  Discrete response of structures is often a key probabilistic quantity of
interest. For example, one may need to identify the probability of a binary
event, such as, whether a structure has buckled or not. In this study, an
adaptive domain-based decomposition and classification method, combined with
sparse grid sampling, is used to develop an efficient classification surrogate
modeling algorithm for such discrete outputs. An assumption of monotonic
behaviour of the output with respect to all model parameters, based on the
physics of the problem, helps to reduce the number of model evaluations and
makes the algorithm more efficient. As an application problem, this paper deals
with the development of a computational framework for generation of
probabilistic penetration response of S-2 glass/SC-15 epoxy composite plates
under ballistic impact. This enables the computationally feasible generation of
the probabilistic velocity response (PVR) curve or the $V_0-V_{100}$ curve as a
function of the impact velocity, and the ballistic limit velocity prediction as
a function of the model parameters. The PVR curve incorporates the variability
of the model input parameters and describes the probability of penetration of
the plate as a function of impact velocity.
