Discrete element stochastic aggregate modeling method and system for steel slag asphalt mixture under freeze-thaw cycle conditions

Abstract

The present invention discloses a discrete element random aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions, including: obtaining the sphericity value, angularity index and roughness of steel slag coarse aggregate under freeze-thaw cycle, obtaining the numerical template of steel slag coarse aggregate under freeze-thaw cycle, performing random placement processing on the numerical template to obtain the random aggregate model of steel slag asphalt mixture, performing microscopic pore random placement processing on the random aggregate model of steel slag asphalt mixture to obtain the random aggregate model of steel slag asphalt mixture under freeze-thaw cycle, and performing ice crystal processing on the asphalt mortar ball units randomly deleted during the random placement processing to obtain the random aggregate model of steel slag asphalt mixture. The present invention realizes the simulation of freeze-thaw cycle conditions in the simulation, and can solve the technical problem that the authenticity of the force chain cannot be guaranteed because the aggregate particle model generated by the existing random generation algorithm cannot fully reflect the surface characteristics of the real aggregate particles.

Description

Technical Field

[0001] The present invention belongs to the technical field of numerical simulation, and more specifically, relates to a discrete element random aggregate modeling method and system for steel slag asphalt mixture under freeze-thaw cycle conditions. Background Art

[0002] Discrete Element Method (DEM) is a physical mechanics model established by the block contact constitutive relationship of discrete units, and is simulated according to Newton's second law, and is applied to the study of heterogeneous materials. The discrete element model can reflect the stress state inside the steel slag asphalt mixture and provide a basis for the study of its internal force chain, but there is no effective means to construct a random aggregate model of steel slag asphalt mixture under freeze-thaw cycles, and there are still problems with accuracy and efficiency.

[0003] There are two main methods for constructing discrete element models of asphalt mixtures. The first method is to use a random generation algorithm. This method usually uses multi-faceted generation technology to simulate aggregate particles and randomly arranges them to form a skeleton structure. Its advantages are high modeling efficiency and the ability to accurately control the gradation composition of the model. The second method is to establish an in-situ discrete element model of asphalt mixtures based on industrial computed tomography (CT) and digital imaging technology. This method can better reproduce the real microstructure of asphalt mixtures to a certain extent.

[0004] However, both of the above two methods for constructing discrete element models of asphalt mixtures have some defects that cannot be ignored: First, the aggregate particle model generated by the random generation algorithm cannot fully reflect the surface characteristics of the real aggregate particles and has significant differences from the actual aggregate, so the authenticity of the force chain cannot be guaranteed; Second, due to the errors that may occur in the image acquisition, processing and segmentation process when establishing an in-situ discrete element model of asphalt mixture based on industrial CT and digital imaging technology, there may be a certain deviation between the constructed digital specimen and the actual structure, and the treatment of the position of the aggregate-mortar interface and the interface between aggregates is still unclear, so the accuracy of the force chain results is not ideal; Third, the above two methods for constructing discrete element models of asphalt mixtures are both modeling considering environmental conditions, so they cannot truly reflect the damage caused to steel slag asphalt mixtures by freeze-thaw cycles and other effects. Summary of the invention

[0005] In response to the above defects or improvement needs of the prior art, the present invention provides a discrete element random aggregate modeling method and system for steel slag asphalt mixture under freeze-thaw cycle conditions, the purpose of which is to solve the technical problem that the aggregate particle model generated by the existing random generation algorithm cannot fully reflect the surface characteristics of the real aggregate particles and has significant differences from the actual aggregate, so that the authenticity of the force chain cannot be guaranteed, and the existing in-situ asphalt mixture discrete element model established based on industrial CT and digital image technology may have certain deviations between the constructed digital specimen and the real structure due to errors that may occur during image acquisition, processing and segmentation, and the treatment of the position of the aggregate-mortar interface and the interface between aggregates is still unclear, resulting in poor accuracy of the force chain results. The two existing methods for constructing asphalt mixture discrete element models are both modeling based on environmental conditions, and therefore cannot truly reflect the damage caused to steel slag asphalt mixture by freeze-thaw cycles and other effects.

[0006] To achieve the above object, according to one aspect of the present invention, a discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions is provided, comprising the following steps:

[0007] (1) Obtaining the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles a , angularity index G A and roughness S a ;

[0008] (2) The sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1) a , angularity index G A and roughness S a , obtain the numerical template of steel slag coarse aggregate under freeze-thaw cycles;

[0009] (3) performing random placement processing on the numerical template of steel slag coarse aggregate obtained in step (2) to obtain a random aggregate model of steel slag asphalt mixture;

[0010] (4) performing micro-pore random placement processing on the random aggregate model of steel slag asphalt mixture obtained in step (3) to obtain a random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles;

[0011] (5) The asphalt mortar ball units randomly deleted during the random placement process in step (4) are subjected to ice crystal treatment to obtain a random aggregate model of steel slag asphalt mixture that undergoes a freezing process and a melting process under the action of a freeze-thaw cycle.

[0012] (6) Applying servo force to the random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the action of a freeze-thaw cycle obtained in step (5) and then setting it to zero to obtain a random aggregate model of steel slag asphalt mixture after internal force release, and setting a cylindrical wall unit on the random aggregate model of steel slag asphalt mixture after internal force release to generate a random aggregate model of steel slag asphalt mixture suitable for a small beam three-point bending discrete element simulation test.

[0013] (7) calibrating the adhesion of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6) to obtain a preliminary calibrated random aggregate model of steel slag asphalt mixture;

[0014] (8) calibrating the contact parameters between the cluster unit numerical template of the steel slag coarse aggregate and the asphalt mortar ball unit in the steel slag asphalt mixture random aggregate model preliminarily calibrated in step (7) to obtain the steel slag asphalt mixture random aggregate model after secondary calibration;

[0015] (9) calibrating the contact parameters of the cluster unit numerical template of the steel slag coarse aggregate in the steel slag asphalt mixture random aggregate model after the secondary calibration in step (8) to obtain the final calibrated steel slag asphalt mixture random aggregate model;

[0016] (10) The final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) is verified to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle.

[0017] Preferably, step (1) is specifically:

[0018] Firstly, a digital camera was used to collect the macroscopic overall picture of steel slag coarse aggregate under the action of freeze-thaw cycles, and a 3D laser scanning confocal microscope (CLSM) and a digital camera were used to collect the microscopic surface morphology of steel slag coarse aggregate under the action of freeze-thaw cycles.

