Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data

This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/AJRUA6.0001106 ABSTRACT In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labelling to describe the measurements is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive, while accommodating for missing information in the training-data - such that new information can be included if it becomes available. By reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modelling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals -- including semi-supervised learning, active learning, and multi-task learning. Various machine learning tools have been applied in the literature, for example (Vanik et al. 2000; Sohn et al. 2003; Chatzi and Smyth 2009), and used to infer the health or performance state of the monitored system, either directly or indirectly. Generally, algorithms for regression, classification, density estimation, or clustering learn patterns in the measured signals (available for training), and the associated patterns can be used to infer the state of the system in operation, given future measurements (Worden and Manson 2006). Unsurprisingly, there are numerous ways to apply machine learning to SHM. Notably (and categorised generally), advances have focussed on various probabilistic (e.g. Each approach has its advantages; however, considering certain challenges associated with SHM data (outlined in the next section) the current work focusses on probabilistic (i.e. Additionally, probabilistic methods can lead to predictions under uncertainty (Papoulis 1965) - a significant advantage in risk-based applications.