Details
| Original language | English |
|---|---|
| Article number | 105802 |
| Journal | Engineering geology |
| Volume | 277 |
| Early online date | 6 Aug 2020 |
| Publication status | Published - Nov 2020 |
Abstract
Soil-rock mixtures (SRMs) are the main unfavorable geologic bodies in Southwest China. This paper presents a novel mesoscale computational modeling study of SRMs with concave aggregates. An efficient 3D mesoscale SRM generation method is proposed by combining the Gilbert–Johnson–Keerthi (GJK)-based collision detection technique, the border placement algorithm and the particle position selection method. A periodic mesh is generated based on the mesh mapping technique. A numerical homogenization analysis of an SRM with a large number of elements is realized, and the estimated parameters are validated by the experimental test results. The results indicate that SRMs with concave aggregates have a higher elastic modulus than those with convex aggregates. This method is helpful for predicting the physical properties of SRMs and has promising applications in engineering.
Keywords
- 3D mesoscale modeling, Concave particles, Numerical homogenization, Soil and rock mixture
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Geotechnical Engineering and Engineering Geology
- Earth and Planetary Sciences(all)
- Geology
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In: Engineering geology, Vol. 277, 105802, 11.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Three-dimensional mesoscale computational modeling of soil-rock mixtures with concave particles
AU - Meng, Qingxiang
AU - Wang, Huanling
AU - Cai, Ming
AU - Xu, Weiya
AU - Zhuang, Xiaoying
AU - Rabczuk, Timon
N1 - Funding Information: This study is financially supported by the National Key R&D Program of China ( 2018YFC0407004 ), the Fundamental Research Funds for the Central Universities ( B200201059 ), the National Natural Science Foundation of China (Grant Nos. 51709089 , 51609070 , 11572110 , 51479049 , 11771116 ), the China Postdoctoral Science Foundation Funded Project ( 2018T110434 ), and the 111 project .
PY - 2020/11
Y1 - 2020/11
N2 - Soil-rock mixtures (SRMs) are the main unfavorable geologic bodies in Southwest China. This paper presents a novel mesoscale computational modeling study of SRMs with concave aggregates. An efficient 3D mesoscale SRM generation method is proposed by combining the Gilbert–Johnson–Keerthi (GJK)-based collision detection technique, the border placement algorithm and the particle position selection method. A periodic mesh is generated based on the mesh mapping technique. A numerical homogenization analysis of an SRM with a large number of elements is realized, and the estimated parameters are validated by the experimental test results. The results indicate that SRMs with concave aggregates have a higher elastic modulus than those with convex aggregates. This method is helpful for predicting the physical properties of SRMs and has promising applications in engineering.
AB - Soil-rock mixtures (SRMs) are the main unfavorable geologic bodies in Southwest China. This paper presents a novel mesoscale computational modeling study of SRMs with concave aggregates. An efficient 3D mesoscale SRM generation method is proposed by combining the Gilbert–Johnson–Keerthi (GJK)-based collision detection technique, the border placement algorithm and the particle position selection method. A periodic mesh is generated based on the mesh mapping technique. A numerical homogenization analysis of an SRM with a large number of elements is realized, and the estimated parameters are validated by the experimental test results. The results indicate that SRMs with concave aggregates have a higher elastic modulus than those with convex aggregates. This method is helpful for predicting the physical properties of SRMs and has promising applications in engineering.
KW - 3D mesoscale modeling
KW - Concave particles
KW - Numerical homogenization
KW - Soil and rock mixture
UR - http://www.scopus.com/inward/record.url?scp=85089275342&partnerID=8YFLogxK
U2 - 10.1016/j.enggeo.2020.105802
DO - 10.1016/j.enggeo.2020.105802
M3 - Article
AN - SCOPUS:85089275342
VL - 277
JO - Engineering geology
JF - Engineering geology
SN - 0013-7952
M1 - 105802
ER -