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

作　　者：Taifeng Li Kang Xie Xiaobin Chen Zhixing Deng Qian Su 

机构地区：[1]Railway Engineering Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China [2]Department of Civil Engineering,Central South University,Changsha 410075,China [3]School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China

出　　处：《Railway Engineering Science》2024年第2期194-210,共17页铁道工程科学（英文版）

基　　金：This work was supported by the National Key R&D Program‘Transportation Infrastructure’project(No.2022YFB2603400).

摘　　要：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 characteristics 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 coefficientμhas little effect on the internal structure of the sample;under the quasi-static loads,however,the increase inμwill 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.

关 键 词：Subgrade filler particles Deep learning particle Shape analysis Particle library Compaction characteristics Discrete element method(DEM) 

分 类 号：U213.1[交通运输工程—道路与铁道工程]