Surrogate measures to optimize structures for robust and predictable progressive failure

Abstract

Optimizing complex structures for robust and predictable progressive failure using probabilistic approaches is computationally expensive. In this paper we investigate the progressive failure characteristics of structures subjected to random variability and deduce patterns to identify surrogate measures that correlate with robustness and predictability of the design’s progressive failure. The procedure is demonstrated for the optimization of robustness and predictability in progressive failure of truss structures. Deterministic optimization of trusses was used to generate candidate designs to compare and contrast robustness and predictability. The stochastic analyses of the candidate designs are then used to identify surrogate features that correlate to robustness and predictability of progressive failure response. These features are converted to numerical surrogate objectives or constraints and used in optimization to demonstrate their effectiveness and computational efficiency. The example shows that surrogate measures can be developed for robustness and predictability optimization, and that such measures are computationally efficient compared to robustness optimization using sampling based methods.