  We present a simulation-based classification approach for large deployed
structures with localized operational excitations. The method extends the
two-level Port-Reduced Reduced-Basis Component (PR-RBC) technique to provide
faster solution estimation to the hyperbolic partial differential equation of
time-domain elastodynamics with a moving load. Time-domain correlation
function-based features are built in order to train classifiers such as
artificial neural networks and perform damage detection. The method is tested
on a bridge example with a moving vehicle (playing the role of a digital twin)
in order to detect cracks' existence. Such problem has $45$ parameters and
shows the merits of the two-level PR-RBC approach and of the correlation
function-based features in the context of operational excitations, other
nuisance parameters and added noise. The quality of the classification task is
enhanced by the sufficiently large synthetic training dataset and the accuracy
of the numerical solutions, reaching test classification errors below $0.1\%$
for disjoint training set of size $7\times10^3$ and test set of size
$3\times10^3$.
