Advanced SHM for Infrastructure Damage Detectio
*Corresponding Author:Received Date: Jul 04, 2025 / Accepted Date: Aug 13, 2025 / Published Date: Aug 13, 2025
Copyright: © 2025 Dr. Tom Williams This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
This collection of research highlights significant advancements in Structural Health Monitoring (SHM) across diverse civil infrastructure. It covers innovative approaches utilizing deep learning and machine learning for real-time damage detection in steel and bridge structures, often leveraging wireless or smartphone vibration data. The studies also emphasize advanced sensor technologies like fiber optics, nondestructive testing for concrete, and remote sensing for bridges. Furthermore, autonomous multi-robot systems and digital twin technology are explored for enhanced inspection and predictive maintenance. Structural identification and computer vision methods further refine damage assessment, collectively improving the efficiency, safety, and longevity of critical infrastructure.
