is any transition matrix. The probabilities are 1, 2, 3, and 4; the sum of the probabilities in any row of this transition matrix is ​​equal to 1.

[0094] Step 402 above includes steps 4021 to 4023.

[0095] Step 4021: Select the coordination number in state 1. By selecting the aggregate number and all aggregate numbers in state 2, and using the "Find Duplicate Values" function, the coordination number can be obtained. The aggregate transitions from state 1 to state 2 with a coordination number of The transition probability. The formula for calculating the transition probability of aggregate coordination number between adjacent typical compaction states is as follows:

[0096] ;

[0097] Where, in the formula The coordination number in the current typical compaction state is The aggregate is transferred to the next typical compaction state with a coordination number of . The probability, , The coordination number in the current typical compaction state is The aggregate is transferred to the next typical compaction state with a coordination number of . Quantity, The coordination number in the current typical compaction state is The quantity of aggregate. , The value range is 0-10.

[0098] Step 4022: Repeat the above operation to obtain the transition probabilities of aggregates with coordination numbers from 0 to 10 from state 1 to state 2, thus obtaining the one-step transition matrix. That is, the transition matrix of the coordination number from state 1 to state 2. In this embodiment:

[0099] .

[0100] Step 4023: Repeat the above operation to obtain the transition matrix of the coordination number from state 2 to state 3. The transition matrix of coordination number from state 3 to state 4 The transition matrix of coordination number from state 4 to state 5 In this embodiment, the above transition matrix is ​​represented as:

[0101] ;

[0102] ;

[0103] .

[0104] Step 403: Establish a predictive model for the dynamic migration of coarse aggregate coordination numbers in asphalt mixtures and an evaluation index for the skeleton structure during compaction. The predictive model for the dynamic migration of coarse aggregate coordination numbers refers to a Markov chain model, where the probability of the current state depends only on the previous state and is independent of the probabilities prior to the previous state. The predictive model for the dynamic migration of coarse aggregate coordination numbers is expressed as follows:

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] in, The coordination number distribution of coarse aggregates in state 1; , , , These are the predicted values ​​of aggregate coordination number distribution for states 2, 3, 4, and 5, respectively. The transition matrix for the coordination number from state 1 to state 2; This is the transition matrix for the coordination number from state 2 to state 3; This is the transition matrix for the coordination number from state 3 to state 4; Let be the transition matrix for the coordination number from state 4 to state 5.

[0110] The evaluation index of the skeleton structure in the compaction process refers to , , , , In this embodiment, the predicted aggregate coordination number distribution values ​​for each state are as follows:

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] .

[0116] The method for predicting the dynamic migration of coarse aggregates during the compaction of asphalt mixtures further includes: verifying the reliability of the prediction model for the dynamic migration of the coordination number of coarse aggregates, including the following steps 501 to 503.

[0117] Step 501: Generate parallel specimens of PAC-13 asphalt mixture based on discrete element method (DEM). Parallel specimens refer to specimens that use the same aggregate morphology database, the same gradation, and the same aggregate quantity as the specimens used to build the model, but with different aggregate spatial locations. This is achieved using the "model random" command. In this embodiment, the parallel specimen is named PAC-13-1. The initial spatial distribution is randomly generated differently, but all material properties and gradation parameters are exactly the same.

[0118] Step 502: Statistically analyze the distribution of aggregate coordination number in state 1 State 1 refers to the specimen after 5 rotational compaction cycles. In this embodiment, State 1 is the PAC-13-1 specimen after 5 rotational compaction cycles, represented as:

[0119] .

[0120] Step 503: Based on the established prediction model (Markov chain model) for the dynamic migration of coarse aggregate coordination number, predict the aggregate coordination number distribution values ​​for states 2, 3, 4, and 5. , , , Error analysis was performed on the aggregate coordination number distributions in states 2, 3, 4, and 5. , , , These refer to the evaluation indicators of the skeleton structure of specimens after 25, 45, 65, and 75 rotational compaction cycles, respectively. Error analysis between the predicted and measured values ​​of aggregate coordination number distribution is evaluated using two indicators: correlation coefficient and root mean square error. In this embodiment, the measured and predicted values ​​of aggregate coordination number in states 2, 3, 4, and 5 of PAC-13-1 are compared as follows: Figures 4-7 As shown.

[0121] This application first uses a 3D blue light scanner to collect the morphological characteristics of aggregates, generating a 3D aggregate model containing these characteristics, and exporting an STL file for discrete element modeling. Based on PFC 3D, a rotary compaction model of asphalt mixture considering the actual aggregate morphology is established. Typical compaction states are selected to represent the compaction process. The code for each coarse aggregate and its corresponding coordination number information are exported using the built-in fish language of PFC 3D. The coordination number distribution of aggregates in the initial state is statistically analyzed, and the transition probability of the coordination number between states is calculated to obtain a four-step transition matrix. A prediction model for the dynamic migration of coarse aggregates in asphalt mixtures and an evaluation index for the skeleton structure are established. Finally, the reliability of the established model is verified. This application applies the Markov chain model to the dynamic evaluation of coarse aggregate migration during asphalt mixture compaction, establishing a prediction model for the dynamic migration of coarse aggregates in asphalt mixtures. This model can quantitatively evaluate the skeleton structure during compaction and reveal the evolution law of the skeleton structure during compaction. Furthermore, the forming and densification mechanism of the asphalt mixture compaction process is deconstructed, providing a theoretical basis for the construction design of asphalt mixtures.

