Transformer-Based Spatiotemporal Modeling of Structural Responses in Intelligent Laminated Systems
Keywords:
transformers, spatiotemporal modeling, intelligent laminated structures, structural health monitoring, edge intelligence, governanceAbstract
The integration of sensing, actuation, and adaptive control into laminated composite structures has given rise to intelligent laminated systems capable of responding to dynamic loads and environmental stimuli in real time. Accurate spatiotemporal modeling of structural responses in these systems is critical for structural health monitoring, damage prognosis, and closed-loop control. While recurrent and convolutional neural networks have historically dominated sequence and image-based structural analysis, transformer architectures have recently emerged as powerful tools for capturing long-range dependencies in high-dimensional, multi-scale data. This paper presents a comprehensive system-level analysis of transformer-based spatiotemporal modeling for intelligent laminated structures, emphasizing architectural trade-offs, data infrastructure, robustness, fairness, sustainability, and governance. We discuss the design of hierarchical transformer models that fuse heterogeneous sensor modalities and incorporate physics-informed constraints without explicit differential equations, thereby enabling interpretable and resilient response predictions. System architecture considerations include the placement of edge and cloud intelligence, data pipeline governance, and the challenges of achieving real-time inference under strict power and latency constraints. We further analyze sources of epistemic and aleatoric uncertainty that may undermine fairness when such models are deployed across geographically and socio-economically diverse infrastructure portfolios, arguing that fairness must be treated as a first-order design principle in structural AI systems. The paper concludes with a discussion of policy instruments, standardization efforts, and lifecycle governance needed to ensure that transformer-based structural intelligence is deployed safely, sustainably, and equitably.
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