Multifunctional Materials and Tomographic Algorithms for Human and Structural Sensing

K.J. Loh
University of California, San Diego,
United States

Keywords: tomographic, algorithms, human, sensing


Structural systems are susceptible to damage and, if they remain undetected, can propagate to cause catastrophic failure. Structural health monitoring (SHM) is crucial for identifying damage initiation, directing repair, and ensuring system safety/reliability. However, many catastrophic structural failures can be traced back to human factors that contributed to the incident. Therefore, in order to ensure structural safety and to prevent future accidents from occurring, both the health of the structure and the well-being of the human operator need to be monitored. This presentation outlines a new paradigm shift in SHM, where sensors are designed from a materials perspective stemming from a “bottom-up” design methodology. Multifunctional materials can be designed with precise engineering functionalities suitable for monitoring the structure and the human operator. The presentation is divided into three parts. First, the design and fabrication of nanocomposite sensors sensitive to different external stimuli will be discussed. Second, by coupling the films with an electrical impedance tomography (EIT) algorithm, these “sensing skins” can localize and characterize damage severity. Last, a unique noncontact tomography method is employed to enable noninvasive structural sensing. An added advantage of this technique is that surface and subsurface “damage” features can be identified and located. This work presents these techniques in the context of SHM and human health monitoring, including both numerical simulations and experimental test studies.