[0019] Then, the macroscopic picture of the steel slag coarse aggregate was imported into the modeling software to obtain the aggregate minor axis length d of the steel slag coarse aggregate under the action of freeze-thaw cycles. S , aggregate secondary axis length d I , and the major axis length d of the aggregate L ;

[0020] Then, the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles is calculated using the following formula: a :

[0021]

[0022] Then, on the contour edge of the steel slag coarse aggregate, multiple coordinate points p are extracted every 5° relative to the centroid of the steel slag coarse aggregate.i (x i ,y i ), p i+1 (x i+1 ,y i+1 ), p i+2 (x i+2 ,y i+2 )……p n (x n ,y n );p i represents the i-th coordinate point on the contour edge of the steel slag coarse aggregate, where i∈[1,n], n represents the total number of coordinate points on the contour edge of the steel slag coarse aggregate, x i Represents the horizontal coordinate of the i-th coordinate point on the edge of the contour of the steel slag coarse aggregate, y i Represents the ordinate of the i-th coordinate point on the contour edge of the slag coarse aggregate.

[0023] Then, the angularity index G of the steel slag coarse aggregate was calculated based on the image segmentation mask algorithm. A , specifically using the following formula: Among them, θ i Represents the i-th coordinate point p on the edge of the contour from the x-axis to the slag coarse aggregate i (x i ,y i )’s gradient direction angle;

[0024] Then, the microscopic surface morphology of the steel slag coarse aggregate under the action of freeze-thaw cycles is imported into the modeling software to obtain the calculated area a of the measured region of the steel slag coarse aggregate and the height function Z(x,y) on the calculated area a of the measured region;

[0025] Finally, according to the calculated area a of the measured area of ​​steel slag coarse aggregate and the height function Z(x,y) on the calculated area a of the measured area, the roughness S of steel slag coarse aggregate under the action of freeze-thaw cycle is calculated by the following formula: a :

[0026] Preferably, step (2) comprises the following sub-steps:

[0027] (2-1) determining the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1), and generating a polyhedron of the steel slag coarse aggregate in Rhino software;

[0028] Specifically, this step first uses Rhino software to draw the particle size and the sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1). aThen, the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle is searched in the first list established in advance, and then the cut body is twisted and stretched at any angle to generate a closed steel slag coarse aggregate polyhedron;

[0029] (2-2) Obtain the roughness S of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1) in the pre-established second table a The defined simulation parameters include the coefficient of restitution, static friction, and rolling friction;

[0030] (2-3) The sphericity value of the steel slag coarse aggregate under the action of freeze-thaw cycles obtained in step (1), the calculated angularity index obtained in (2-1), and the simulation parameters defined by the roughness of the steel slag coarse aggregate obtained in (2-2) are input into the steel slag coarse aggregate polyhedron obtained in step (2-2) to obtain a cluster unit numerical template of the steel slag coarse aggregate under the action of freeze-thaw cycles.

[0031] Preferably, the first list is established in the following manner:

[0032] A. Obtain different steel slag coarse aggregates;

[0033] B. For all the steel slag coarse aggregates obtained in step A, the angularity index G of the steel slag coarse aggregate with the maximum angularity index is obtained. A ; Then, by obtaining the best R that can be used for full-threaded operation of the computer in the PFC software p and a, where R p is the particle size ratio of the smallest aggregate to the largest aggregate used by the PFC software to fill the cluster unit; a is the overlap between aggregates; then, the maximum calculated angularity index a / R of the steel slag coarse aggregate is obtained. p , finally, the ratio of the maximum value of the angularity index of the steel slag coarse aggregate to the maximum calculated angularity index of 600 is calculated as the reduction ratio of the calculated angularity index of the steel slag coarse aggregate;

[0034] C. For the steel slag coarse aggregate with the maximum angularity index obtained in step B, the angularity index G of the steel slag coarse aggregate is reduced by the calculated angularity index of the steel slag coarse aggregate obtained in the second step. A Proportionately reduce the size to obtain the calculated angularity index of the modified slag coarse aggregate and fill it into the first table.

[0035] D. For all the steel slag coarse aggregates obtained in step A, repeat the above steps B and C until all the steel slag coarse aggregates are processed;

[0036] The second list is established in the following manner: obtain a plurality of steel slag coarse aggregates, and obtain their corresponding roughness S according to the method in the above step (1) aThen, the roughness S of the steel slag coarse aggregate is obtained through the particle Newton motion verification test of the PFC software. a The defined simulation parameters, including the coefficient of restitution, the coefficient of static friction and the coefficient of rolling friction, are filled into the second table.

[0037] Preferably, step (3) comprises the following sub-steps:

[0038] (3-1) Generate a small beam specimen consisting of multiple wall units in the PFC software;

[0039] (3-2) randomly placing the cluster unit numerical template of the steel slag coarse aggregate obtained in step (2-2) into the small beam sample generated in step (3-1) according to the gradation curve, so as to obtain the steel slag coarse aggregate and small beam boundary numerical template of the random aggregate model of the steel slag asphalt mixture;

[0040] (3-3) Asphalt mortar ball units are placed between the cluster unit numerical template of the steel slag coarse aggregate obtained in step (3-2) and the boundary of the beam sample to obtain a random aggregate model of the steel slag asphalt mixture.

[0041] Preferably, step (4) comprises the following sub-steps:

[0042] (4-1) The micro-porosity of steel slag asphalt mixture under freeze-thaw cycles was obtained by nuclear magnetic resonance test;

[0043] (4-2) According to the micro-porosity of the steel slag asphalt mixture obtained in step (4-1), the asphalt mortar ball units of the random aggregate model of the steel slag asphalt mixture obtained in step (3-3) are randomly deleted to obtain the random aggregate model of the steel slag asphalt mixture under the action of freeze-thaw cycles.

[0044] Step (5) includes the following sub-steps:

[0045] (5-1) randomly inserting ice crystal cluster units into the asphalt mortar ball units randomly deleted in step (4-2), the number of which is equal to the total number of asphalt mortar ball units deleted in step (4-2), so as to obtain the freezing process under the action of freeze-thaw cycle;

[0046] (5-2) All ice crystal cluster units in the freezing process under the freeze-thaw cycle obtained in (5-1) are deleted to obtain a random aggregate model of steel slag asphalt mixture that undergoes freezing and melting processes under the freeze-thaw cycle.

[0047] Preferably, step (6) is specifically as follows: first, a servo force is applied to the wall units around the small beam sample obtained in step (3-1) for extrusion to obtain a random aggregate model of steel slag asphalt mixture after extrusion; then, during the extrusion process, when the servo forces applied by all wall units are of the same magnitude, the extrusion is stopped to obtain a random aggregate model of steel slag asphalt mixture after internal force release; thereafter, the servo force applied to the wall units of the small beam sample is set to zero to release its internal force; finally, three cylindrical wall units are generated as pressure heads through PFC software, the displacement of the two cylindrical wall units at the lower end is controlled to be 0, and the cylindrical wall unit at the upper end is used as a numerical simulation pressure head for the small beam three-point bending test to obtain a random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test;

[0048] Step (7) is specifically as follows: first, the adhesion of the interface between asphalt and steel slag coarse aggregate is obtained from the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6); then, a direct tensile testing machine is used to load asphalt and steel slag coarse aggregate from the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6) to obtain the adhesion area A of the interface between the asphalt and the steel slag coarse aggregate, and the failure bonding stress σ of the interface between the asphalt and the steel slag coarse aggregate. N , and then the following formula is used to obtain the adhesion force F at the interface between asphalt and steel slag coarse aggregate in steel slag asphalt mixture: F = σ N × A. Finally, the calculated adhesion force F is used to calibrate the adhesion of the interface between asphalt and steel slag coarse aggregate to obtain a preliminarily calibrated random aggregate model of steel slag asphalt mixture.

