D.J. Jerez

TL;DR: The reliability-based design of structural dynamic systems under stochastic excitation is presented, and a kriging meta-model is selected in the present formulation, which generates a set of nearly optimal solutions uniformly distributed over a neighborhood of the optimal solution set.

TL;DR: In this article, the authors present a brief survey on some of the latest developments in the area of reliability-based design optimization of structural systems under stochastic excitation, which can be grouped into three main categories, namely, sequential optimization approaches, search based techniques, and schemes based on augmented reliability spaces.

TL;DR: A Two-Phase approach based on Bayesian model updating is considered for obtaining the optimal designs, and the population-based stochastic optimization algorithm generates a set of nearly optimal solutions uniformly distributed over the vicinity of the optimal solution set.

TL;DR: In this article, an efficient framework for reliability-based design optimization (RBDO) of structural systems is proposed, which makes full use of an established functional relationship between the probability of failure and the distribution design parameters, which is termed as the failure probability function (FPF).

TL;DR: A Bayesian model updating approach for handling contaminant source characterization problems in the context of water distribution networks that provides additional insight into the current network state in terms of posterior samples of the parameters that describe the contaminant event.