Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, serviceability, and long-term resilience of foundations and high-rise structures, especially in urban areas susceptible to seismic events, wind loads, and environmental degradation. Conventional SHM techniques, which rely on periodic inspections and isolated sensing devices, often fall short in providing continuous, real-time, and predictive assessments of structural integrity. The integration of Artificial Intelligence (AI) and machine learning into SHM frameworks has opened new possibilities for intelligent, data-driven infrastructure management.This paper presents an AI-assisted SHM framework specifically tailored for foundations and high-rise buildings. The proposed system combines distributed Internet of Things (IoT) sensors, vibration-based monitoring, and advanced machine learning algorithms to analyze large volumes of structural response data. Techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to detect hidden patterns, classify damage states, and predict long-term settlement or degradation trends. Numerical simulations and case studies demonstrate that AI-assisted SHM significantly enhances early anomaly detection, improves prediction accuracy, and enables adaptive monitoring under both operational and extreme loading conditions.The findings underscore the potential of AI-enhanced SHM to revolutionize infrastructure management. By supporting proactive maintenance, reducing life-cycle costs, and improving resilience, this framework provides a pathway toward safer and more sustainable high-rise construction in hazard-prone regions.
Joy Chandra Bormon (Mon,) studied this question.