Metamodel-Based Reliability Analysis in Spatially Variable Soils Using Convolutional Neural Networks
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 147, Issue 3
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 properties of both natural and treated soils. However, this method, which usually is performed in tandem with Monte Carlo simulation (MCS), requires significantly greater computational resources than deterministic finite-element analysis. Metamodeling is one of the techniques commonly adopted to alleviate the computational burden. This paper proposes a novel and computationally efficient metamodeling technique that involves the use of convolutional neural networks (CNNs) to perform random field finite-element analyses. CNNs, which treat random fields as images, are capable of outputting FEM predicted quantities with learned high-level features that contain information about the random variabilities in both spatial distribution and intensity. CNNs, after being trained with sufficient random field samples, could be used as a metamodel to replace the expensive random field finite-element simulations for all subsequent calculations. The validity of the proposed approach was illustrated using a synthetic excavation problem and a synthetic surface footing problem. The good agreement between the CNN outputs and the FEM predictions demonstrated the promising potential of using CNNs as a metamodel for reliability analysis in spatially variable soils.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the National Research Foundation (NRF) Singapore (FI 370074011) and was conducted at the Future Cities Laboratory at the Singapore-ETH Centre (SEC). The SEC was established as a collaboration between ETH Zurich and the National Research Foundation (NRF) Singapore (FI 370074011) under the auspices of the NRF’s Campus for Research Excellence and Technological Enterprise (CREATE) program. The authors thank the anonymous reviewers for the constructive comments and questions raised that have helped to improve the quality and rigor of this work. In particular, the example and information provided by one of the reviewers in the discussion of the K-L terms was very helpful and is deeply appreciated.
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Journal of Geotechnical and Geoenvironmental Engineering
Volume 147 • Issue 3 • March 2021
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© 2021 American Society of Civil Engineers.
History
Received: Feb 3, 2020
Accepted: Nov 20, 2020
Published online: Jan 11, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 11, 2021
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