[0122] This application has the following beneficial effects:

[0123] (1) This application uses a three-dimensional blue light scanner to collect the morphological features of aggregates and generate a 3D aggregate model containing complex morphological characteristics. It can accurately obtain the surface morphological features of aggregates and provide technical support for establishing a discrete element rotational compaction model that considers the morphology of complex aggregates.

[0124] (2) This application converts the mass of each particle size in the asphalt mixture gradation into quantity by using the equivalent spherical volume method, which improves the accuracy of the discrete element rotational compaction model that considers the complex aggregate morphology and creates a new paradigm of high-value and refined discrete element modeling.

[0125] (3) In this application, fine aggregates, mineral powder and asphalt binder with a diameter of less than 2.36 mm are equivalent to spherical elements with a diameter of 1 mm, which greatly improves the computational efficiency of the discrete element rotational compaction model that considers complex aggregate morphology.

[0126] (4) This application introduces the Markov chain model into the study of the migration law of coarse aggregates during the compaction process of asphalt mixtures. This not only reflects the randomness of the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, but also simplifies the complex compaction process in a scientific and reasonable way. This makes the established prediction model more consistent with the actual situation.

[0127] (5) Based on the Markov chain model, this application establishes a quantitative evaluation index of the skeleton structure during the compaction process of asphalt mixture, which can accurately reflect the skeleton structure of asphalt mixture and provide theoretical guidance for further revealing the compaction mechanism of asphalt mixture.

[0128] This application also provides an application scenario in which the above-mentioned method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction is applied. Specifically, the method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction provided in this embodiment can be applied to a road construction scenario. The road construction scenario includes a request issuance stage, a coarse aggregate dynamic migration prediction link, and a construction stage. The request to be processed enters the coarse aggregate dynamic migration prediction link from the request issuance stage, obtains the evaluation index of the skeleton structure during compaction, and then enters the downstream construction stage. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction provided in this embodiment belongs to the coarse aggregate dynamic migration prediction link. Specifically, in the process of predicting the dynamic migration of coarse aggregates for requests to be processed, the morphological characteristics of real coarse aggregates can be collected to generate a 3D aggregate model containing complex morphological characteristics. Based on the 3D aggregate model, an asphalt mixture rotary compaction model can be established, and multiple typical compaction states can be selected to represent the compaction process. The coordination number information of each coarse aggregate under each typical compaction state can be determined. Based on the coordination number information, the coordination number distribution of coarse aggregates in the initial compaction state can be statistically analyzed. The transition probability of aggregate coordination number between adjacent typical compaction states can be calculated, a multi-step transition matrix can be constructed, and a prediction model of coarse aggregate coordination number dynamic migration based on Markov chain can be established based on the multi-step transition matrix to predict the evaluation index of skeleton structure during the compaction process.

[0129] Based on the same inventive concept, this application also provides an asphalt mixture dynamic migration prediction device for implementing the above-mentioned method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the asphalt mixture dynamic migration prediction device provided below can be found in the limitations of the asphalt mixture dynamic migration prediction method described above, and will not be repeated here.

[0130] In one exemplary embodiment, such as Figure 8As shown, a device for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures is provided, including a three-dimensional blue light scanner, a compaction module establishment and typical compaction state selection module, a coordination number information determination module, and a coarse aggregate coordination number dynamic migration prediction module.

[0131] Among them, the 3D blue light scanner is used to collect the morphological features of real coarse aggregates and generate 3D aggregate models containing complex morphological characteristics.

[0132] The compaction module establishment and typical compaction state selection module is used to establish an asphalt mixture rotary compaction model based on the 3D aggregate model and select multiple typical compaction states to represent the compaction process.

[0133] The coordination number information determination module is used to determine the coordination number information of each coarse aggregate under each of the typical compaction states.

[0134] The coarse aggregate coordination number dynamic migration prediction module is used to statistically analyze the coarse aggregate coordination number distribution in the initial compaction state based on the coordination number information, calculate the transition probability of aggregate coordination number between adjacent typical compaction states, construct a multi-step transition matrix, and establish a prediction model for the dynamic migration of coarse aggregate coordination number based on the multi-step transition matrix, thereby predicting the evaluation index of the skeleton structure during the compaction process. The evaluation index of the skeleton structure during the compaction process is the predicted value of the aggregate coordination number distribution in each typical compaction state during the compaction process.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the dynamic migration of coarse aggregates during the compaction of asphalt mixtures, characterized in that, The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction includes: Collect the morphological features of real coarse aggregates and generate 3D aggregate models containing complex morphological characteristics; A rotary compaction model for asphalt mixtures was established based on the 3D aggregate model, and several typical compaction states were selected to represent the compaction process. Determine the coordination number information of each coarse aggregate under each of the typical compaction states; Based on the coordination number information, the coordination number distribution of coarse aggregates in the initial compaction state is statistically analyzed, the transition probability of aggregate coordination number between adjacent typical compaction states is calculated, a multi-step transition matrix is ​​constructed, and a prediction model for the dynamic migration of coarse aggregate coordination number based on Markov chain is established based on the multi-step transition matrix to predict the evaluation index of the skeleton structure during the compaction process; the evaluation index of the skeleton structure during the compaction process is the predicted value of the aggregate coordination number distribution of each typical compaction state during the compaction process.

2. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 1, characterized in that, The method for predicting the dynamic migration of coarse aggregates during the compaction of asphalt mixtures also includes: verifying the reliability of the prediction model for the dynamic migration of the coordination number of coarse aggregates.

3. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 1, characterized in that, The morphological features of real coarse aggregates are collected to generate 3D aggregate models containing complex morphological characteristics, specifically including: A 3D blue light scanner was used to scan real coarse aggregates, and the morphological features of the real coarse aggregates were collected to obtain several two-dimensional images. All two-dimensional images are stitched together to generate a 3D aggregate model containing complex morphological characteristics; these complex morphological characteristics include different shapes, angularity, and surface texture.

4. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 1, characterized in that, A rotary compaction model for asphalt mixtures was established based on the 3D aggregate model, and several typical compaction states were selected to represent the compaction process, specifically including: The 3D aggregate model is imported into discrete element software to generate numerical aggregates. Based on the gradation of the target asphalt mixture, the quantity of aggregates of each particle size is calculated using the equivalent spherical volume method. Based on the Monte Carlo random algorithm, a rotary compaction model of asphalt mixture is generated by calling the corresponding number of numerical aggregates of each particle size. Based on the rotary compaction model of asphalt mixture, multiple typical compaction states are selected at equal intervals to represent the compaction process.

5. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 4, characterized in that, The formula for calculating the quantity of aggregates in each particle size fraction is as follows: ； ； ； In the formula, It is a particle size The equivalent sphere radius, It is a particle size Size limit, It is a particle size The lower limit of the size; It is a particle size The equivalent sphere volume; Pi; It is a particle size The amount of aggregate. It is a particle size The quality of aggregates, The sieve particle size is The aggregate density.

6. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 1, characterized in that, Determining the coordination number information of each coarse aggregate under each of the aforementioned typical compaction states specifically includes: The coordination number information of each coarse aggregate under each typical compaction state is exported using the built-in fish language of the discrete element method software; the coordination number information includes the number of each coarse aggregate and its corresponding coordination number.

7. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 1, characterized in that, The formula for calculating the transition probability of aggregate coordination number between adjacent typical compaction states is as follows: ； Where, in the formula The coordination number in the current typical compaction state is The aggregate is transferred to the next typical compaction state with a coordination number of . The probability, The coordination number in the current typical compaction state is The aggregate is transferred to the next typical compaction state with a coordination number of . Quantity, The coordination number in the current typical compaction state is The quantity of aggregate.

8. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 1, characterized in that, Typical compaction states are denoted as State 1, State 2, State 3, State 4, and State 5, respectively.

9. The method for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction according to claim 8, characterized in that, The predictive model for the dynamic migration of coordination number in coarse aggregates is expressed as follows: ； ； ； ； in, The coordination number distribution of coarse aggregates in state 1; , , , These are the predicted values ​​of aggregate coordination number distribution for states 2, 3, 4, and 5, respectively. The transition matrix for the coordination number from state 1 to state 2; The transition matrix for the coordination number from state 2 to state 3; This is the transition matrix for the coordination number from state 3 to state 4; This is the transition matrix for the coordination number from state 4 to state 5.

10. A device for predicting the dynamic migration of coarse aggregates during the compaction process of asphalt mixtures, characterized in that, The device for predicting the dynamic migration of coarse aggregates during asphalt mixture compaction includes: A 3D blue light scanner is used to collect the morphological features of real coarse aggregates and generate 3D aggregate models containing complex morphological characteristics. The compaction module establishment and typical compaction state selection module is used to establish an asphalt mixture rotary compaction model based on the 3D aggregate model and select multiple typical compaction states to represent the compaction process; The coordination number information determination module is used to determine the coordination number information of each coarse aggregate under each of the typical compaction states. The coarse aggregate coordination number dynamic migration prediction module is used to statistically analyze the coarse aggregate coordination number distribution in the initial compaction state based on the coordination number information, calculate the transition probability of aggregate coordination number between adjacent typical compaction states, construct a multi-step transition matrix, and establish a prediction model for the dynamic migration of coarse aggregate coordination number based on the multi-step transition matrix, thereby predicting the evaluation index of the skeleton structure during the compaction process. The evaluation index of the skeleton structure during the compaction process is the predicted value of the aggregate coordination number distribution in each typical compaction state during the compaction process.

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