Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes

•A semi-supervised and a supervised learning algorithm for health monitoring of pipes is proposed.•The proposed approaches are data-driven, hence circumventing the need of computationally prohibitive model-based approaches.•The proposed approaches can perform damage detection in pipes with only two actuator-sensor pairs.•Efficacy of the proposed approaches are demonstrated using experimental data from two cast iron pipes.