[0049] Preferably, step (8) is specifically as follows: first, extracting the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit template from the steel slag asphalt mixture random aggregate model preliminarily calibrated in step (7), and querying the Burgers model parameters of the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit in a third list established in advance according to the environmental conditions of the steel slag asphalt mixture random aggregate model; then, using the obtained Burgers model parameters, calibrating the contact parameters of the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit to obtain the steel slag asphalt mixture random aggregate model after secondary calibration;

[0050] Step (9) specifically comprises the following steps: first, extracting the steel slag coarse aggregate cluster unit numerical template from the steel slag asphalt mixture random aggregate model after secondary calibration obtained in step (8); then, according to the steel slag asphalt mixture splitting test under freeze-thaw cycle, querying the contact parameters of the corresponding steel slag coarse aggregate cluster unit numerical template in the pre-established fourth list; finally, calibrating the steel slag coarse aggregate cluster unit numerical template using the obtained steel slag coarse aggregate cluster unit numerical template contact parameters to obtain the final calibrated steel slag asphalt mixture random aggregate model.

[0051] Preferably, step (10) specifically comprises the following steps: first, subjecting the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to a small beam three-point bending discrete element simulation test to obtain a simulated fracture energy density; at the same time, subjecting the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to a small beam three-point bending laboratory test to obtain a test fracture energy density; finally, subjecting the simulated fracture energy density and the test fracture energy density to a Bland-Altman method comparative analysis using the automated statistical software SPSSAU to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles.

[0052] According to another aspect of the present invention, a discrete element stochastic aggregate modeling system for steel slag asphalt mixture under freeze-thaw cycle conditions is provided, comprising:

[0053] The first module is used to obtain the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles. a , angularity index G A and roughness S a ;

[0054] The second module is used to determine the sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in the first module. a , angularity index G A and roughness S a , obtain the numerical template of steel slag coarse aggregate under freeze-thaw cycles;

[0055] The third module is used to perform random placement processing on the numerical template of steel slag coarse aggregate obtained in the second module to obtain a random aggregate model of steel slag asphalt mixture;

[0056] The fourth module is used to perform microscopic pore random placement processing on the random aggregate model of steel slag asphalt mixture obtained in the third module to obtain the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles;

[0057] The fifth module is used to perform ice crystal treatment on the asphalt mortar ball units randomly deleted during the random placement treatment in the fourth module, so as to obtain a random aggregate model of steel slag asphalt mixture that undergoes a freezing process and a melting process under the action of a freeze-thaw cycle.

[0058] The sixth module is used to apply servo force to the random aggregate model of steel slag asphalt mixture that has undergone the freezing process and the melting process under the action of the freeze-thaw cycle obtained in the fifth module and then reset it to zero to obtain the random aggregate model of steel slag asphalt mixture after the internal force is released, and set cylindrical wall units on the random aggregate model of steel slag asphalt mixture after the internal force is released to generate a random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test.

[0059] The seventh module is used to calibrate the adhesion of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in the sixth module, so as to obtain a preliminary calibrated random aggregate model of steel slag asphalt mixture;

[0060] The eighth module is used to calibrate the contact parameters between the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit in the random aggregate model of steel slag asphalt mixture preliminarily calibrated in the seventh module, so as to obtain the random aggregate model of steel slag asphalt mixture after secondary calibration;

[0061] The ninth module is used to calibrate the contact parameters of the cluster unit numerical template of the steel slag coarse aggregate in the random aggregate model of the steel slag asphalt mixture calibrated twice in the eighth module, so as to obtain the final calibrated random aggregate model of the steel slag asphalt mixture;

[0062] The tenth module is used to verify the final calibrated random aggregate model of steel slag asphalt mixture obtained in the ninth module to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles.

[0063] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0064] (1) The present invention adopts steps (1) and (2), and uses a 3D laser scanning confocal microscope (CLSM) and a digital camera to collect microscopic surface morphology of steel slag coarse aggregate under freeze-thaw cycles, and tests the sphericity, angularity and roughness of the steel slag coarse aggregate under freeze-thaw cycles to obtain cluster unit numerical template parameters of the steel slag coarse aggregate under freeze-thaw cycles. Therefore, the technical problem that the aggregate particle model of the existing random generation algorithm cannot fully reflect the surface characteristics of the real aggregate particles and has significant differences from the actual aggregate can be solved;

[0065] (2) The present invention adopts steps (7), (8) and (9), and calibrates the adhesion of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle, the contact parameters between the cluster unit numerical template of steel slag coarse aggregate and the cluster unit numerical template of steel slag coarse aggregate, etc. by designing interface tensile property test, steel slag asphalt mortar beam three-point bending test, and steel slag asphalt mixture splitting test. Therefore, it can solve the technical problem that the processing of the interface between aggregate and mortar and the interface position between aggregates is still unclear, which leads to unsatisfactory accuracy of force chain results in the existing method of establishing in-situ asphalt mixture discrete element model based on industrial CT and digital image technology;

[0066] (3) The present invention adopts steps (4) and (5), and sets the micro-porosity of the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle by randomly deleting the micro-pore sphere units. The final micro-porosity of the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle corresponds to the final micro-porosity of the steel slag asphalt mixture under the action of indoor freeze-thaw cycle, reflecting the internal structural damage of the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle. The freezing process of freeze-thaw cycle is simulated by randomly inserting ice crystal cluster units into the micro-pore sphere units deleted in the model; and the melting process is simulated by deleting the ice crystal cluster units. The dynamic simulation of the freeze-thaw cycle process is realized in the PFC software, so that the technical problem that the two existing steel slag asphalt mixture discrete element modeling methods cannot truly reflect the damage caused by freeze-thaw cycle and other effects to the steel slag asphalt mixture can be solved because both of them are modeling based on environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a physical form of the steel slag coarse aggregate and a schematic diagram of the test equipment of the present invention;

[0068] Figure 2 is a schematic diagram of a numerical model of steel slag coarse aggregate of the present invention;

[0069] Figure 3 It is a schematic diagram of a random aggregate model of freeze-thaw damage of steel slag asphalt mixture of the present invention;

[0070] Figure 4 It is a schematic diagram of ice crystals generated during the freezing process of the random aggregate model of the steel slag asphalt mixture of the present invention;

[0071] Figure 5 It is a schematic diagram of the disappearance of ice crystals during the melting process simulated by the random aggregate model of the steel slag asphalt mixture of the present invention;

[0072] Figure 6 It is a schematic diagram of the interface tensile property test of the present invention;

[0073] Figure 7 It is a schematic diagram of the steel slag asphalt mixture splitting test of the present invention;

[0074] Figure 8 is the Bland-ALtman diagram of fracture energy density obtained from experiments and simulations under freeze-thaw cycles;

[0075] Fig. 9 It is a flow chart of the discrete element random aggregate modeling method of steel slag asphalt mixture under freeze-thaw cycle conditions of the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0077] The present invention provides a discrete element random aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions. The method can reflect the actual conditions of steel slag coarse aggregate particles, the interface adhesion between steel slag coarse aggregate and asphalt mortar, and the internal pores of the steel slag asphalt mixture under the action of freeze-thaw cycles, and realizes the simulation of the freezing and melting process of water in the internal pores of the random aggregate model of the steel slag asphalt mixture under the action of freeze-thaw cycles.

