A sampling-based RBDO algorithm with local refinement and efficient gradient estimation

This article describes a two stage Reliability-Based Design Optimization (RBDO) algorithm. The first stage consists of solving an approximated RBDO problem using meta-models. In order to use gradient-based techniques, the sensitivity of failure probabilities are derived with respect to hyperparameters of random variables as well as, and this is a novelty, deterministic variables. The second stage focuses on the local refinement of the meta-models around the first stage solution using generalized “max-min” samples. The approach is demonstrated on three examples including a crashworthiness problem with 11 random variables and 10 probabilistic constraints.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.FacultyResearche