Bridge condition assessment via fine-tuning a foundation model: An analysis of the impact of imbalanced data on judgment accuracy

Abstract

This study verifies the feasibility of constructing a high-precision model for assessing bridge conditions, even under situations of severe data imbalance. In bridge inspection data, images indicating critical damage ("IV: Emergency Action Stage") are extremely rare, which poses a challenge as conventionally trained AI models tend to be biased towards the majority classes. To address this issue, our research introduces the use of Focal Loss to promote learning on minority classes and data augmentation to enhance the diversity of training data during the fine-tuning of a foundation model. Experiments quantitatively compared and analyzed the performance with and without these two methods, confirming that their combination significantly improves the judgment accuracy for minority classes, including "IV: Emergency Action Stage." This result contributes to reducing the risk of AI models overlooking critical damage andenhancing the reliability of condition assessment.