Using discrete element models to track movement of coarse aggregates during compaction of asphalt mixture

Gong, Fangyuan; Zhou, Xiaodong; You, Zhanping; Liu, Yu; Chen, Siyu

doi:10.1016/j.conbuildmat.2018.08.133

Cited by 86 publications

(22 citation statements)

References 29 publications

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“…MATLAB software included plenty of useful functions of neural network algorithm in the Neural Network Toolbox as shown in the literature [32]. These functions provide the possibility to build a basic model for those not professional at machine learning.…”

Section: Data Analysis and Bp Neural Network Model Buildingmentioning

confidence: 99%

“…In general, the BP neural network model includes an input layer, an output layer and multiple hidden layers. The previous research shows that the three layer BP neural network with one hidden layer can approximate any non-linear function [32]. Hence, the model in this article was established with one hidden layer and the model structure is shown in Figure 4.…”

Section: Data Analysis and Bp Neural Network Model Buildingmentioning

confidence: 99%

“…For each group, 10 parallel tests were carried out to determine the optimum node number. The detailed procedure is omitted in this article since it was introduced in another publication [32]. The following Figure 5 shows that coefficient of determination (R 2 ) changes with node number.…”

Section: Hidden Layer Node Number Determinationmentioning

confidence: 99%

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Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

Liu

et al. 2020

Applied Sciences

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Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape.

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Section: Data Analysis and Bp Neural Network Model Buildingmentioning

confidence: 99%

Section: Data Analysis and Bp Neural Network Model Buildingmentioning

confidence: 99%

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Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

Liu

et al. 2020

Applied Sciences

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“…Since asphalt mixture is a bulk medium, its deformation is mainly caused by the relative sliding and rolling of the internal medium or the opening and closing of the weak interface, rather than its own deformation [37]. Therefore, the PFC software based on the discrete element calculation method is widely used in the numerical simulation of asphalt mixture.…”

Section: Two-dimensional Mesoscopic Model Of Ogfcmentioning

confidence: 99%

Aggregate-Bitumen Interface Enhancement Mechanism of Utilization of Oil Shale Waste as Fine Aggregate in Open Grade Friction Course

Guo

Chen

et al. 2019

Coatings

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Oil shale waste (OSW), as fine aggregate in the mixture (particle size less than 4.75 mm), can effectively improve the overall properties of open grade friction course (OGFC), but the reinforcement mechanism is not clear. Thus, a comprehensive investigation of the reinforcement mechanism of OSW as fine aggregate is essential to provide better understanding for promoting its engineering application. In this paper, the reinforcement mechanism of OSW was explored through numerical calculations and laboratory tests from three aspects: macroscopic mechanical characteristics of mixture, micromechanics of asphalt mortar containing OSW filler, and adsorption characteristics of OSW. Numerical simulation results showed that the aggregate with a particle size greater than 4.75 mm in OGFC is the skeleton, which is the main loading bearing aggregate, and the skeleton bears more than 85% of external loads. The beam bending test and indirect tensile test results illustrated that the introduction of OSW improves the shear resistance and flexure-tension resistance properties of asphalt mortar, which is beneficial the overall properties of OGFC. From the Brunauer Emmett Tell test and Scanning Electron Microscope test, it was known that OSW has large specific surface area, dense pore structure, and various mesoporous shapes, which means a larger adsorption area and stronger adsorption with asphalt binder. Three self-developed tests containing asphalt adsorption capacity test, infiltrated asphalt saturation test and aggregate-bitumen interface observation test manifested that the existence of “claws”-pointed synapses at OSW-bitumen interface is the main reason for the significant improvement of properties of asphalt mortar containing OSW filler.

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“…There is good interlocking at the contact points among aggregates that restricts the movement of coarse aggregates, and there is sufficient contact within the aggregate skeleton to bear the contact force. The foregoing is important to guarantee the rutting resistance of an asphalt mixture [12,13]. Meanwhile, research findings show that there is a strong correlation between the internal skeleton structure indices of contact points, the aggregate size, the inclination angle, and the rutting performance of the asphalt mixture [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Gradual Meso-Structural Response Behaviour of Characteristics of Asphalt Mixture Main Skeleton Subjected to Load

et al. 2019

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In order to provide a reference for the gradation design of dense skeleton asphalt mixtures (DSAM), this study conducts a thorough analysis of the gradual meso-structural response behaviour of characteristics of the asphalt mixture main skeleton subjected to load using the digital image processing (DIP) technique. Moreover, gradation optimisation measures and the design criteria of mesoscopic evaluation indices for the main skeleton are proposed. The results indicate that aggregates with particle sizes of 2.36–4.75 mm can effectively increase the number of contact points; however, the stability of the main skeleton remains insufficient. Furthermore, coarse aggregates with a particle size larger than 4.75 mm provide the most significant contribution to the formation of a steady main skeleton; this is the critical particle size for the formation of a steadier main skeleton. Gradation is the major determinant of mesoscopic evaluation indices, including average coordination number ( n ¯ c ) and the ratio of the quantity of coarse aggregates without contact points to the total quantity of coarse aggregates (C value) for the asphalt mixture of the main skeleton. On the other hand, the performance of asphalt has an insignificant influence on mesoscopic evaluation indices; it mainly affects the development trend of macroscopic rutting. In the design process of DSAM, it is necessary to optimise gradation with the aim of increasing n ¯ c and reducing the C value so as to enhance the load resistance capacity of the primary skeleton. When preparing asphalt mixture specimens using the wheel rolling method, the design criteria for the aforementioned indices are n ¯ c ≥ 1.5 and C ≤ 15%, which can be used as bases for the design of DSAM with a nominal maximum particle size of 13.2 mm to ensure that the coarse aggregates are interlocked and form a steady main skeleton.

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Using discrete element models to track movement of coarse aggregates during compaction of asphalt mixture

Cited by 86 publications

References 29 publications

Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

Aggregate-Bitumen Interface Enhancement Mechanism of Utilization of Oil Shale Waste as Fine Aggregate in Open Grade Friction Course

Gradual Meso-Structural Response Behaviour of Characteristics of Asphalt Mixture Main Skeleton Subjected to Load

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