Robust Structural Response Prediction Under Coupled Thermo-Mechanical Effects via Hierarchical Attention-Based Sensor Fusion
DOI:
https://doi.org/10.54097/2thg6m48Keywords:
Structural health monitoring, thermo-mechanical coupling, hierarchical attention mechanism, sensor fusion, physics-informed deep learning, response predictionAbstract
Accurate prediction of structural responses under coupled thermo-mechanical loading remains a persistent challenge in structural health monitoring (SHM) and reliability engineering. Conventional approaches typically treat thermal and mechanical effects in isolation, failing to capture the nonlinear interactions that arise when temperature gradients and mechanical loads coexist. This paper introduces a hierarchical attention-based sensor fusion (HASF) framework that integrates multi-modal sensor data—encompassing strain gauges, thermocouples, and acceleration sensors—to deliver robust structural response predictions under such coupled conditions. The hierarchical attention mechanism operates at two levels: an intra-modal level that captures temporal dependencies within each sensor modality, and an inter-modal level that adaptively fuses information across modalities according to learned relevance weights. A physics-informed loss function is incorporated to enforce consistency with established thermo-mechanical governing equations, thereby improving generalization in data-sparse regimes. Validation on both a laboratory steel frame structure and a publicly available benchmark dataset confirms that HASF reduces root mean square error (RMSE) by 31.4% compared with state-of-the-art baselines, while achieving competitive inference speeds suitable for real-time monitoring. These findings establish HASF as a viable approach for structural prognosis and health monitoring across aerospace, civil, and mechanical engineering applications.
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