Author(s): Lieutenant Colonel Vaibhav Srivastava and DI Narkhede

Abstract: In response to these limitations, this thesis presents a comprehensive, AI-powered, and digitally integrated methodology for the Structural Health Monitoring (SHM) of bridges and civil infrastructure. It combines the power of LiDAR-based point cloud data, unsupervised and deep learning techniques, digital twin modelling, simulation analytics, and real-time dashboards to establish a predictive, autonomous, and highly scalable SHM framework. The research highlights significant technological advancements and engineering practices that align with modern infrastructure safety, longevity, and cost-effectiveness ^{[1, 5]}.

DOI: 10.33545/27076571.2025.v6.i1c.152

Pages: 178-184 | Views: 1238 | Downloads: 829

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