Smart Sensing and AI-Driven Monitoring for Natural Hazards and Geotechnical Infrastructure

Dear Colleagues,

Natural hazards and the performance of geotechnical infrastructure are pressing global concerns that require continuous monitoring to mitigate risks and ensure public safety. Advances in sensor technologies—including wireless sensor networks, fiber optic sensing, MEMS, remote sensing, and UAV-based systems—have greatly improved our ability to acquire real-time high-resolution data across diverse spatial and temporal scales. At the same time, the complexity and volume of such monitoring data demand advanced analytical approaches that go beyond traditional methods. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools for anomaly detection, early warning systems, predictive modeling, and data-driven decision-making. This Special Issue will provide a platform for interdisciplinary research on smart sensing technologies and AI-driven data analysis for natural hazard monitoring and geotechnical infrastructure resilience. We welcome contributions covering novel sensing systems, innovative AI/ML algorithms, data fusion techniques, case studies, and practical applications that the fields of bridge engineering and data science. By integrating sensor innovation with intelligent analysis, the Special Issue aims to highlight next-generation approaches for improving infrastructure safety and disaster risk reduction.

Dr. Daoyuan Tan
Dr. Gang Cheng
Dr. Xiao Ye
Guest Editors

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