ISI Report

The integration of artificial intelligence (AI) with structural health monitoring (SHM) and seismic safety frameworks represents a pivotal advancement for the resilience of urban infrastructure. Traditional monitoring and retrofitting practices often struggle to provide real-time insights, predictive capabilities, and adaptive interventions that are essential for safeguarding critical assets in smart cities. Recent advances in data-driven modeling, sensor technologies, and algorithmic intelligence have enabled the development of systems that not only detect damage but also predict failure mechanisms under seismic loads. Such approaches create opportunities for transformative improvements in the durability, safety, and adaptability of high-rise structures, transportation networks, and essential lifelines. This research addresses the convergence of SHM and AI by exploring new strategies for seismic retrofitting and resilience-focused design. Emphasis is placed on how AI-driven models can assimilate massive datasets from heterogeneous sensors, extract meaningful patterns from guided waves and fiber optic systems, and integrate real-time decision-making into urban safety operations. The methodology involves a hybrid framework that combines deep learning architectures, finite element analysis, and knowledge distillation approaches to evaluate structural vulnerabilities under dynamic conditions. Results demonstrate that AI-enabled monitoring significantly enhances the detection of micro-cracks, the optimization of retrofitting designs, and the prediction of performance levels during seismic events. The findings highlight that future urban infrastructures, when coupled with intelligent monitoring systems, can achieve unprecedented levels of resilience. This study contributes to the ongoing discourse on how cities can prepare for natural hazards by leveraging artificial intelligence not merely as a support tool but as a core driver of adaptive, self-correcting, and sustainable safety solutions. The implications extend beyond engineering practice, offering frameworks for policymakers and planners to integrate resilience into the DNA of next-generation smart cities.