in this work an improved method for designing a linear vibrational absorber, excited by random vibrations is presented and analyzed. first, analytical expressions, for non-stationary white noise accelerations, are derived. the criterion is different from the conventional criteria, used for structural design under random vibration, and it is based on minimum displacement or acceleration response...

The purpose of the paper is to discuss the applicability of stochastic programming models and methods to civil engineering design problems. In cooperation with experts in civil engineering, the problem concerning an optimal design of beam dimensions has been chosen. The corresponding mathematical model involves an ODE-type constraint, uncertain parameter related to the material characteristics ...

Discrete-event simulation based optimization is the process of finding the optimum design of a stochastic system when the performance measure(s) could only be estimated via simulation. Randomness in simulation outputs often challenges the correct selection of the optimum. We propose an algorithm that merges Ranking and Selection procedures with a large class of random search methods for continu...

Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochasti...

Augmenting an existing network with additional links to achieve higher robustness and survivability plays an important role in network design. We consider the problem of augmenting a network with links of minimum total cost in order to make it edge-biconnected, i.e. the failure of a single link will never disconnect any two nodes. A new evolutionary algorithm is proposed that works directly on ...

This paper presents a sampling-based RBDO method using surrogate models. The Dynamic Kriging (D-Kriging) method is used for surrogate models, and a stochastic sensitivity analysis is introduced to compute the sensitivities of probabilistic constraints with respect to independent or correlated random variables. For the sampling-based RBDO, which requires Monte Carlo simulation (MCS) to evaluate ...

This study presents how to carry out RBDO when surrogate models are used to represent true performance functions. The Dynamic Kriging (D-Kriging) method is used to generate surrogate models, and stochastic sensitivity analysis is introduced to compute sensitivities of probabilistic constraints with respect to the design variables, which are the mean values of the input independent or correlated...