Machine learning techniques also are being developed to detect and classify damage in different types of structures. Currently, an active learning, hierarchical support vector clustering algorithm helps to classify multiple damage types in a computationally efficient manner.

In addition, multidisciplinary design optimization-based sensor placement algorithms for SHM and prognosis address: (i) integrated airloads, (ii) damage probability and sensor sensitivity estimations to damage parameters, (iii) sensor/host structure coupling, and (iv) material attenuation. Experimental studies and model validations are carried out using a number of state- of-the-art detection systems, including PZT and FBG sensors, an air coupled C-scan, an Echotherm thermographic system and a scanning laser-Doppler vibrometer.