Deep learning methods for underground deformation time-series prediction

Enlin Ma; Mateusz Janiszewski; Masoud Torkan

doi:10.1201/9781003348030-334

Deep learning methods for underground deformation time-series prediction

Enlin Ma, Mateusz Janiszewski, Masoud Torkan

Chang'an University

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

75 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023
Alaotsikko	Proceedings of the ITA-AITES World Tunnel Congress 2023 (WTC 2023), 12-18 May 2023, Athens, Greece
Toimittajat	Georgios Anagnostou, Andreas Benardos, Vassilis P. Marinos
Julkaisupaikka	London
Kustantaja	CRC Press
Sivut	2775-2781
Sivumäärä	7
Painos	1st Edition
ISBN (elektroninen)	978-1-003-34803-0
DOI - pysyväislinkit	https://doi.org/10.1201/9781003348030-334
Tila	Julkaistu - 12 huhtik. 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	World Tunnel Congress - Athens, Kreikka Kesto: 12 toukok. 2023 → 18 toukok. 2023

Conference

Conference	World Tunnel Congress
Lyhennettä	WTC
Maa/Alue	Kreikka
Kaupunki	Athens
Ajanjakso	12/05/2023 → 18/05/2023

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10.1201/9781003348030-334Lisenssi: CC BY-NC-ND

10.1201_9781003348030-334_chapterpdfFinal published version, 1,99 MBLisenssi: CC BY-NC-ND

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Ma, E., Janiszewski, M., & Torkan, M. (2023). Deep learning methods for underground deformation time-series prediction. teoksessa G. Anagnostou, A. Benardos, & V. P. Marinos (Toimittajat), 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 toim., Sivut 2775-2781). CRC Press. https://doi.org/10.1201/9781003348030-334

Ma, Enlin ; Janiszewski, Mateusz ; Torkan, Masoud. / Deep learning methods for underground deformation time-series prediction. 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. Toimittaja / Georgios Anagnostou ; Andreas Benardos ; Vassilis P. Marinos. 1st Edition. toim. London : CRC Press, 2023. Sivut 2775-2781

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title = "Deep learning methods for underground deformation time-series prediction",

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.",

keywords = "underground engineering, time-series, deep learning, deformation prediction, machine learning",

author = "Enlin Ma and Mateusz Janiszewski and Masoud Torkan",

year = "2023",

month = apr,

day = "12",

doi = "10.1201/9781003348030-334",

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publisher = "CRC Press",

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note = "World Tunnel Congress, WTC ; Conference date: 12-05-2023 Through 18-05-2023",

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Ma, E, Janiszewski, M & Torkan, M 2023, Deep learning methods for underground deformation time-series prediction. julkaisussa G Anagnostou, A Benardos & VP Marinos (toim), 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 toim, CRC Press, London, Sivut 2775-2781, World Tunnel Congress, Athens, Kreikka, 12/05/2023. https://doi.org/10.1201/9781003348030-334

Deep learning methods for underground deformation time-series prediction. / Ma, Enlin; Janiszewski, Mateusz ; Torkan, Masoud.
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. toim. / Georgios Anagnostou; Andreas Benardos; Vassilis P. Marinos. 1st Edition. toim. London: CRC Press, 2023. s. 2775-2781.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Deep learning methods for underground deformation time-series prediction

AU - Ma, Enlin

AU - Janiszewski, Mateusz

AU - Torkan, Masoud

PY - 2023/4/12

Y1 - 2023/4/12

N2 - 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.

AB - 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.

KW - underground engineering

KW - time-series

KW - deep learning

KW - deformation prediction

KW - machine learning

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BT - Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023

A2 - Anagnostou, Georgios

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A2 - Marinos, Vassilis P.

PB - CRC Press

CY - London

T2 - World Tunnel Congress

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Ma E, Janiszewski M , Torkan M. Deep learning methods for underground deformation time-series prediction. julkaisussa Anagnostou G, Benardos A, Marinos VP, toimittajat, 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 toim. London: CRC Press. 2023. s. 2775-2781 doi: 10.1201/9781003348030-334

Deep learning methods for underground deformation time-series prediction

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