Multi-objective design optimization of structural systems based on probabilistic life-cycle criteria through a sequential decision process

A challenge with incorporating life-cycle criteria into design optimization is the need to quantify time-varying reliability of structural systems deteriorating in uncertain and non-stationary environments. This paper presents a computational methodology that addresses this challenge by combining set-based design with multi-fidelity modeling to broadly and efficiently explore a diverse set of design alternatives while systematically converging to the set of Pareto optimal designs. The result is a framework for multi-objective design optimization of structural systems based on probabilistic life-cycle criteria through a sequential decision process (SDP). At each decision state, design alternatives are evaluated and compared using bounds on decision criteria, and dominated (less-promising) designs are eliminated from further evaluation. Computational efficiency is achieved by sequencing models of increasingly higher fidelity. The SDP accommodates multiple objectives, discrete design variables, varying structural concepts, accounts for redundancy and system reliability, and the risk attitude of decision maker(s). The efficacy of the methodology is demonstrated through numerical examples involving multi-objective design optimization of steel trusses, where the goal is to identify optimal design variables that simultaneously minimize the expected value of the life-cycle cost and the corresponding risk of deviation from the expected value. By sequencing models of increasing fidelity, SDP is shown to efficiently converge to the set of Pareto optimal designs using 0.125–0.151 times the number of model evaluations in comparison to full evaluation by the highest fidelity model. Furthermore, the influence of structural configuration, material grade, and cross-sectional areas on tradeoffs among life-cycle costs is shown for Pareto optimal designs.