Abstract
The parameters of the constitutive models used in the design of rockfill dams are associated with a high degree of uncertainty. This occurs because rockfill dams are comprised of numerous zones, each with different soil materials, and it is not feasible to extract materials from such structures to accurately ascertain their behavior or their respective parameters. The general approach involves laboratory tests using small material samples or empirical data from the literature. However, such measures lack an accurate representation of the actual scenario, resulting in uncertainties. This limits the suitability of the model in the design process. Inverse analysis provides an option to better understand dam behavior. This procedure involves the use of real monitored data, such as deformations and stresses, from the dam structure via installed instruments. Fundamentally, it is a non-destructive approach that considers optimization methods and actual performance data to determine the values of the parameters by minimizing the differences between simulated and observed results. This paper considers data from an actual rockfill dam and proposes a surrogate assisted non-deterministic framework for its inverse analysis. A suitable error/objective function that measures the differences between the actual and simulated displacement values is defined first. Non-deterministic algorithms are used as the optimization technique, as they can avoid local optima and are more robust when compared to the conventional deterministic methods. Three such approaches, the genetic algorithm, differential evolution, and particle swarm optimization are evaluated to identify the best strategy in solving problems of this nature. A surrogate model in the form of a polynomial regression is studied and recommended in place of the actual numerical model of the dam to reduce computation cost. Finally, this paper presents the relevant dam parameters estimated by the analysis and provides insights into the performance of the three procedures to solve the inverse problem.
Highlights
Introduction published maps and institutional affilThe design of Rockfill dams presents significant challenges to geotechnical engineers because of the uncertainties and the complex structural and material characteristics involved [1]
The presence of uncertainties has always posed a challenge in the design of rockfill The presence of uncertainties has always posed a challenge in the design of rockfill dams, and traditional approaches are not very effective in resolving them
Recent adin computing power and in the ability of modern instruments to collect data (often in vances in computing power and in the ability of modern instruments to collect data from rockfill dams have made techniques based on optimization and statistical in real-time) from rockfill dams have made techniques based on optimization and statismethods much more attractive
Summary
Introduction published maps and institutional affilThe design of Rockfill dams presents significant challenges to geotechnical engineers because of the uncertainties and the complex structural and material characteristics involved [1]. Computational methods have played an important role in addressing the associated difficulties These methods have been successful in providing better dam designs that are more reliable and have helped in reducing the cost and time of dam construction. Such approaches usually involve the development of numerical models, and the application of the finite element method (FEM) in this discipline has become the norm. This successful use of the FEM could be attributed to its ability to provide a high degree of accuracy and to effectively deal with complex geometries and boundary conditions as well as material (rock/soil) nonlinearities [2,3]. The lack of information pertaining to material properties, external factors such as weather, and iations
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