Optimization of Geometric Parameters for Double-Arch Dams through Bayesian Implementation
Publication: Journal of Structural Engineering
Volume 146, Issue 12
Abstract
This paper comprises the search of the optimal geometric parameters (area and volume) for double-arch dams. The approach is structured in several consecutive stages. The process begins with a definition of the problem about Bayesian estimators, to define the dam-shape design values. After that, an iterative sequence of equations calculation was developed until reaching a solution that satisfies the a priori established constraints. A modeling of the optimized dam has been carried out to estimate static and dynamic internal stresses. Data were retrieved from inventories of existing dams, whereas to obtain the unknown data, a Gaussian distribution under hypotheses of the Bayes’ theorem has been employed. This theorem converts the a priori distribution, through unknown parameters, into the a posteriori distribution providing expected estimators. The design of the dam shape is strongly based on the experience, therefore by collecting real information about existing dams a more accurate analysis is possible.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The first author acknowledges the Servicios Informáticos CPD of the University of Salamanca for the Wolfram Mathematica license and the University of Salamanca for paying the rights (when applicable) to completely download all the references. The study was funded by Support Program for Foreign Students of Doctorate (PAEDEX) and Ibero-American University Postgraduate Association (AUIP) (Reference No. 3.224.803.003.569). This study was also funded by doctoral school Studii Salamantini of University of Salamanca (Reference No. 100015235810).
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Published In
Journal of Structural Engineering
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Oct 26, 2019
Accepted: May 29, 2020
Published online: Sep 21, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 21, 2021
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