Deep learning methods for underground deformation time-series prediction

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openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2023-04-12
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Mcode
Degree programme
Language
en
Pages
7
2775-2781
Series
Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World, issue 1st Edition
Abstract
Prediction is a vague concept that is why we need to conceptualize it specifically for underground deformation time-series data. For this impending issue, this paper employs an advanced deep learning model Bi-LSTM-AM to address it. The results show its applicability for practical engineering. The proposed model is compared with other basic deep learning models including long short-term memory (LSTM), Bi-LSTM, gated recurrent units (GRU), and temporal convolutional networks (TCN). These models cover the most common three forms of deep learning for time-series prediction: recurrent neural networks (RNN) and convolutional neural networks (CNN). This research is supposed to benefit the underground deformation time-series prediction.
Description
Keywords
underground engineering, time-series, deep learning, deformation prediction, machine learning
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Citation
Ma, E, Janiszewski, M & Torkan, M 2023, Deep learning methods for underground deformation time-series prediction . in G Anagnostou, A Benardos & V P Marinos (eds), Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023 : Proceedings of the ITA-AITES World Tunnel Congress 2023 (WTC 2023), 12-18 May 2023, Athens, Greece . 1st Edition edn, CRC Press, London, pp. 2775-2781, World Tunnel Congress, Athens, Greece, 12/05/2023 . https://doi.org/10.1201/9781003348030-334