Optimization is a useful tool for determining the best design configuration for a structural system according to a prescribed criterion. Nonetheless, for its practical implementation, it is necessary to cope with unavoidable uncertainty affecting structural performance. From a practical viewpoint, this leads to a double-loop problem, where the outer loop explores different design configurations by means of an optimization algorithm, while the inner loop explores different possible performance scenarios by means of an algorithm for uncertainty quantification. The numerical costs associated with such double-loop problem may become significant or even prohibitive, as they involve repeated analysis of the structural model, which can be quite complex on its own. Thus, optimal structural design under uncertainty has became a field of active research, with focus on efficient strategies for decreasing numerical efforts.

The aim of this mini-symposium is addressing the latest progress on approaches for designing complex systems and structures under uncertain conditions. The scope of the mini-symposium is broad, as it covers different topics such as uncertainty quantification, efficient optimization schemes, advanced simulation techniques, surrogate models, robust design, reliability-based design, multi-objective optimization, life-cycle optimal design, optimal inspection, and maintenance scheduling, etc. Both theoretical developments and applications involving systems of engineering interest are particularly welcomed in this session.

This activity is organized under auspices of the Committee on Probability and Statistics in Physical Sciences (C(PS)^2) of the Bernoulli Society for Mathematical Statistics and Probability and the Risk and Resilience Measurements Committee (RRMC) of the Infrastructure Resilience Division (IRD) at the American Society of Civil Engineers (ASCE).