[0078] The key technical points of the present invention are:

[0079] (1) The porosity of the random aggregate specimen of steel slag asphalt mixture under freeze-thaw cycles was set by randomly deleting spherical units. The final porosity of the numerical specimen after freeze-thaw cycles corresponded to the final porosity after indoor freeze-thaw cycles, reflecting the internal structural damage of the random aggregate model of steel slag asphalt mixture under freeze-thaw cycles.

[0080] (2) The freezing process of the freeze-thaw cycle is simulated by randomly inserting ice crystal cluster units into the spherical units whose pores are randomly deleted in the model; the melting process is simulated by deleting the ice crystal cluster units, and the dynamic simulation of the freeze-thaw cycle process is realized in the PFC software.

[0081] like Fig. 9 As shown, the present invention provides a discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions, comprising the following steps:

[0082] (1) Obtaining the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles a , angularity index G A and roughness S a ;

[0083] Specifically, this step first uses a digital camera to collect a macroscopic overall picture of the steel slag coarse aggregate under the action of freeze-thaw cycles, and uses a 3D laser scanning confocal microscope (CLSM) and a digital camera (using the macro mode of the camera) to collect a microscopic surface morphology of the steel slag coarse aggregate under the action of freeze-thaw cycles, such as Figure 1 As shown;

[0084] Then, the macroscopic picture of the steel slag coarse aggregate was imported into the modeling software (Shapeasy software) to obtain the aggregate minor axis length d of the steel slag coarse aggregate under the action of freeze-thaw cycles. S (unit: mm), aggregate secondary axis length d I (unit is mm), and the major axis length d of the aggregate L (Unit is mm) (For details, please refer to the description of the article "Study on Mesoscopic Model of Low-Temperature Cracking of Steel Slag Asphalt Mixture Based on Random Aggregate" written in 2022);

[0085] Then, the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles is calculated using the following formula: a :

[0086]

[0087] Then, on the contour edge of the steel slag coarse aggregate, multiple coordinate points p are extracted every 5° relative to the centroid of the steel slag coarse aggregate. i (x i ,y i ), p i+1 (x i+1 ,y i+1 ), p i+2 (x i+2 ,y i+2 )……p n (x n ,y n );p i represents the i-th coordinate point on the contour edge of the steel slag coarse aggregate, where i∈[1,n], n represents the total number of coordinate points on the contour edge of the steel slag coarse aggregate, x i Represents the horizontal coordinate of the i-th coordinate point on the edge of the contour of the steel slag coarse aggregate, y i Represents the ordinate of the i-th coordinate point on the contour edge of the slag coarse aggregate.

[0088] Then, the angularity index G of the steel slag coarse aggregate is calculated based on the image segmentation mask algorithm (Socel mask algorithm). A , specifically using the following formula: Among them, θ i Represents the i-th coordinate point p on the edge of the contour from the x-axis to the slag coarse aggregate i (x i ,y i )’s gradient direction angle;

[0089] Then, the microscopic surface morphology of the steel slag coarse aggregate under the action of freeze-thaw cycles is imported into the modeling software (Shapeasy software) to obtain the calculated area a of the measured region of the steel slag coarse aggregate and the height function Z (x, y) on the calculated area a of the measured region;

[0090] Finally, according to the calculated area a of the measured area of ​​steel slag coarse aggregate and the height function Z(x,y) on the calculated area a of the measured area, the roughness S of steel slag coarse aggregate under the action of freeze-thaw cycle is calculated by the following formula: a :

[0091] (2) The sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1) a , angularity index G A and roughness S a , obtain the numerical template of steel slag coarse aggregate under freeze-thaw cycles;

[0092] Specifically, this step includes the following sub-steps:

[0093] (2-1) determining the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1), and generating a polyhedron of the steel slag coarse aggregate in the Rhino software;

[0094] Specifically, this step first uses the Rhino software to plot the particle size and the sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1). a A consistent sphere (Table 1 shows the corresponding sphericity values ​​for different steel slag coarse aggregates, which are all calculated according to the sphericity value calculation formula described above); then, the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle is queried in the pre-established first list (as shown in Table 2 below), and then the cut body is twisted and stretched at any angle to generate a closed steel slag coarse aggregate polyhedron;

[0095] The first list in this step is built as follows:

[0096] The first step is to obtain different steel slag coarse aggregates;

[0097] In the second step, for all the steel slag coarse aggregates obtained in the first step, the angularity index G of the steel slag coarse aggregate with the maximum angularity index is obtained. A ; Then, by obtaining the best R that can be used for full-threaded operation of the computer in the PFC software p and a (e.g., R p =0.2, a = 120°), where R p is the particle size ratio of the smallest aggregate (pebble) to the largest aggregate (pebble) used by the PFC software to fill the cluster unit; a is the overlap between the aggregates (pebbles); then, the maximum calculated angularity index a / R of the steel slag coarse aggregate is obtained. p (In this example, it is equal to 120 / 0.2=600), finally, the ratio of the maximum value of the angularity index of the steel slag coarse aggregate (in this example, it is equal to 3114) and the maximum calculated angularity index 600 is calculated as the reduction ratio of the calculated angularity index of the steel slag coarse aggregate (in this example, it is equal to 5.19).

[0098] In the third step, for the steel slag coarse aggregate with the maximum angularity index obtained in the second step, the angularity index G of the steel slag coarse aggregate is calculated by reducing the angularity index of the steel slag coarse aggregate obtained in the second step. A Proportionately reduce the size to obtain the calculated angularity index of the modified slag coarse aggregate and fill it into the first table.

[0099] In the fourth step, for all the steel slag coarse aggregates obtained in the first step, the above second and third steps are repeated until all the steel slag coarse aggregates are processed. The final results are shown in Table 2.

[0100] (2-2) Obtain the roughness S of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1) from the pre-established second table (i.e., Table 3 below): a The defined simulation parameters include the coefficient of restitution, static friction, and rolling friction;

[0101] The second list in this step is created as follows:

[0102] Obtain a plurality of steel slag coarse aggregates, and obtain the corresponding roughness S according to the method in the above step (1) a Then, the roughness S of the steel slag coarse aggregate is obtained through the particle Newton motion verification test of the PFC software. a The defined simulation parameters include the coefficient of restitution, the coefficient of static friction and the coefficient of rolling friction, and are entered into the second table, as shown in Table 3 below.

