Computational thermal analysis for health monitoring of concrete dam structures using shadow modeling and deep learning

This study compares LSTM neural networks and physics-based heat balance models for predicting concrete dam surface temperatures under shadow effects from surrounding terrain. Three-dimensional shadow modeling is integrated with UAV-LiDAR point cloud data to quantify shadow-induced radiation variations. Monitoring data from 2023-2024 are analyzed to evaluate physical consistency and spatial generalization capability. Strong correlation (R²=0.562-0.587) between radiation reduction and temperature changes is demonstrated by the heat balance model. In contrast, near-zero correlation (R²=0.000-10.0083) is observed in LSTM predictions. Spatial analysis reveals identical predictions (r=0.9999) across different monitoring locations by LSTM, indicating that spurious temporal correlations are learned rather than causal shadow mechanisms. These findings demonstrate that high accuracy metrics cannot guarantee physical correctness in data-driven models. Physics-based or hybrid approaches are recommended for structural health monitoring applications requiring spatial generalization beyond training data locations.