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Machine Learning: A Novel Approach to Predicting Slope Instabilities
Joint Authors
Kothari, Upasna Chandarana
Momayez, Moe
Source
International Journal of Geophysics
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Geomechanical analysis plays a major role in providing a safe working environment in an active mine.
Geomechanical analysis includes but is not limited to providing active monitoring of pit walls and predicting slope failures.
During the analysis of a slope failure, it is essential to provide a safe prediction, that is, a predicted time of failure prior to the actual failure.
Modern-day monitoring technology is a powerful tool used to obtain the time and deformation data used to predict the time of slope failure.
This research aims to demonstrate the use of machine learning (ML) to predict the time of slope failures.
Twenty-two datasets of past failures collected from radar monitoring systems were utilized in this study.
A two-layer feed-forward prediction network was used to make multistep predictions into the future.
The results show an 86% improvement in the predicted values compared to the inverse velocity (IV) method.
Eighty-two percent of the failure predictions made using ML method fell in the safe zone.
While 18% of the predictions were in the unsafe zone, all the unsafe predictions were within five minutes of the actual failure time, all practical purposes making the entire set of predictions safe and reliable.
American Psychological Association (APA)
Kothari, Upasna Chandarana& Momayez, Moe. 2018. Machine Learning: A Novel Approach to Predicting Slope Instabilities. International Journal of Geophysics،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1172982
Modern Language Association (MLA)
Kothari, Upasna Chandarana& Momayez, Moe. Machine Learning: A Novel Approach to Predicting Slope Instabilities. International Journal of Geophysics No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1172982
American Medical Association (AMA)
Kothari, Upasna Chandarana& Momayez, Moe. Machine Learning: A Novel Approach to Predicting Slope Instabilities. International Journal of Geophysics. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1172982
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172982