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A platform for research: civil engineering, architecture and urbanism
Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties
https://orcid.org/0000-0002-9907-0193
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 the literature. This paper presents a deep learning technique for an efficient geotechnical reliability analysis with multiple uncertainties. The proposed method involves using convolutional neural networks (CNNs) as metamodels of the physics-based simulation model of a geotechnical system. In the present study, the spatially variable soil properties and the external loads are simultaneously considered in the analysis of a geotechnical system. The proposed neural network method configures these uncertainties to form a multi-channel “image.” CNNs can then simultaneously learn high-level features that contain information about the multiple uncertainties. With an appropriate architecture and adequate training, the trained CNNs can replace the computationally demanding physics-based simulation model for Monte Carlo simulations. Application of the neural network method is illustrated using a synthetic geotechnical example. The results reveal that the proposed neural network method effectively handles multiple uncertainties and efficiently predicts a failure probability value that is in good agreement with the benchmark result obtained using direct Monte Carlo simulations.
Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties
https://orcid.org/0000-0002-9907-0193
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 the literature. This paper presents a deep learning technique for an efficient geotechnical reliability analysis with multiple uncertainties. The proposed method involves using convolutional neural networks (CNNs) as metamodels of the physics-based simulation model of a geotechnical system. In the present study, the spatially variable soil properties and the external loads are simultaneously considered in the analysis of a geotechnical system. The proposed neural network method configures these uncertainties to form a multi-channel “image.” CNNs can then simultaneously learn high-level features that contain information about the multiple uncertainties. With an appropriate architecture and adequate training, the trained CNNs can replace the computationally demanding physics-based simulation model for Monte Carlo simulations. Application of the neural network method is illustrated using a synthetic geotechnical example. The results reveal that the proposed neural network method effectively handles multiple uncertainties and efficiently predicts a failure probability value that is in good agreement with the benchmark result obtained using direct Monte Carlo simulations.
Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties
https://orcid.org/0000-0002-9907-0193
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 the literature. This paper presents a deep learning technique for an efficient geotechnical reliability analysis with multiple uncertainties. The proposed method involves using convolutional neural networks (CNNs) as metamodels of the physics-based simulation model of a geotechnical system. In the present study, the spatially variable soil properties and the external loads are simultaneously considered in the analysis of a geotechnical system. The proposed neural network method configures these uncertainties to form a multi-channel “image.” CNNs can then simultaneously learn high-level features that contain information about the multiple uncertainties. With an appropriate architecture and adequate training, the trained CNNs can replace the computationally demanding physics-based simulation model for Monte Carlo simulations. Application of the neural network method is illustrated using a synthetic geotechnical example. The results reveal that the proposed neural network method effectively handles multiple uncertainties and efficiently predicts a failure probability value that is in good agreement with the benchmark result obtained using direct Monte Carlo simulations.
Deep Learning for Geotechnical Reliability Analysis with Multiple Uncertainties
J. Geotech. Geoenviron. Eng.
Wang, Ze Zhou (author)
2022-04-01
Article (Journal)
Electronic Resource
English
Geotechnical Uncertainties and Reliability-Based Design
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Uncertainties in Geotechnical Data
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Uncertainties in Geotechnical Design
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Minimizing Uncertainties in Geotechnical Investigations
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Minimizing Uncertainties in Geotechnical Investigations
| British Library Conference Proceedings | 1996