Artificial Intelligence and Machine Learning for Structural Health Monitoring and Nondestructive Evaluation
A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 1427
Editor
Interests: structural health monitoring; nondestructive evaluation; fault detection and diagnosis; damage identification; artificial intelligence; physics-informed machine learning; data-driven modeling; digital twins; structural vibration control; structural dynamics
Special Issue Information
Dear Colleagues,
Structural systems are widely utilized across diverse engineering disciplines, including civil, mechanical and aerospace engineering. However, these systems may degrade over time due to repetitive loading, temperature fluctuations, environmental corrosion and natural hazards such as earthquakes and typhoons. Therefore, it is essential to monitor their health condition and make robust decisions regarding maintenance and retrofitting to ensure structural integrity, safety and reliability while enhancing resilience and sustainability and reducing lifecycle costs and economic losses. In this context, structural health monitoring (SHM) and nondestructive evaluation (NDE) play critical roles in enabling automated condition assessment and informed decision-making for structural systems.
Recently, advances in artificial intelligence (AI) and machine learning (ML) have enabled the extraction of complex patterns from data and have been successfully applied across a wide range of domains, including computer vision, natural language processing and speech recognition. Their applications in SHM and NDE have grown rapidly in recent years, demonstrating significant advantages such as the ability to handle large-scale and complex data, improved accuracy in fault detection and diagnosis, enhanced predictive maintenance, increased monitoring efficiency with reduced reliance on manual inspection, and the capability for continuous, real-time monitoring.
This Special Issue aims to present and disseminate the most recent advances in AI/ML-based SHM and NDE technologies. We invite both review articles and original research contributions addressing SHM and NDE tasks through physics-informed machine learning, data-driven modeling, hybrid digital twins, domain adaptation and transfer learning, data assimilation, advanced signal processing, information fusion and other emerging AI-driven approaches. Topics of interest include, but are not limited to, the following:
- Structural damage detection, localization and quantification
- System identification and model updating
- Smart sensing and signal processing for SHM and NDE
- Sensor fault detection, diagnosis and accommodation
- Hybrid digital twins for SHM and NDE
- Predictive maintenance and remaining useful life (RUL) prediction
- Material characterization and corrosion assessment
- Vibration-based and vision-based SHM
- Information fusion and heterogeneous data fusion
- Surrogate and reduced-order modeling
- Probabilistic risk assessment and decision making
Dr. Zixin Wang
Guest Editor
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Keywords
- artificial intelligence
- machine learning
- physics-informed learning
- data-driven modeling
- structural health monitoring
- nondestructive evaluation
- fault detection and diagnosis
- digital twins
- material characterization
- system identification
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