Spatiotemporal deep learning approach on estimation of diaphragm wall deformation induced by excavation
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Title
Spatiotemporal deep learning approach on estimation of diaphragm wall deformation induced by excavation
Authors
Keywords
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Journal
Acta Geotechnica
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-06-15
DOI
10.1007/s11440-021-01264-z
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- Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization
- (2008) Amvrossios C. Bagtzoglou et al. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
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