Attention Fusion for Bridge Deck Delamination Detection

Subsurface delaminations in reinforced concrete bridge decks escape conventional visual inspection, and the two principal sensing techniques used to find them are individually incomplete: Ground Penetrating Radar (GPR) penetrates deeply but degrades near the surface, while Infrared Thermography (IRT) resolves shallow defects but cannot reach deeper structure. This paper presents a framework for fusing the two modalities through hierarchical attention: temporal self-attention over GPR A-scans, channel-spatial attention over IRT patches, and cross-modal multi-head attention with learnable modality embeddings, coupled with decomposed aleatoric/epistemic uncertainty estimation. Beyond the architecture itself, which is lightweight at approximately 0.53M parameters with a closed-form accounting of where capacity resides, we contribute an elementary formal analysis. Two-token cross-modal attention is shown to be exactly a bank of per-sample learned gates; a gradient-allocation proposition quantifies how class imbalance starves attention parameters of minority-class signal and how loss reweighting trades that starvation for gradient variance; and closed-form metric floors under majority-class collapse anchor a diagnostic divergence between ranking metrics (AUC) and thresholded metrics (F1). The analysis suggests that adaptively weighted fusion, precisely because its feature-selection policy is learned, may be distinctively vulnerable to the severe class imbalance typical of operational bridge decks; establishing whether and when this occurs is deferred to empirical evaluation.