Non-Deterministic Methods and Surrogates in the Design of Rockfill Dams

The parameters of the constitutive models used in design rockfill dams are associated with a high degree uncertainty. This occurs because comprised numerous zones, each different soil materials, and it is not feasible to extract materials from such structures accurately ascertain their behavior or respective parameters. general approach involves laboratory tests using small material samples empirical data literature. However, measures lack an accurate representation actual scenario, resulting uncertainties. limits suitability model process. Inverse analysis provides option better understand dam behavior. procedure use real monitored data, as deformations stresses, structure via installed instruments. Fundamentally, non-destructive that considers optimization methods performance determine values by minimizing differences between simulated observed results. paper proposes surrogate assisted non-deterministic framework for its inverse analysis. A suitable error/objective function displacement defined first. Non-deterministic algorithms technique, they can avoid local optima more robust when compared conventional deterministic methods. Three approaches, genetic algorithm, differential evolution, particle swarm evaluated identify best strategy solving problems this nature. form polynomial regression studied recommended place numerical reduce computation cost. Finally, presents relevant estimated insights into three procedures solve problem.