On assessing the robustness of structural health monitoring technologies

Abstract

As structural health monitoring continues to gain popularity, both as an area of research and as a tool for use in industrial applications, the number of technologies associated with structural health monitoring will also continue to grow. As a result, the engineer tasked with developing a structural health monitoring system is faced with myriad hardware and software technologies from which to choose, often adopting an ad hoc qualitative approach based on physical intuition or past experience to making such decisions, and offering little in the way of justification for a particular decision. This article offers a framework that aims to provide the engineer with a quantitative approach for choosing from among a suite of candidate structural health monitoring technologies. The framework is outlined for the general case, where a supervised learning approach to structural health monitoring is adopted and is then demonstrated on two problems commonly encountered when developing structural health monitoring systems: (a) selection of damage-sensitive features, where the engineer must determine the appropriate order of an autoregressive model for modeling of time-history data, and (b) selection of a damage classifier, where the engineer must select from among a suite of candidate classifiers, the one most appropriate for the task at hand. The data employed for these problems are taken from a preliminary study that examined the feasibility of applying structural health monitoring technologies to the RAPid Telescopes for Optical Response observatory network.