Abstract
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.
Highlights
Granular mixture is widely applied in industrial manufacturing, agricultural production and civil engineering, including powder metallurgy, food accumulation and building materials
In order to contain all possible proportions of coarse aggregates from 2.36 mm to maximum particle size, the recommended range of each particle size group was summarized on the basis of Chinese Construction Specification JTG F40-2004 [31], including various common gradation types: asphalt concrete (AC), asphalt macadam (AM), open graded friction course (OGFC) and stone mastic asphalt (SMA)
A porosity prediction model was established for granular mixtures through the Discrete Element Modeling (DEM) model and Back Propagation Neural Network algorithm (BPNN) algorithm
Summary
Granular mixture is widely applied in industrial manufacturing, agricultural production and civil engineering, including powder metallurgy, food accumulation and building materials. For these mixtures, porosity is one of the most crucial factors that has a considerable impact on mixture structure and performance. As demonstrated in the previous research [13,30], both gradations and particle shapes could have certain impacts on mixture volumetric properties. When both gradations and particle shapes are considered simultaneously, it is not easy to make a consistent conclusion. In this article the aggregate gradations were selected as the major control parameters, while the particle shapes were considered as minor parameters by introducing a reduction factor
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