This study introduces data mining for acoustic emission (AE) monitoring of containment structures. As being post-tensioned, hidden delamination cracks may develop in these structures and remain undetected. Since concrete cracking emits acoustic noises, advanced data mining techniques are here introduced to recognize common patterns in such AEs. Specifically, non-linear dimensionality reduction, k-mean clustering, and hidden Markov modelling are used. Validation performed on a large-scale, curved concrete wall shows, interpreting the physical meaning of AE patterns allows early detection of delamination.