[0103] Table 1 Sphericity of steel slag coarse aggregate

[0104]

[0105] Table 2 Calculated angularity index of steel slag coarse aggregate

[0106]

[0107] Table 3 Roughness simulation parameters of steel slag coarse aggregate

[0108]

[0109]

[0110] (2-3) The sphericity value of the steel slag coarse aggregate under the action of freeze-thaw cycles obtained in step (1), the calculated angularity index obtained in step (2-1), and the simulation parameters defined by the roughness of the steel slag coarse aggregate obtained in step (2-2) are input into the steel slag coarse aggregate polyhedron obtained in step (2-2) to obtain a cluster unit numerical template of the steel slag coarse aggregate under the action of freeze-thaw cycles (e.g. Figure 2 shown);

[0111] The advantages of steps (1) and (2) are that the sphericity, angularity and roughness of steel slag coarse aggregate under freeze-thaw cycles can be tested experimentally to obtain the cluster unit numerical template parameters of steel slag coarse aggregate under freeze-thaw cycles, so that the cluster unit numerical template of steel slag coarse aggregate under freeze-thaw cycles has better consistency with the actual aggregate.

[0112] (3) performing random placement processing on the numerical template of steel slag coarse aggregate obtained in step (2) to obtain a random aggregate model of steel slag asphalt mixture;

[0113] Specifically, this step includes the following sub-steps:

[0114] (3-1) Generate a small beam specimen consisting of multiple wall units in the PFC software;

[0115] (3-2) randomly placing the cluster unit numerical template of the steel slag coarse aggregate obtained in step (2-2) into the small beam sample generated in step (3-1) according to the gradation curve, so as to obtain the steel slag coarse aggregate and small beam boundary numerical template of the random aggregate model of the steel slag asphalt mixture;

[0116] (3-3) placing asphalt mortar ball units between the cluster unit numerical template of the steel slag coarse aggregate obtained in step (3-2) and the boundary of the beam sample to obtain a random aggregate model of the steel slag asphalt mixture;

[0117] (4) performing micro-pore random placement processing on the random aggregate model of steel slag asphalt mixture obtained in step (3) to obtain a random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles;

[0118] Specifically, this step includes the following sub-steps:

[0119] (4-1) Obtaining the micro-porosity of steel slag asphalt mixture under freeze-thaw cycles;

[0120] Specifically, this step is to obtain the microscopic porosity of the steel slag asphalt mixture under the action of freeze-thaw cycles through a nuclear magnetic resonance test (NMR).

[0121] (4-2) Randomly delete the asphalt mortar ball units of the random aggregate model of the steel slag asphalt mixture obtained in step (3-3) according to the micro-porosity of the steel slag asphalt mixture obtained in step (4-1) to obtain a random aggregate model of the steel slag asphalt mixture under the action of freeze-thaw cycles, such as Figure 3 As shown;

[0122] (5) The asphalt mortar ball units randomly deleted during the random placement process in step (4) are subjected to ice crystal treatment to obtain a random aggregate model of steel slag asphalt mixture that undergoes a freezing process and a melting process under the action of a freeze-thaw cycle.

[0123] Specifically, this step includes the following sub-steps:

[0124] (5-1) randomly inserting ice crystal cluster units (whose number is equal to the total number of asphalt mortar ball units deleted in step (4-2)) into the asphalt mortar ball units randomly deleted in step (4-2) to obtain the freezing process under the action of freeze-thaw cycle (such as Figure 4 shown);

[0125] (5-2) All ice crystal cluster units in the freezing process under the freeze-thaw cycle obtained in (5-1) are deleted to obtain a random aggregate model of steel slag asphalt mixture that has undergone the freezing process and the melting process under the freeze-thaw cycle (such as Figure 5 shown).

[0126] The advantages of steps (4) and (5) are that the freeze-thaw cycle effect of the random aggregate model of the steel slag asphalt mixture can be simulated by controlling the pore ball unit.

[0127] (6) Applying servo force to the random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the action of a freeze-thaw cycle obtained in step (5) and then setting it to zero to obtain a random aggregate model of steel slag asphalt mixture after internal force release, and setting a cylindrical wall unit on the random aggregate model of steel slag asphalt mixture after internal force release to generate a random aggregate model of steel slag asphalt mixture suitable for a small beam three-point bending discrete element simulation test.

[0128] This step is specifically as follows: first, a servo force is applied to the wall units around the small beam sample obtained in step (3-1) to extrude (apply force step by step) to obtain the random aggregate model of steel slag asphalt mixture after extrusion; then, during the extrusion process, when the servo forces applied by all wall units are of the same magnitude, the extrusion is stopped to obtain the random aggregate model of steel slag asphalt mixture after the internal force is released; thereafter, the servo force applied to the wall units of the small beam sample is set to zero to release its internal force; finally, three cylindrical wall units (two of which are below the random aggregate model of steel slag asphalt mixture and one is above the random aggregate model of steel slag asphalt mixture) are generated as pressure heads through PFC software, the displacement of the two cylindrical wall units at the lower end is controlled to be 0, and the cylindrical wall unit at the upper end is used as the numerical simulation pressure head for the small beam three-point bending test to obtain the random aggregate model of steel slag asphalt mixture suitable for the discrete element simulation test of the small beam three-point bending.

[0129] (7) calibrating the adhesion of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6) to obtain a preliminary calibrated random aggregate model of steel slag asphalt mixture;

[0130] Specifically, this step includes, first, obtaining the adhesion of the interface between asphalt and steel slag coarse aggregate from the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6); then, using a direct tensile testing machine to load asphalt and steel slag coarse aggregate from the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6) (loading rate is 1 mm / min, such as Figure 6 As shown in the figure), the adhesion area A of the interface between asphalt and steel slag coarse aggregate and the failure bonding stress σ of the interface between asphalt and steel slag coarse aggregate are obtained. N , and then the following formula is used to obtain the adhesion force F at the interface between asphalt and steel slag coarse aggregate in steel slag asphalt mixture: F = σ N × A. Finally, the calculated adhesion force F is used to calibrate the adhesion of the interface between asphalt and steel slag coarse aggregate to obtain a preliminarily calibrated random aggregate model of steel slag asphalt mixture.

