Slope Failure Prediction Study based on LSTM Autoencoder

Jun-Hwi Cho; Jun-Hyuk Choi; Yong-Seong Kim; Jae-Heum Yeon; Seung-Joo Lee; Bong-Jun Ji

doi:10.22698/jales.20220034

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2022 Vol.34, Issue 3 Preview Page Next Page

Research Article

Slope Failure Prediction Study based on LSTM Autoencoder LSTM Autoencoder 기반의 사면 붕괴 예측 연구

31 December 2022. pp. 345-353

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Abstract

Numerous researchers have evaluated the stability of the slope, as its failure can cause severe damage to life and property. To this end, real-time data were collected and analyzed to evaluate the real-time risk in various slopes. However, most of the previous studies have either derived an empirical regression equation or relied on statistical methodologies, including statistical process control (SPC). In this study, a machine learning model, long short-term memory network (LSTM) Autoencoder, based on slope failure prediction was developed and validated by applying it to data on actual slope failure. As a result, it was possible to detect signs of collapse earlier than the SPC method used in previous studies and is expected to ensure lead time to cope with such collapse.

Keywords

Autoencoder

LSTM

Machine learning

Slope failure

Stability assessment

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Information

Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
Publisher(Ko) :강원대학교 농업생명과학대학 농업생명과학연구원
Journal Title :Journal of Agricultural, Life and Environmental Sciences
Journal Title(Ko) :농업생명환경연구
Volume : 34
No :3
Pages :345-353
Received Date : 2022-11-23
Revised Date : 2022-11-25
Accepted Date : 2022-11-25
DOI :https://doi.org/10.22698/jales.20220034

Journal of Agricultural, Life and Environmental Sciences ISSN:2233-8322(Print) 2508-870X(Online) 농업생명환경연구

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