Spatiotemporal deep learning approach on estimation of diaphragm wall deformation induced by excavation

This paper proposes a convolution neural network (CNN) based prediction method for concrete diaphragm wall (CDW) deflections. CNN algorithm is modified for processing the CDW deformation data collected from in-situ measurement in both time and space dimensions, and capable of making dynamic predicti...

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Veröffentlicht in:	Acta geotechnica Jg. 16; H. 11; S. 3631 - 3645
Hauptverfasser:	Zhao, Hua-jing, Liu, Wei, Shi, Pei-xin, Du, Jiang-tao, Chen, Xiu-ming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021 Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Back propagation networks Complex Fluids and Microfluidics Construction accidents & safety Construction industry Convolution Deep learning Deflection Deformation Diaphragm wall Dimensions Engineering Excavation Feature extraction Foundations Geoengineering Geotechnical Engineering & Applied Earth Sciences Hydraulics Long short-term memory Machine learning Measurement Neural networks Occupational safety Predictions Research Paper Safety management Soft and Granular Matter Soil Science & Conservation Solid Mechanics Diaphragm wall Deformation Deep excavation Deep learning prediction
ISSN:	1861-1125, 1861-1133
Online-Zugang:	Volltext
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