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Geotechnical and Geological Engineering

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25-07-2021 | Original Paper

Reliability Analysis of Reinforced Slope Combining Random Forest Algorithm and Meshless SPH Algorithm

Authors: Xu Liu, Liang Li, ShangShang Wang, Fu Chen, Ming Zhai, Zhengquan Yang, Yuan Gao

Published in: Geotechnical and Geological Engineering | Issue 3/2022

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Abstract

This paper presents a framework combining a mesh-free method called Smoothed Particle Hydrodynamics (SPH) algorithm with Random Forest (RF) algorithm to evaluate the failure probability for reinforced slope. The reinforced slope model is established in Slope/w and Monte Carlo Simulation is implemented using Win-Batch to obtain the failure samples yielding reinforced slope failure with factor of safety < 1. The run-out distances corresponding to failure samples are modeled using SPH and these distances are based to build a new limit state function of reinforced slope failure. Then, RF algorithm is used to predict the state of the reinforced slope to enhance the computation efficiency. The proposed framework is illustrated through a roadside reinforced slope of Harbin to Jiamusi Railway to investigate the influence of retaining wall height on the failure probability of slope. The computation results show that RF algorithm exhibits a relative high accuracy in predicting the state of the reinforced slope according to its factors of safety, retaining wall heights and the corresponding run-out distances. It is found that as the retaining wall height increases, the failure probability of the reinforced slope decreases significantly. The proposed framework and the research outputs provide much insight into risk assessment and risk mitigation for reinforced slopes.

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Title: Reliability Analysis of Reinforced Slope Combining Random Forest Algorithm and Meshless SPH Algorithm
Authors: Xu Liu
Liang Li
ShangShang Wang
Fu Chen
Ming Zhai
Zhengquan Yang
Yuan Gao
Publication date: 25-07-2021
Publisher: Springer International Publishing
Published in: Geotechnical and Geological Engineering / Issue 3/2022
Print ISSN: 0960-3182
Electronic ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-021-01943-4

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