首页|Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

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  • 国家科技期刊平台
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The compaction quality of subgrade filler strongly affects subgrade settlement.The main objective of this research is to analyze the macro-and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles.First,an improved Viola-Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler.Shape indexes of 2D subgrade filler are then computed and statistically analyzed.Finally,numerical simulations are performed to quantitatively investigate the effects of the aspect ratio(AR)and interparticle friction coefficient(μ)on the macro-and micro-mechanical compaction charac-teristics of subgrade filler based on the discrete element method(DEM).The results show that with the increasing AR,the coarse particles are narrower,leading to the increasing movement of fine particles during compaction,which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles.Moreover,the average displacement of particles is strongly influenced by the AR,indicating that their occlusion under power relies on particle shapes.The dis-placement and velocity of fine particles are much greater than those of the coarse particles,which shows that compaction is primarily a migration of fine particles.Under the cyclic load,the interparticle friction coefficient μ has little effect on the internal structure of the sample;under the quasi-static loads,however,the increase in μ will lead to a significant increase in the porosity of the sample.This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.

Subgrade filler particlesDeep learning particleShape analysisParticle libraryCompaction characteristicsDiscrete element method(DEM)

Taifeng Li、Kang Xie、Xiaobin Chen、Zhixing Deng、Qian Su

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Railway Engineering Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China

Department of Civil Engineering,Central South University,Changsha 410075,China

School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China

National Key Research and Development Program'Transportation Infrastructure'project

2022YFB2603400

2024

10.1007/s40534-023-00325-1

铁道工程科学(英文)
西南交通大学

铁道工程科学(英文)

影响因子：0.403
ISSN：2662-4745
年,卷(期)：2024.32(2)
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