Abstract
In the past few years, several works focused on the integration of methodologies within the field of Structural Health Monitoring to build reliable automatic damage-assessment procedures.Within this context, only a few papers specifically refer to the automatic assessment of tendon malfunctions in prestressed concrete (PSC) structures, despite the key role that this construction paradigm plays in modern infrastructure networks. This paper describes a novel Extreme Learning Machine (ELM) framework characterized by a layout-aware weight generating procedure (LA-ELM), that analyzes stress data to accurately detect and localize damages affecting the prestressing system of a target PSC bridge.A comprehensive computational study is conducted, testing the proposed methodology of three structural specimens, and comparing the proposed LA-ELM with classical Machine Learning algorithms. The numerical results evidence that the proposed methodology achieves remarkable accuracies in short computational times, and the LA-ELM obtains statistically significant improvements compared to the classical ELM implementation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.