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

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.

Research on the modified surrogate model based on local RBF for concrete dam static and dynamic response analysis

“…This method identifies the elastic modulus of materials to determine whether a dam will fail. Das and Soulaimani. (2021) established a surrogate model in the form of polynomial regression and used GA, FDTD, and PSO to identify the relevant parameters of the dam.…”

Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model

“…In analyzing complex engineering problems, polynomial response surface (PRS), RBF, Kriging and SVR are the primary examples widely used to establish surrogate models. For example, Das [16] used PRS to establish a surrogate model that characterizes the relationship between a rockfill dam's mechanical parameters and displacement response; Tullu [17] developed an RBF-based surrogate model for predicting inter-ply shear stresses during composite press forming; Wang [18] introduced the SBO method to the arch dam body design and developed a Kriging-based surrogate model for body shape parameters optimization; Chen [19] proposed a surrogate model based on SVR for identifying the thermophysical parameters of aircraft components. Meanwhile, applications of the SBO approach for thermal parameter inversion are also notable.…”

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

“…Several studies in geotechnical engineering have been carried out, such as that examining the contact between the soils and concrete used in earth and rockfill dams [ 1 ]. Inverse analysis provides an means to better understand dam behavior [ 2 ], and offers measures of surface roughness, apparent porosity, apparent density, water absorption, and uniaxial compression strength (UCS), as well as an understanding of the influence of the heating rate and cooling process of gneiss stone [ 3 ].…”