Foundation Model-Assisted Structural State Estimation for Smart Composite Laminates under Uncertain Environments
Keywords:
structural health monitoring, smart composite laminates, foundation models, uncertainty quantification, digital twin, edge intelligenceAbstract
The increasing deployment of smart composite laminates in aerospace, energy, and civil infrastructure demands robust structural state estimation capable of coping with severe environmental and operational uncertainties. Traditional physics-based model updating and purely data-driven deep learning approaches each exhibit brittleness under distribution shift, motivating the search for more adaptive architectures. This paper investigates the integration of foundation models—large-scale pretrained time-series models—into a system-level framework for real-time strain, damage, and damping state inference in laminated composite structures instrumented with embedded sensor networks. We articulate a hybrid architecture that couples a foundation model backbone with physics-informed priors, domain-specific fine-tuning, and edge-cloud orchestration to reconcile representational generality with task-specific precision. The analysis foregrounds structural trade-offs including latency versus accuracy, data provenance and sensor heterogeneity, uncertainty quantification, and the tension between centralized model governance and on-device adaptation. The discussion extends to socio-technical dimensions, examining how foundation model-assisted estimation reshapes certification pathways, algorithmic fairness across heterogeneous structural populations, and the regulatory implications of automated decision-making in safety-critical systems. By centering the system-level perspective rather than algorithmic novelty, we provide a comprehensive roadmap for deploying foundation model capabilities in physically-grounded, uncertainty-aware infrastructure health management.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