[0131] (8) calibrating the contact parameters between the cluster unit numerical template of the steel slag coarse aggregate and the asphalt mortar ball unit in the steel slag asphalt mixture random aggregate model preliminarily calibrated in step (7) to obtain the steel slag asphalt mixture random aggregate model after secondary calibration;

[0132] Specifically, this step includes firstly extracting the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit template from the steel slag asphalt mixture random aggregate model preliminarily calibrated in step (7), and querying the Burgers model parameters of the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit in the pre-established third list (i.e., Table 4 below) according to the environmental conditions of the steel slag asphalt mixture random aggregate model (Table 4 below shows the Burgers model parameters of steel slag asphalt mortar, which include the Calvin partial spring normal stiffness K kn , damping coefficient f s 、Maxwell part spring normal stiffness K mn 、Maxwell partial viscosity normal stiffness C mn and Calvin partial viscosity normal stiffness C kn ), then, the obtained Burgers model parameters are used to calibrate the contact parameters between the cluster unit numerical template of the steel slag coarse aggregate and the asphalt mortar ball unit, so as to obtain the random aggregate model of the steel slag asphalt mixture after secondary calibration;

[0133] Table 4 Parameters of Burgers model for steel slag asphalt mortar

[0134]

[0135] (9) The contact parameters (including vertical stiffness ratio k) of the cluster unit numerical template of steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture after secondary calibration in step (8) are calculated. n , shear stiffness ratio k s and friction coefficient μ,) are calibrated to obtain the final calibrated random aggregate model of steel slag asphalt mixture;

[0136] Specifically, this step includes firstly extracting the steel slag coarse aggregate cluster unit numerical template from the steel slag asphalt mixture random aggregate model obtained in step (8) after secondary calibration, and then, according to the steel slag asphalt mixture splitting test under freeze-thaw cycle (such as Figure 7 As shown), the contact parameters of the corresponding cluster unit numerical template of steel slag coarse aggregate are queried in the pre-established fourth table (i.e., Table 5 below) (Table 5 below shows the contact parameters of the cluster unit numerical template of steel slag coarse aggregate, which includes the vertical stiffness ratio k n , shear stiffness ratio k s and friction coefficient μ), finally, the obtained cluster unit numerical template contact parameters of steel slag coarse aggregate are used to calibrate the cluster unit numerical template of steel slag coarse aggregate to obtain the final calibrated random aggregate model of steel slag asphalt mixture;

[0137] Table 5 Calibration results of linear contact model of steel slag coarse aggregate in steel slag asphalt mixture

[0138] Environmental conditions <![CDATA[k n / N·m -1 ]]> <![CDATA[k s / N·m -1 ]]> μ Original condition 5.40E+07 4.20E+07 0.7 Freeze-thaw cycles 0 times 5.40E+07 4.20E+07 0.7 Freeze-thaw cycles 5 times 5.37E+07 4.20E+07 0.7 Freeze-thaw cycles 10 times 5.32E+07 4.20E+07 0.6 Freeze-thaw cycles 15 times 5.25E+07 4.14E+07 0.6 Freeze-thaw cycles 20 times 5.22E+07 4.14E+07 0.5

[0139] The advantages of the above steps (7) to (9) are that the adhesion parameters of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture under freeze-thaw cycles, the contact parameters of the cluster unit numerical template of steel slag coarse aggregate and the cluster unit numerical template of steel slag coarse aggregate, etc. can be obtained through experiments, thereby obtaining more model parameter information.

[0140] (10) verifying the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle;

[0141] Specifically, this step includes, firstly, performing a small beam three-point bending discrete element simulation test on the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to obtain a simulated fracture energy density (simulated value); at the same time, performing a small beam three-point bending laboratory test on the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to obtain a test fracture energy density (test value); finally, performing a Bland-Altman method comparison (Bland-Altman method comparison, referred to as Bland-ALtman diagram) analysis on the simulated fracture energy density and the test fracture energy density using automated statistical software (Statistical Product and Service Software Automatically, referred to as SPSSAU software) to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles. Figure 8 As shown, all data of the difference between the simulated value of fracture energy density and the experimental value of fracture energy density are within the 95% confidence interval, indicating that the discrete element simulation results are in good consistency with the laboratory results, and the modeling method of the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles of the present invention is scientific.

[0142] The advantage of this step is that the scientificity of the random aggregate model of steel slag asphalt mixture under the freeze-thaw cycle obtained by the present invention can be verified by statistical methods.

[0143] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions, characterized in that: The following steps are involved: (1) Obtaining the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles a , angularity index G A and roughness S a ; (2) The sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1) a , angularity index G A and roughness S a , obtain the numerical template of steel slag coarse aggregate under the action of freeze-thaw cycle; step (2) includes the following sub-steps: (2-1) determining the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1), and generating a polyhedron of the steel slag coarse aggregate in Rhino software; Specifically, this step first uses Rhino software to plot the particle size and the sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1). a Then, the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle is searched in the first list established in advance, and then the cut body is twisted and stretched at any angle to generate a closed steel slag coarse aggregate polyhedron; (2-2) Obtain the roughness S of the steel slag coarse aggregate under the freeze-thaw cycle obtained in step (1) in the pre-established second table. a The defined simulation parameters include the coefficient of restitution, static friction, and rolling friction; (2-3) inputting the sphericity value of the steel slag coarse aggregate under the action of freeze-thaw cycles obtained in step (1), the calculated angularity index obtained in step (2-1), and the simulation parameters defined by the roughness of the steel slag coarse aggregate obtained in step (2-2) into the steel slag coarse aggregate polyhedron obtained in step (2-2) to obtain a cluster unit numerical template of the steel slag coarse aggregate under the action of freeze-thaw cycles; (3) The numerical template of the steel slag coarse aggregate obtained in step (2) is randomly placed to obtain a random aggregate model of the steel slag asphalt mixture; step (3) includes the following sub-steps: (3-1) Generate a small beam specimen consisting of multiple wall units in the PFC software; (3-2) randomly placing the cluster unit numerical template of the steel slag coarse aggregate obtained in step (2-2) into the small beam sample generated in step (3-1) according to the gradation curve, so as to obtain the steel slag coarse aggregate and small beam boundary numerical template of the random aggregate model of the steel slag asphalt mixture; (3-3) placing asphalt mortar ball units between the cluster unit numerical template of the steel slag coarse aggregate obtained in step (3-2) and the boundary of the beam sample to obtain a random aggregate model of the steel slag asphalt mixture; (4) performing microscopic pore random placement processing on the random aggregate model of steel slag asphalt mixture obtained in step (3) to obtain a random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles; step (4) comprises the following sub-steps: (4-1) The micro-porosity of steel slag asphalt mixture under freeze-thaw cycles was obtained by nuclear magnetic resonance test; (4-2) randomly deleting the asphalt mortar ball units of the random aggregate model of the steel slag asphalt mixture obtained in step (3-3) according to the micro-porosity of the steel slag asphalt mixture obtained in step (4-1) to obtain a random aggregate model of the steel slag asphalt mixture under the action of freeze-thaw cycles; (5) performing ice crystal treatment on the asphalt mortar ball units randomly deleted during the random placement treatment in step (4) to obtain a random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the action of a freeze-thaw cycle; step (5) includes the following sub-steps: (5-1) randomly inserting ice crystal cluster units into the asphalt mortar ball units randomly deleted in step (4-2), the number of which is equal to the total number of asphalt mortar ball units deleted in step (4-2), so as to obtain the freezing process under the action of freeze-thaw cycle; (5-2) All ice crystal cluster units in the freezing process under the freeze-thaw cycle obtained in (5-1) are deleted to obtain a random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the freeze-thaw cycle; (6) applying a servo force to the random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the action of a freeze-thaw cycle obtained in step (5) and then setting it to zero to obtain a random aggregate model of steel slag asphalt mixture after internal force release, and setting a cylindrical wall unit on the random aggregate model of steel slag asphalt mixture after internal force release to generate a random aggregate model of steel slag asphalt mixture suitable for a small beam three-point bending discrete element simulation test; (7) calibrating the adhesion of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6) to obtain a preliminary calibrated random aggregate model of steel slag asphalt mixture; (8) calibrating the contact parameters between the cluster unit numerical template of the steel slag coarse aggregate and the asphalt mortar ball unit in the steel slag asphalt mixture random aggregate model preliminarily calibrated in step (7) to obtain the steel slag asphalt mixture random aggregate model after secondary calibration; (9) calibrating the contact parameters of the cluster unit numerical template of the steel slag coarse aggregate in the steel slag asphalt mixture random aggregate model after the secondary calibration in step (8) to obtain the final calibrated steel slag asphalt mixture random aggregate model; (10) The final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) is verified to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle.

