Concrete crack classification by a lightweight Bayesian CNN: a comparative evaluation with UNet on performance improvement through a Bayesian approach

Concrete cracks significantly impact structural durability by allowing harmful elements such as carbon dioxide and chloride ions to penetrate. Thus, the cracks should be prevented and treated to maintain the lifespan of concrete structures. Convolutional neural network (CNN) has been widely applied in crack detection. This study investigated the use of a Bayesian convolutional neural network (BCNN) for concrete crack classification and compared the performance of BCNN with UNet, a deep CNN-based model, and CNN. BCNN weight uncertainty through a Bayesian method, improved prediction reliability and prevented overfitting. BCNN demonstrated superior expected calibration error (0.0030) and Brier score (0.0025) compared to CNN, highlighting its robustness in prediction reliability. Furthermore, Bayesian entropy analysis confirmed the ability of BCNN to quantify uncertainty, leading to more precise crack classification. These results imply that BCNN provides a balance between computational efficiency and performance, offering strong potential for smart construction applications. Validation results indicated that BCNN achieved a validation accuracy of 92.22%, higher than CNN (85.78%) and slightly lower than UNet (94.34%). However, BCNN demonstrated a superior intersection over union (IoU) of 0.8049, outperforming CNN (0.6685) and approaching UNet (0.9135). These results implied that BCNN provides a balance between computational efficiency and performance, offering strong potential for smart construction applications.