In situ damage level characterization of carbon-fiber-reinforced polymers via self-sensing and statistical approaches

Abstract

Structural health monitoring (SHM) and prognostics and health management (PHM) play an important role in ensuring user safety and controlling maintenance expenses. To improve these techniques, this paper presents a holistic PHM system for carbon-fiber-reinforced polymers (CFRPs), utilizing a self-sensing method. Concentric holes were progressively machined into CFRPs with continuous electrical resistance monitoring. Empirical correlations between electrical resistance and hole diameters were established based on fiber types and stacking sequences. The correlations enable damage severity characterization and localization, and anticipate future electromechanical behavior under continuous loading. By integrating statistical tools, Markov chain Monte Carlo (MCMC) and Bayesian, the system predicts prospective electrical resistance within 0.65 % error. Therefore, operators can determine both current and future health statuses of in-service CFRP structures with simple polynomial correlations and electrical resistance measurement. This study advances SHM and PHM systems by providing quantitative damage assessment, which enhances understanding of structural integrity and reduces maintenance costs.