Influence of particle shape on aggregate mixture’s performance: DEM results

Zhou, Chunxiang; Zhang, Miaomiao; Li, Yuhua; Lu, Jiale; Chen, Jingyun

doi:10.1080/14680629.2017.1396236

Cited by 35 publications

(8 citation statements)

References 26 publications

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“…Most frequently, the type and general strength properties of the feed are taken into consideration at the starting point of the optimization approach. However, results of various investigations show that particle shape of aggregate product [25,26] significantly influences the qualitative characteristics of concrete mixes and cement mortals with adding of such aggregate. It is, however, worth mentioning that the problem of control the crushing products characteristics-is the subject of many studies.…”

Section: Problem Significance and Research Motivationmentioning

confidence: 99%

Optimization of Aggregate Production Circuit through Modeling of Crusher Operation

Gawenda

Saramak

2022

Minerals

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The paper concerns investigation of the effect of impact crusher operation on selected qualitative characteristics of mineral aggregate products. Qualitative characteristics of crushing products in terms of size reduction ratio and fine particles contents were analyzed from the point of view of operational parameters of the impact crusher. An investigative program was carried out on a plant scale and two primary parameters of the impactor were analyzed: velocity of the crusher rotor and the width of the outlet gap. The models of the crushing device operation were built separately for each type of the tested material, as well as for general conditions.

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Section: Problem Significance and Research Motivationmentioning

confidence: 99%

Optimization of Aggregate Production Circuit through Modeling of Crusher Operation

Gawenda

Saramak

2022

Minerals

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“…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). In this way, the maximum possible gradation scopes of different maximum nominal particle sizes are obtained finally, as shown in Tables 1 and 2.…”

Section: Determination Of Possible Gradation Scopementioning

confidence: 99%

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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“…To simplify the micromechanical modeling of LTPAs, the mix was divided into three different individual constituents: (i) Coarse aggregate particles larger than 2.0 mm, (ii) asphalt mortar, and (iii) air voids. Considering that the shape of the coarse aggregates, which formed the aggregate skeleton in the mix, has a major impact on mix compaction and performance [22][23][24], five different particles with complex geometries ( Figure 3) were developed to compose the PA 0/16 material. The shape of each aggregate particle was randomly chosen from these five shapes.…”

Section: Micromechanical Modeling Of Asphalt MIXmentioning

confidence: 99%

Induction Heating-Assisted Compaction in Porous Asphalt Pavements: A Computational Study

et al. 2018

Self Cite

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Low temperature asphalt (LTA) technologies, such as warm-mixed asphalt mixes, are utilized in the paving industry to lower energy demands and greenhouse gas emissions during asphalt mixing and pavement construction. However, the asphalt mixes developed that incorporate LTA additives are more sensitive than hot-mixed asphalts to temperature reduction during compaction, which leads to inadequate compaction and subsequent poor pavement performance. The induction heating-assisted compaction of pavement structures appears to be an effective way to ameliorate such issues and to improve mix compactability at lower temperatures. Considering that induction-assisted compaction is a complex process, a computational methodology is proposed in this paper. A porous asphalt concrete mix was considered as case material. For the pavement compaction analyses after induction, the temperature field generated by electromagnetic induction was predicted and the material parameters of asphalt mortar were adjusted. The effect of induction heating on asphalt compaction effectiveness, the tendency of mix density changing, the increase in compactor passes, and the influence of temperature on compaction at different locations in the pavement were studied as well.

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Influence of particle shape on aggregate mixture’s performance: DEM results

Cited by 35 publications

References 26 publications

Optimization of Aggregate Production Circuit through Modeling of Crusher Operation

Optimization of Aggregate Production Circuit through Modeling of Crusher Operation

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

Induction Heating-Assisted Compaction in Porous Asphalt Pavements: A Computational Study

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