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Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties
Publisher: ASCE
Abstract: Apart from spatial variability of soil properties, a geotechnical system can have many other sources of uncertainties. To efficiently analyze such a system in a probabilistic manner, many strategies have been proposed in ...
Metamodel-Based Reliability Analysis in Spatially Variable Soils Using Convolutional Neural Networks
Publisher: ASCE
Abstract: In recent years, the random field finite-element method (FEM) has been used increasingly in geotechnical engineering to carry out analyses that account for the inherent spatial variability in the physical and mechanical ...
Deep Learning–Based Prediction of Tunnel Face Stability in Layered Soils Using Images of Random Fields
Publisher: American Society of Civil Engineers
Abstract: The stability analysis of tunnel faces in multilayered soils presents challenges due to the inherent variability in natural soils. Although the random field finite-element methods offer a reliable approach to address such ...