Guest Editors
Assist. Prof. Luyang Xu
Email: luyang.xu@ndsu.edu
Affiliation: Department of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, United States
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Research Interests: fiber optic sensing, structural health monitoring, smart materials, advanced coatings
Summary
This Special Issue highlights emerging advances in fiber optic sensing and data-driven methodologies that are reshaping the practice of structural health monitoring (SHM) across civil, aerospace, mechanical, and energy infrastructure. Growing demands from aging assets, extreme climate events, and increasing operational loads emphasize the need for intelligent, continuous, and high-resolution monitoring solutions beyond the limits of conventional sensors. Fiber optic sensing technologies—including FBG, OFDR, BOTDA/DTS, and distributed acoustic sensing—enable high-fidelity strain, temperature, and vibration detection with long-distance, embeddable, and harsh-environment capabilities. When combined with machine learning, digital twins, signal processing advancements, and physics-informed modeling, these systems unlock real-time anomaly detection, predictive maintenance, and autonomous condition assessment.
This Special Issue seeks contributions that address the integration of distributed sensing, hybrid sensor systems, adaptive algorithms, and data fusion strategies to support intelligent decision-making frameworks. The convergence of advanced sensing and data-driven insights will accelerate the transition toward proactive, automated, and resilient infrastructure management. Topics of interest include, but are not limited to:
· Fiber Optic Sensing Technologies and Deployment Strategies
· AI-Enabled Feature Extraction, Diagnostics, and Prognostics
· Digital Twin Frameworks and Data Fusion
· SHM Applications for Pipelines, Transportation Systems, Offshore Platforms, Aerospace, and Smart Materials
Keywords
fiber optic sensing, structural health monitoring (SHM), data-driven methodologies, machine learning, AI, digital twin, predictive maintenance, data fusion, smart infrastructure
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