2. The discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions according to claim 1 is characterized in that: Step (1) is specifically as follows: Firstly, a digital camera was used to collect the macroscopic overall picture of steel slag coarse aggregate under the action of freeze-thaw cycles, and a 3D laser scanning confocal microscope (CLSM) and a digital camera were used to collect the microscopic surface morphology of steel slag coarse aggregate under the action of freeze-thaw cycles. Then, the macroscopic picture of the steel slag coarse aggregate was imported into the modeling software to obtain the aggregate minor axis length d of the steel slag coarse aggregate under the action of freeze-thaw cycles. S , aggregate secondary axis length d I , and the major axis length d of the aggregate L ; Then, the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles is calculated using the following formula: a : Then, on the contour edge of the steel slag coarse aggregate, multiple coordinate points p are extracted every 5° relative to the centroid of the steel slag coarse aggregate. i (x i ,y i ), p i+1 (x i+1 ,y i+1 ), p i+2 (x i+2 ,y i+2 )……p n (x n ,y n );p i represents the i-th coordinate point on the contour edge of the steel slag coarse aggregate, where i∈[1,n], n represents the total number of coordinate points on the contour edge of the steel slag coarse aggregate, x i Represents the horizontal coordinate of the i-th coordinate point on the edge of the contour of the steel slag coarse aggregate, y i Represents the ordinate of the i-th coordinate point on the contour edge of the steel slag coarse aggregate; Then, the angularity index G of the steel slag coarse aggregate was calculated based on the image segmentation mask algorithm. A , specifically using the following formula: Among them, θ i Represents the i-th coordinate point p on the edge of the contour from the x-axis to the slag coarse aggregate i (x i ,y i )’s gradient direction angle; Then, the microscopic surface morphology of the steel slag coarse aggregate under the action of freeze-thaw cycles is imported into the modeling software to obtain the calculated area a of the measured region of the steel slag coarse aggregate and the height function Z(x,y) on the calculated area a of the measured region; Finally, according to the calculated area a of the measured area of ​​steel slag coarse aggregate and the height function Z(x,y) on the calculated area a of the measured area, the roughness S of steel slag coarse aggregate under the action of freeze-thaw cycle is calculated by the following formula: a :

3. The discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions according to claim 2 is characterized in that: The first list is built as follows: A. Obtain different steel slag coarse aggregates; B. For all the steel slag coarse aggregates obtained in step A, the angularity index G of the steel slag coarse aggregate with the maximum angularity index is obtained. A ; Then, by obtaining the best R that can be used for full-threaded operation of the computer in the PFC software p and a, where R p is the particle size ratio of the smallest aggregate to the largest aggregate used by the PFC software to fill the cluster unit; a is the overlap between aggregates; then, the maximum calculated angularity index a / R of the steel slag coarse aggregate is obtained. p , finally, the ratio of the maximum value of the angularity index of the steel slag coarse aggregate to the maximum calculated angularity index of 600 is calculated as the reduction ratio of the calculated angularity index of the steel slag coarse aggregate; C. For the steel slag coarse aggregate with the maximum angularity index obtained in step B, the angularity index G of the steel slag coarse aggregate is reduced by the calculated angularity index of the steel slag coarse aggregate obtained in the second step. A Proportionately reduce the size to obtain the calculated angularity index of the modified slag coarse aggregate and fill it into the first table. D. For all the steel slag coarse aggregates obtained in step A, repeat the above steps B and C until all the steel slag coarse aggregates are processed; The second list is established in the following manner: obtain a plurality of steel slag coarse aggregates, and obtain their corresponding roughness S according to the method in the above step (1) a Then, the roughness S of the steel slag coarse aggregate is obtained through the particle Newton motion verification test of the PFC software. a The defined simulation parameters, including the coefficient of restitution, the coefficient of static friction and the coefficient of rolling friction, are filled into the second list.

4. The discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions according to claim 3 is characterized in that: Step (6) is specifically as follows: first, a servo force is applied to the wall units around the small beam sample obtained in step (3-1) to extrude the sample so as to obtain a random aggregate model of steel slag asphalt mixture after extrusion; then, during the extrusion process, when the servo forces applied by all wall units are of the same magnitude, the extrusion is stopped so as to obtain a random aggregate model of steel slag asphalt mixture after the internal force is released; thereafter, the servo force applied to the wall units of the small beam sample is set to zero so as to release the internal force; finally, three cylindrical wall units are generated as pressure heads through PFC software, the displacement of the two cylindrical wall units at the lower end is controlled to be 0, and the cylindrical wall unit at the upper end is used as a numerical simulation pressure head for the small beam three-point bending test so as to obtain a random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test; Step (7) is specifically as follows: first, the adhesion of the interface between asphalt and steel slag coarse aggregate is obtained from the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6); then, a direct tensile testing machine is used to load asphalt and steel slag coarse aggregate from the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in step (6) to obtain the adhesion area A of the interface between the asphalt and the steel slag coarse aggregate, and the failure bonding stress σ of the interface between the asphalt and the steel slag coarse aggregate. N , and then the following formula is used to obtain the adhesion force F at the interface between asphalt and steel slag coarse aggregate in steel slag asphalt mixture: F = σ N ×A; Finally, the calculated adhesion force F is used to calibrate the adhesion of the interface between asphalt and steel slag coarse aggregate to obtain a preliminary calibrated random aggregate model of steel slag asphalt mixture.

