Efficient optimal design-under-uncertainty of passive structural control devices

This paper proposes a computationally efficient framework for design optimization under uncertainty for structures with local nonlinearities. To reduce the high computational cost of Monte Carlo simulation of such problems, an exact model reduction to a low-order Volterra integral equation is used to accelerate each simulation, and variance-reduced sampling is used to reduce the number of simulations required for the uncertainty quantification. This optimization framework is applied to a benchmark cable-stayed bridge problem, designing one pair of passive tuned mass dampers given a pair of uncertain passive power law dampers, providing significant gains in computational efficiency, two orders of magnitude, compared to traditional approaches.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyGraduat