Abstract
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.
Highlights
A real rockfill dam was selected for a case study in order to illustrate the application of the surrogate modeling methodology for a global sensitivity analysis and an uncertainty analysis
To assess the accuracy of these models, we examine the residual errors (the root mean square error (RMSE) and the coefficient of determination ( R2 ))
This paper contributes to the sensitivity and uncertainty analysis for rockfill dams using the surrogate modeling approach
Summary
Hariri-Ardebili, Fernando Salazar, Farhad Pourkamali-Anaraki, Guido Mazzà and Juan Mata. The use of sensitivity analysis has attracted the interest of engineers seeking to understand the complex behavior associated with soil parameters. Numerous techniques have been developed for obtaining Sobol indices through variants of the Monte Carlo sampling technique [6] and variance-based global sensitivity analysis are performed to identify the parameters that most affect the dam stability [7], these techniques for sensitivity analysis often require a large number of simulations [8]. Polynomial chaos expansion based surrogate models have recently been used for the sensitivity analysis of dams [14]. Finite element method models (FEM) with appropriate soil parameters are often utilized for dam modeling and design [15,16,17]. The surrogate models are trained by utilizing an error function that measures the difference between the computed and measured displacements on the inclinometers
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