5. The discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions according to claim 4 is characterized in that: Step (8) is specifically as follows: first, extract the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit template from the steel slag asphalt mixture random aggregate model preliminarily calibrated in step (7), and query the Burgers model parameters of the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit in a third list established in advance according to the environmental conditions of the steel slag asphalt mixture random aggregate model; then, use the obtained Burgers model parameters to calibrate the contact parameters of the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit to obtain the steel slag asphalt mixture random aggregate model after secondary calibration; Step (9) specifically comprises the following steps: first, extracting the steel slag coarse aggregate cluster unit numerical template from the steel slag asphalt mixture random aggregate model after secondary calibration obtained in step (8); then, according to the steel slag asphalt mixture splitting test under freeze-thaw cycle, querying the contact parameters of the corresponding steel slag coarse aggregate cluster unit numerical template in the pre-established fourth list; finally, calibrating the steel slag coarse aggregate cluster unit numerical template using the obtained steel slag coarse aggregate cluster unit numerical template contact parameters to obtain the final calibrated steel slag asphalt mixture random aggregate model.

6. The discrete element stochastic aggregate modeling method for steel slag asphalt mixture under freeze-thaw cycle conditions according to claim 5 is characterized in that: Step (10) specifically comprises the following steps: first, subjecting the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to a small beam three-point bending discrete element simulation test to obtain a simulated fracture energy density; at the same time, subjecting the final calibrated random aggregate model of steel slag asphalt mixture obtained in step (9) to a small beam three-point bending laboratory test to obtain a test fracture energy density; finally, subjecting the simulated fracture energy density and the test fracture energy density to a Bland-Altman method comparative analysis using the automated statistical software SPSSAU to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles.

7. A discrete element stochastic aggregate modeling system for steel slag asphalt mixture under freeze-thaw cycle conditions, characterized in that: include: The first module is used to obtain the sphericity value P of steel slag coarse aggregate under freeze-thaw cycles. a , angularity index G A and roughness S a ; The second module is used to determine the sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in the first module. a , angularity index G A and roughness S a , obtain the numerical template of steel slag coarse aggregate under freeze-thaw cycles; The second module includes: The first submodule is used to determine the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle obtained in the first module, and generate the steel slag coarse aggregate polyhedron in the Rhino software; Specifically, this step first uses Rhino software to plot the particle size and the sphericity value P of the steel slag coarse aggregate under the freeze-thaw cycle obtained in the first module. a Then, the calculated angularity index of the steel slag coarse aggregate under the freeze-thaw cycle is searched in the first list established in advance, and then the cut body is twisted and stretched at any angle to generate a closed steel slag coarse aggregate polyhedron; The second submodule is used to obtain the roughness S of the steel slag coarse aggregate under the freeze-thaw cycle obtained in the first module in the pre-established second list. a The defined simulation parameters include the coefficient of restitution, static friction, and rolling friction; The third submodule is used to input the sphericity value of the steel slag coarse aggregate under the action of freeze-thaw cycles obtained by the first module, the calculated angularity index obtained by the first submodule, and the simulation parameters defined by the roughness of the steel slag coarse aggregate obtained by the second submodule into the steel slag coarse aggregate polyhedron obtained by the second submodule, so as to obtain a cluster unit numerical template of the steel slag coarse aggregate under the action of freeze-thaw cycles; The third module is used to perform random placement processing on the numerical template of steel slag coarse aggregate obtained in the second module to obtain a random aggregate model of steel slag asphalt mixture; the third module includes: The fourth submodule is used to generate a beam specimen composed of multiple wall units in the PFC software; The fifth submodule is used to randomly place the cluster unit numerical template of the steel slag coarse aggregate obtained by the second submodule into the small beam sample generated by the fourth submodule according to the gradation curve, so as to obtain the steel slag coarse aggregate and small beam boundary numerical template of the random aggregate model of the steel slag asphalt mixture; The sixth submodule is used to place asphalt mortar ball units between the cluster unit numerical template of steel slag coarse aggregate obtained in the fifth submodule and the boundary of the beam sample to obtain a random aggregate model of steel slag asphalt mixture; The fourth module is used to perform microscopic pore random placement processing on the random aggregate model of steel slag asphalt mixture obtained in the third module to obtain the random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycle; the fourth module includes: The seventh submodule is used to obtain the micro-porosity of steel slag asphalt mixture under freeze-thaw cycles through nuclear magnetic resonance test; The eighth submodule is used to randomly delete the asphalt mortar ball units of the random aggregate model of the steel slag asphalt mixture obtained by the sixth submodule according to the micro-porosity of the steel slag asphalt mixture obtained by the seventh submodule, so as to obtain the random aggregate model of the steel slag asphalt mixture under the action of freeze-thaw cycles; The fifth module is used to perform ice crystal processing on the asphalt mortar ball units randomly deleted during the random placement process in the fourth module to obtain a random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the action of a freeze-thaw cycle; the fifth module includes: A ninth submodule is used to randomly insert ice crystal cluster units into the asphalt mortar ball units randomly deleted by the eighth submodule, and the number of ice crystal cluster units is equal to the total number of asphalt mortar ball units deleted by the eighth submodule, so as to obtain the freezing process under the action of freeze-thaw cycle; The tenth submodule is used to delete all ice crystal cluster units in the freezing process under the freeze-thaw cycle obtained by the ninth submodule, so as to obtain a random aggregate model of steel slag asphalt mixture that has undergone a freezing process and a melting process under the freeze-thaw cycle; The sixth module is used to apply servo force to the random aggregate model of steel slag asphalt mixture that has undergone freezing and melting processes under the action of freeze-thaw cycles obtained in the fifth module and then reset it to zero, so as to obtain the random aggregate model of steel slag asphalt mixture after internal force release, and set cylindrical wall units on the random aggregate model of steel slag asphalt mixture after internal force release, so as to generate a random aggregate model of steel slag asphalt mixture suitable for small beam three-point bending discrete element simulation test; The seventh module is used to calibrate the adhesion of the interface between asphalt and steel slag coarse aggregate in the random aggregate model of steel slag asphalt mixture suitable for the small beam three-point bending discrete element simulation test obtained in the sixth module, so as to obtain a preliminary calibrated random aggregate model of steel slag asphalt mixture; The eighth module is used to calibrate the contact parameters between the cluster unit numerical template of steel slag coarse aggregate and the asphalt mortar ball unit in the random aggregate model of steel slag asphalt mixture preliminarily calibrated in the seventh module, so as to obtain the random aggregate model of steel slag asphalt mixture after secondary calibration; The ninth module is used to calibrate the contact parameters of the cluster unit numerical template of the steel slag coarse aggregate in the random aggregate model of the steel slag asphalt mixture calibrated twice in the eighth module, so as to obtain the final calibrated random aggregate model of the steel slag asphalt mixture; The tenth module is used to verify the final calibrated random aggregate model of steel slag asphalt mixture obtained in the ninth module to obtain the final random aggregate model of steel slag asphalt mixture under the action of freeze-thaw cycles.

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