Explainable Artificial Intelligence for Vibration Analysis and Model Updating of Smart Laminated Structures
Keywords:
explainable artificial intelligence, smart laminated structures, vibration analysis, model updating, structural health monitoring, interpretability, robustness, infrastructure governanceAbstract
The integration of smart laminated composite structures into critical engineering systems has transformed the landscape of structural health monitoring, yet it also introduces substantial complexity in interpreting sensor data and updating numerical models. As artificial intelligence becomes central to vibration-based damage detection and model updating, the opacity of many high-performance machine learning models presents a barrier to trust, regulatory compliance, and informed decision-making. This paper presents a systems-level analysis of explainable artificial intelligence applied to vibration analysis and model updating of smart laminated structures. It investigates the architectural and infrastructural demands of embedding explainability within the full lifecycle of data-driven structural assessment, from signal acquisition and feature extraction through model updating and deployment. The discussion delineates fundamental trade-offs between predictive accuracy and interpretability, examines the influence of post-hoc explanation methods on engineering confidence, and highlights the challenges associated with the non-uniqueness and ill-posedness inherent in model updating of laminated composites. Deployment considerations are addressed through the lens of edge computing, low-power sensing networks, and long-term sustainability of monitoring systems. Robustness and adversarial vulnerabilities of explainable models are scrutinized, as are the governance and fairness dimensions that arise when AI-informed decisions affect public safety and resource allocation across diverse communities. By synthesizing perspectives from structural dynamics, machine learning, human-computer interaction, and policy studies, the paper articulates a forward-looking framework for responsible and interpretable AI in next-generation smart infrastructure. The analysis underscores that explainability must be treated as a system property rather than a post-processing accessory, demanding co-design across hardware, algorithms, and institutional protocols to achieve safe, equitable, and transparent structural health management.
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