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AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

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Pubblicato in:Infrastructures vol. 9, no. 12 (2024), p. 225
Autore principale: Plevris, Vagelis
Altri autori: Papazafeiropoulos, George
Pubblicazione:
MDPI AG
Soggetti:
Signal analysis
Data processing
Data acquisition
Trends
Noise prediction
Nondestructive testing
Civil engineering
Maintenance management
Damage detection
Remote monitoring
Data analysis
Automation
Machine learning
Infrastructure
Risk analysis
Data collection
Internet of Things
Vibration
Risk assessment
Accountability
Structural health monitoring
Big Data
Construction
Adaptive systems
Embedded systems
Bibliometrics
Artificial intelligence
Computer vision
Noise reduction
Decision making
Sensors
Algorithms
Anomalies
Deep learning
Real time
Bridges
Nuclear power plants
Predictive maintenance
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Abstract:This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems.
ISSN:2412-3811
DOI:10.3390/infrastructures9120225
Fonte:Advanced Technologies & Aerospace Database