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Article

Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam

Department of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre-Dame W., Montréal, QC H3C 1K3, Canada
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Academic Editors: M. Amin Hariri-Ardebili, Fernando Salazar, Farhad Pourkamali-Anaraki, Guido Mazzà and Juan Mata
Water 2021, 13(13), 1830; https://doi.org/10.3390/w13131830
Received: 29 April 2021 / Revised: 27 June 2021 / Accepted: 27 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study performs an uncertainty analysis and a global sensitivity analysis to assess the effect of constitutive soil parameters on the behavior of a rockfill dam. A Finite Element code (Plaxis) is utilized for the structure analysis. A database of the computed displacements at inclinometers installed in the dam is generated and compared to in situ measurements. Surrogate models are significant tools for approximating the relationship between input soil parameters and displacements and thereby reducing the computational costs of parametric studies. Polynomial chaos expansion and deep neural networks are used to build surrogate models to compute the Sobol indices required to identify the impact of soil parameters on dam behavior. View Full-Text
Keywords: sensitivity analysis; polynomial chaos expansion; uncertainty; deep neural networks; rockfill dams sensitivity analysis; polynomial chaos expansion; uncertainty; deep neural networks; rockfill dams
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MDPI and ACS Style

Shahzadi, G.; Soulaïmani, A. Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam. Water 2021, 13, 1830. https://doi.org/10.3390/w13131830

AMA Style

Shahzadi G, Soulaïmani A. Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam. Water. 2021; 13(13):1830. https://doi.org/10.3390/w13131830

Chicago/Turabian Style

Shahzadi, Gullnaz, and Azzeddine Soulaïmani. 2021. "Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam" Water 13, no. 13: 1830. https://doi.org/10.3390/w13131830

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