Content area
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.
Details
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
; Papazafeiropoulos, George 2
1 College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar
2 School of Civil Engineering, National Technical University of Athens, 15780 Athens, Greece;