Abstract

This paper proposes a novel unsupervised damage detection approach based on a memetic algorithm that establishes the normal or undamaged condition of a structural system as data clusters through a global xpectation–maximization technique, using only damage-sensitive features extracted from output-only vibration measurements. The health state is then discriminated by considering the Mahalanobis squared distance between the learned clusters and a new observation. The proposed approach is compared with state-of-the-art ones by taking into account real-world data sets from the Z-24 Bridge (Switzerland), where several damage scenarios were performed. The results indicated that the proposed approach can be applied in structural health monitoring applications where life safety, economic, and reliability issues are the most important motivations to consider.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.