Quantitative Investigation of Aggregate Skeleton Force Chains of Asphalt Mixtures Based on Computational Granular Mechanics

Joint Authors

Liu, Guoqiang
Han, Dongdong
Zhao, Yongli

Source

Advances in Civil Engineering

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

Civil Engineering

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