A Forward Model Driven Structural Health Monitoring Paradigm: Damage Detection

Abstract

Structural Health Monitoring (SHM) involves determining the health state of an engineered structure based upon measured, damage-sensitive features such as natural frequencies, modeshapes and time-domain model coefficients. One of the key challenges in SHM is the difficulty associated with gathering experimental data from a structure in its damaged state. This challenge is particularly acute for purely data-based supervised learning methods. Numerical modelling offers the potential to overcome the lack-of-data problem by making physically informed predictions of how the structure will behave once damaged. However, numerical modelling raises challenges of its own, with a major question being how one incorporates uncertainties and errors arising from the model prediction process within SHM decision-making. In addition, variability inevitably arises in the observed experimental responses and this, too, should be incorporated in the decision process. Finally, it is desirable that the cost of misclassification be incorporated within the decision process, with risk-based approaches being an attractive option for moving from classification to decision-making. This paper introduces a practical application of a Forward Model Driven (FMD) paradigm for SHM. A key tenet of the approach is that numerical model predictions may be used to inform a statistical classifier. The method is demonstrated for the case of damage detection on an experimental truss bridge structure for which an associated finite element (FE) model has been developed. A framework based upon a sequence of binary classifiers is introduced, with attention drawn to the importance both of the choice of individual classifier and the strategy for their combination.