Probabilistic Slope Stability Analysis of Mount St. Helens Using Scoops3D and a Hybrid Intelligence Paradigm

Sumit Kumar, Shiva Shankar Choudhary, Avijit Burman, Raushan Kumar Singh, Abidhan Bardhan () and Panagiotis G. Asteris
Additional contact information
Sumit Kumar: Department of Civil Engineering, National Institute of Technology, Patna 800005, India
Shiva Shankar Choudhary: Department of Civil Engineering, National Institute of Technology, Patna 800005, India
Avijit Burman: Department of Civil Engineering, National Institute of Technology, Patna 800005, India
Raushan Kumar Singh: Department of Computer Engineering and Applications, GLA University, Mathura 281406, India
Abidhan Bardhan: Department of Civil Engineering, National Institute of Technology, Patna 800005, India
Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece

Mathematics, 2023, vol. 11, issue 18, 1-23

Abstract: In the past, numerous stratovolcanoes worldwide witnessed catastrophic flank collapses. One of the greatest risks associated with stratovolcanoes is a massive rock failure. On 18 May 1980, we witnessed a rock slope failure due to a volcano eruption, and a 2185.60 m high rock slope of Mount St. Helens was collapsed. Thus, from the serviceability perspective, this work presents an effective computational technique to perform probabilistic analyses of Mount St. Helens situated in Washington, USA. Using the first-order second-moment method, probability theory and statistics were employed to map the uncertainties in rock parameters. Initially, Scoops3D was used to perform slope stability analysis followed by probabilistic evaluation using a hybrid computational model of artificial neural network (ANN) and firefly algorithm (FF), i.e., ANN-FF. The performance of the ANN-FF model was examined and compared with that of conventional ANN and other hybrid ANNs built using seven additional meta-heuristic algorithms. In the validation stage, the proposed ANN-FF model was the best-fitted hybrid model with R 2 = 0.9996 and RMSE = 0.0042. Under seismic and non-seismic situations, the reliability index and the probability of failure were estimated. The suggested method allows for an effective assessment of the failure probability of Mount St. Helens under various earthquake circumstances. The developed MATLAB model is also attached as a supplementary material for future studies.

Keywords: reliability analysis; rock slope stability; Scoops3D; artificial neural network; swarm intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/18/3809/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/18/3809/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:18:p:3809-:d:1233420

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().