Population Based Structural Health Monitoring

Population-based structural health monitoring (PBSHM) leverages data from multiple similar structures to improve the accuracy and efficiency of damage detection and prognosis in individual structures, overcoming limitations of data scarcity in traditional methods. Current research focuses on developing advanced machine learning models, including hierarchical Bayesian models, graph neural networks, and multi-task learning approaches, to effectively share information across populations and optimize resource allocation for monitoring. This approach holds significant promise for reducing maintenance costs and improving safety in various applications, such as offshore wind farms and aircraft fleets, by enabling more robust and reliable structural assessments.