Study on the dynamic response characteristics of loess slope under traffic load based on numerical modelling
Year: 2024
Type: article
Abstract: This study aims to explore the dynamic response of loess slopes under traffic loads. By using finite difference software, a comprehensive numerical model of a typical loess slope was established. The analysis focused on understanding variations in slope deformation, vertical maximum acceleration, velocity, and displacement on the surface and inside of the slope under different axle loads and vehicle speeds. The results show that after applying traffic load, the shear strain increment zone of the slope extends from the toe to the crest. This extension indicates a potential development of the slip surface, and the instability of the slope begins with shear failure at the toe. As the distance from the load source increases, the vertical maximum acceleration, velocity, and displacement at each monitoring point on the surface and inside of the slope gradually decrease. Under the same speed and different axle load conditions, as the axle load increases, the vertical maximum velocity, acceleration, and displacement at each monitoring point on the surface and inside of the slope all show an increasing trend. Under fixed axle load and different speed conditions, the vertical maximum acceleration and velocity at each monitoring point inside and on the surface of the slope generally increase with the increase of vehicle speed, while the displacement gradually decreases with the increase of driving speed. The research findings have important theoretical significance and practical engineering application value for understanding the dynamic response of loess slopes to traffic load and road construction within loess regions.
Authors: Guoliang DU, Junjiang LIU, Lu CONG, Liying GU, Jingyu LE, Yanchao WANG
Institutions: Hebei GEO University, Shanxi University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6962547617
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