Deep learning methods for underground deformation time-series prediction

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

59 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.
AlkuperäiskieliEnglanti
OtsikkoExpanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023
AlaotsikkoProceedings of the ITA-AITES World Tunnel Congress 2023 (WTC 2023), 12-18 May 2023, Athens, Greece
ToimittajatGeorgios Anagnostou, Andreas Benardos, Vassilis P. Marinos
JulkaisupaikkaLondon
KustantajaCRC Press
Sivut2775-2781
Sivumäärä7
Painos1st Edition
ISBN (elektroninen)978-1-003-34803-0
DOI - pysyväislinkit
TilaJulkaistu - 12 huhtik. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaWorld Tunnel Congress - Athens, Kreikka
Kesto: 12 toukok. 202318 toukok. 2023

Conference

ConferenceWorld Tunnel Congress
LyhennettäWTC
Maa/AlueKreikka
KaupunkiAthens
Ajanjakso12/05/202318/05/2023

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