eCite Digital Repository

Prediction and classification for finite element slope stability analysis by random field comparison


Dyson, AP and Tolooiyan, A, Prediction and classification for finite element slope stability analysis by random field comparison, Computers and Geotechnics, 109 pp. 117-129. ISSN 0266-352X (2019) [Refereed Article]

Copyright Statement

2019 Elsevier Ltd. All rights reserved.

DOI: doi:10.1016/j.compgeo.2019.01.026


This paper considers probabilistic slope stability analysis using the Random Finite Element Method (RFEM) combined with processes to determine the level of similarity between random fields. A procedure is introduced to predict the Factor of Safety (FoS) of individual Monte Carlo Method (MCM) random field instances prior to finite element simulation, based on random field similarity measures. Previous studies of probabilistic slope stability analysis have required numerous MCM instances to reach FoS convergence. However, the methods provided in this research drastically reduce computational processing time, allowing simulations previously considered too computationally expensive for MCM analysis to be simulated without obstacle. In addition to computational efficiency, the comparison based procedure is combined with cluster analysis methods to locate random field characteristics contributing to slope failure. Comparison measures are presented for slope geometries of an Australian open pit mine to consider the impacts of associated factors such as groundwater on random field similarity predictors, while highlighting the capacity of the similarity procedure for prediction, classification and computational efficiency.

Item Details

Item Type:Refereed Article
Keywords:random field, slope stability, random finite element method, RFEM, probabilistic methods, clustering analysis
Research Division:Engineering
Research Group:Civil engineering
Research Field:Civil geotechnical engineering
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Dyson, AP (Dr Ashley Dyson)
UTAS Author:Tolooiyan, A (Dr Ali Tolooiyan)
ID Code:131115
Year Published:2019
Web of Science® Times Cited:45
Deposited By:Engineering
Deposited On:2019-03-04
Last Modified:2022-08-29

Repository Staff Only: item control page