Citation

Liang X, He H, Zhang Y. Safety Sci. 2019; 118: 861-867.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ssci.2019.06.021

PMID

unavailable

Abstract

Landslides are one of the most important natural disasters at present. This paper uses machine learning to optimize the design of micro-piles, reinforce the expansive soil landslide, and achieve safety protection. The effective safety protection of the landslide is realized by machine learning design of pile arrangement, pile spacing, row spacing, anchoring depth, sizes and reinforcement bars of piles. It is found that when three-row piles are used to reinforce the expansive soil landslide, the proportional coefficient of the bearing sliding force of each expansive soil is 1:0.7:0.6, in which the distribution of sliding forces is calculated by the respective steel piles. Through the calculation of the overall stability of each pile, the shear bearing capacity and bearing capacity of the sliding surface, the rationality of the design is analyzed and finally verified by the engineering project.

Language: en

Keywords

Landslide safety protection; Machine learning; Micro-pile; Optimization design