, Associate Professor, Civil, Environmental and Geo-Engineering
The essential goal of structural health monitoring (SHM) techniques is to identify defects or anomalies of the internal configuration of solids and structures. Dynamics-based SHM attempts to extract microscopic defect and anomaly information from measurements of the material's macroscopic dynamic response. In wave-based SHM the standard approach entails exciting an incident wave through the material and extracting from the resulting measured displacement field some characteristic information from which the position, size, and under certain circumstances, the shape of the defects can be inferred. The successful implementation of such approach relies implicitly upon sometimes stringent assumptions on the geometry, composition, and wave propagation properties of the material under test. As such, these SHM methodologies become far less effective when applied to materials with complex geometries, heterogeneous microstructure, or highly nonlinear material behavior. A correct interpretation of the dynamic response is particularly cumbersome when dealing with lossy materials, such as the polymeric matrix of a composite, or heterogeneous solids characterized by a rich microstructural organization (possibly featuring several competing length scales) as in most alloys, fiber or particle reinforced composites, and porous materials. By generalization, the issues arising in inhomogeneous solids also apply to damaged specimens comprised of a macroscopically homogeneous material with clusters of multiple defects or damage zones.
Motivated by the limitations of current dynamics-based inspection strategies, and inspired by the flexibility of state-of-the-art laser-based sensing systems, the researchers propose a paradigm for agile response-driven laser-based inspection and characterization of heterogeneous material systems. This approach leverages the synergy between efficient and agile sensing methodologies, enabled by three-dimensional scanning laser vibormetry and newly-defined damage-sensitive metrics of structural response, and will lead to the development of a systematic framework for efficient data-driven identification of microstructural features in heterogeneous solids.