Engineering topics

Hemez, Francois M

Publications and source records attributed to Hemez, Francois M.

2 records

DOE OSTI2012

On Assessing the Robustness of Structural Health Monitoring Technologies

As Structural Health Monitoring (SHM) 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 SHM will also continue to grow. As a result, the engineer tasked with developing a SHM 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. This paper offers a framework that aims to provide the engineer with a quantitative approach for choosing from among a suite of candidate SHM technologies. The framework is outlined for the general case, where a supervised learning approach to SHM is adopted, and the presentation will focus on applying the framework to two commonly encountered problems: (1) selection of damage-sensitive features and (2) selection of a damage classifier. The data employed for these problems will be drawn from a study that examined the feasibility of applying SHM to the RAPid Telescopes for Optical Response observatory network.

42 ENGINEERING↗

DOE OSTI2012

Robust Decision-making Applied to Model Selection

The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.

17 WIND ENERGY↗