Quantitative Investigation of Aggregate Skeleton Force Chains of Asphalt Mixtures Based on Computational Granular Mechanics
Joint Authors
Liu, Guoqiang
Han, Dongdong
Zhao, Yongli
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-11
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
For asphalt mixtures, the difference between strong force chains (SCF) can reflect the skeleton performance.
In this paper, six kinds of mineral mixture discrete element model were established.
And various SCF evaluation indices of different mineral mixtures were calculated.
Results indicate that the short length SCF number proportions of dense-skeleton type mineral mixtures are higher than that of dense-suspended type mineral mixtures under the same nominal maximum aggregate size (NMAS).
And the NMAS has a great influence on the SCF length cumulative proportions, and different NMAS can significantly change the stress transfer path for dense-suspended type mixture.
Nevertheless, the SCF length cumulative proportions have consistency for dense-skeleton type mixtures.
The small SCF alignment coefficient proportions of dense-suspended type mixtures are higher than that of dense-skeleton type mixtures.
In particular, under larger NMAS, the difference is more obvious.
The SCF that is close to straight line is conducive to transfer loading.
Therefore, dense-skeleton type mixture has better rutting resistance.
The SCF bears the main loading for mixtures.
Mixtures stone matrix asphalt (SMA) has a stronger bearing capacity than that of mixtures AC under the same NMAS.
These findings provide insight into the mechanics of skeleton structure.
American Psychological Association (APA)
Liu, Guoqiang& Han, Dongdong& Zhao, Yongli. 2020. Quantitative Investigation of Aggregate Skeleton Force Chains of Asphalt Mixtures Based on Computational Granular Mechanics. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1121100
Modern Language Association (MLA)
Liu, Guoqiang…[et al.]. Quantitative Investigation of Aggregate Skeleton Force Chains of Asphalt Mixtures Based on Computational Granular Mechanics. Advances in Civil Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1121100
American Medical Association (AMA)
Liu, Guoqiang& Han, Dongdong& Zhao, Yongli. Quantitative Investigation of Aggregate Skeleton Force Chains of Asphalt Mixtures Based on Computational Granular Mechanics. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1121100
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121100