Representative SHM Maintenance Problem

Representative SHM maintenance problems focus on improving the efficiency and accuracy of structural health monitoring (SHM) by leveraging information across multiple similar structures (population-based SHM). Current research emphasizes developing robust transfer learning strategies, often employing hierarchical Bayesian models or physics-informed machine learning (e.g., Gaussian process regression) to address data scarcity and improve generalization across diverse operating conditions. These advancements aim to optimize maintenance decisions by enabling more accurate damage detection and prediction, ultimately leading to cost savings and improved safety in various engineering applications.