Comparison of different probabilistic methods for analyzing stability of underground rock excavations
Number of Authors: 3
2016 (English)In: The Electronic journal of geotechnical engineering, ISSN 1089-3032, E-ISSN 1089-3032, Vol. 21, no 21, 6555-6585 p.Article in journal (Refereed) Published
Stability analyses of underground rock excavations are often performed using traditional deterministic methods. In deterministic methods the mean or characteristics values of the input parameters are used for the analyses. These method neglect the inherent variability of the rock mass properties in the analyses and the results could be misleading. Therefore, for a realistic stability analyses probabilistic methods, which consider the inherent variability of the rock mass properties, are considered appropriate. A number of probabilistic methods, each based on different theories and assumptions have been developed for the analysis of geotechnical problems. Geotechnical engineers must therefore choose appropriate probabilistic method to achieve a specific objective while taking into account simplicity, accuracy and time efficiency. In this study finite difference method was combined with five different probabilistic methods to analyze the stability of an underground rock excavation. The probabilistic methods considered were the Point Estimate Method (PEM), the Response Surface Method (RSM), the Artificial Neural Network (ANN), the Monte Carlos Simulation (MCS), and the Strength Classification Method (SCM). The results and the relative merits of the methods were compared. Also the general advantages of the probabilistic method over the deterministic method were discussed. Though the methods presented in this study are not exhaustive, the results of this study will assist in the choice of appropriate probabilistic methods for the analysis of underground rock excavations.
Place, publisher, year, edition, pages
2016. Vol. 21, no 21, 6555-6585 p.
Geotechnical Engineering Other Civil Engineering
Research subject Mining and Rock Engineering
IdentifiersURN: urn:nbn:se:ltu:diva-59926ScopusID: 2-s2.0-84992509213OAI: oai:DiVA.org:ltu-59926DiVA: diva2:1039624
Validerad; 2016; Nivå 2; 2016-11-14 (andbra)2016-10-242016-10-242016-11-23Bibliographically approved