Maintaining the structural integrity of reinforced concrete bridges necessitates the timely and accurate detection of surface defects. Conventional inspection methodologies remain labor-intensive, inherently subjective, and susceptible to human error, driving the need for automated assessment frameworks. This study introduces a multi-label defect classification model tailored for reinforced concrete bridge inspection, engineered to process imagery consistent with prevailing bridge inspection standards. The proposed framework is designed to simultaneously identify multiple co-occurring defects within a single image, addressing the practical reality of overlapping deterioration mechanisms. Leveraging the open-source Concrete Defect Bridge Image Dataset (CODEBRIM), three distinct ImageNet-pretrained deep neural network architectures were subjected to systematic hyperparameter optimization and fine-tuning to enhance classification performance across bridge-relevant defect categories. Beyond achieving high per-class accuracy, the optimized model attained a subset accuracy of 84.0% and a micro-averaged F1-score of 85.2% on a held-out test set, signifying robust recognition of overlapping distress conditions. Furthermore, evaluation on a synthetically generated dataset validated the model's generalization capacity under domain shift. The findings demonstrate that the proposed framework effectively supports automated defect documentation and holds significant potential for enhancing the objectivity and efficiency of bridge condition assessment protocols.
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Funding
National Science Foundation(2026612)
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