- Title
- Stability prediction of residual soil and rock slope using artificial neural network
- Creator
- Paliwal, Mahesh; Goswami, Himkar; Ray, Arunava; Bharati, Ashutosh; Rai, Rajesh; Khandelwal, Manoj
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189460
- Identifier
- vital:17458
- Identifier
-
https://doi.org/10.1155/2022/4121193
- Identifier
- ISSN:1687-8086 (ISSN)
- Abstract
- A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of both, along the mountain slopes due to various natural or anthropogenic factors is known as a landslide. The Himalayan Mountain slopes are either made up of residual soil or rocks. Residual soil is formed from weathering of the bedrock and mainly occurs in gentle-to-moderate slope inclinations. In contrast, steep slopes are mostly devoid of soil cover and are primarily rocky. A stability prediction system that can analyse the slope under both the condition of the soil or rock surface is missing. In this study, artificial neural network technology has been utilised to predict the stability of jointed rock and residual soil slope of the Himalayan region. The database for the artificial neural network was obtained from numerical simulation of several residual soils and rock slope models. Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. It was observed that the developed android app provides promising results in predicting the factor of safety and stability state of the slopes. © 2022 Mahesh Paliwal et al. This is an open access article distributed under the Creative Commons Attribution License.
- Publisher
- Hindawi Limited
- Relation
- Advances in Civil Engineering Vol. 2022, no. (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022 Mahesh Paliwal et al.
- Rights
- Open Access
- Subject
- 4005 Civil engineering
- Full Text
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