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1 Ergebnisse
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A performance-based hybrid deep learning model for predicti..:
Yu, Sihao
;
Zhang, Zixin
;
Wang, Shuaifeng
..
Journal of Rock Mechanics and Geotechnical Engineering, 1674-7755, 2023, 16:1, s. 65-80. , 2023
Link:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-508641
RT Journal T1 <Title>A performance-based hybrid deep learning model for predicting TBM advance rate using attention-ResNet-LSTM</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftuppsalauniv:oai:DiVA.org:uu-508641&Exemplar=1&LAN=DE A1 Yu, Sihao A1 Zhang, Zixin A1 Wang, Shuaifeng A1 Huang, Xin A1 Lei, Qinghua PB Uppsala universitet, Luft-, vatten- och landskapslära; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, China;Department of Earth Sciences, ETH Zürich, Zürich, Switzerland YR 2023 K1 Tunnel boring machine (TBM) K1 Advance rate K1 Deep learning K1 Attention-ResNet-LSTM K1 Evolutionary polynomial regression K1 Geotechnical Engineering K1 Geoteknik JF Journal of Rock Mechanics and Geotechnical Engineering, 1674-7755, 2023, 16:1, s. 65-80 LK http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-508641 DO http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-508641 SF ELIB - SuUB Bremen
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