As a means to mitigate the economic burden of structural maintenance and the risk of catastrophic failures, the field of structural health monitoring (SHM) offers a diverse suite of sensing and algorithmic technologies that identify structural degradation so as to facilitate timely repairs. While many SHM techniques have been proposed, the majority relies on correlating changes in global structural properties to damage. In order to detect component level damage, techniques such as modal frequency analysis are often not sufficiently sensitive to local structural variations such as damage. While strain gages can be installed to obtain local structural measurements, their main disadvantage is that they can only provide response data at one discrete location in the structure. Accurate damage detection requires either a dense network of these point-sensors or accurate models for extrapolating localized strain fields to the remainder of the structure.
Researchers at University of Michigan have developed a method to detect various types of environmental stimuli. Carbon nanotube (CNT) thin films are fabricated by a layer-by-layer (LbL) technique or other techniques and mounted with electrodes along their boundaries. The response of the thin films to various stimuli determined by relying on electric current excitation and corresponding boundary potential measurements. The spatial conductivity variations are reconstructed based on a mathematical model for the electrical impedance tomography technique.
Applications and Advantages
- Structural monitoring of bridges, pipelines, and other large civil infrastructure systems
- Detects tensile-compressive cyclic strains, impact damage, and corrosion byproduct formation in typical metallic structural components