  Computer vision leveraging deep learning has achieved significant success in
the last decade. Despite the promising performance of the existing deep models
in the recent literature, the extent of models' reliability remains unknown.
Structural health monitoring (SHM) is a crucial task for the safety and
sustainability of structures, and thus prediction mistakes can have fatal
outcomes. This paper proposes Bayesian inference for deep vision SHM models
where uncertainty can be quantified using the Monte Carlo dropout sampling.
Three independent case studies for cracks, local damage identification, and
bridge component detection are investigated using Bayesian inference. Aside
from better prediction results, mean class softmax variance and entropy, the
two uncertainty metrics, are shown to have good correlations with
misclassifications. While the uncertainty metrics can be used to trigger human
intervention and potentially improve prediction results, interpretation of
uncertainty masks can be challenging. Therefore, surrogate models are
introduced to take the uncertainty as input such that the performance can be
further boosted. The proposed methodology in this paper can be applied to
future deep vision SHM frameworks to incorporate model uncertainty in the
inspection processes.
