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Discrete response of structures is often a key probabilistic quantity ofinterest. For example, one may need to identify the probability of a binaryevent, such as, whether a structure has buckled or not. In this study, anadaptive domain-based decomposition and classification method, combined withsparse grid sampling, is used to develop an efficient classification surrogatemodeling algorithm for such discrete outputs. An assumption of monotonicbehaviour of the output with respect to all model parameters, based on thephysics of the problem, helps to reduce the number of model evaluations andmakes the algorithm more efficient. As an application problem, this paper dealswith the development of a computational framework for generation ofprobabilistic penetration response of S-2 glass/SC-15 epoxy composite platesunder ballistic impact. This enables the computationally feasible generation ofthe probabilistic velocity response (PVR) curve or the $V_0-V_{100}$ curve as afunction of the impact velocity, and the ballistic limit velocity prediction asa function of the model parameters. The PVR curve incorporates the variabilityof the model input parameters and describes the probability of penetration ofthe plate as a function of impact velocity.

Probabilistic modeling of discrete structural response with application to composite plate penetration models