Leveraging Artificial Intelligence for Predictive Maintenance and Condition Rating of Off-System Bridges

Off-system bridges are critical components of the United States’ transportation infrastructure, providing essential access to rural communities and enabling residents to reach vital services such as employment, education, and healthcare. Many of these bridges are structurally deficient, functionally obsolete, and unmaintained. This disproportionately hinders the mobility of underserved populations, worsening socioeconomic disparities. Despite existing research, there is insufficient focus on the unique challenges posed by off-system bridges, including handling the class imbalanced nature of the bridge condition rating dataset. This paper predicts bridge deck conditions by using Generative Adversarial Networks with Focal Loss (GAN-FL) to generate synthetic data which enhances precision–recall balance in imbalanced datasets. Results show that integrating GAN-FL with random forest (RF) classifiers significantly enhances the performance of minority classes, improving their precision, recall, and F1 scores. The study finds that augmenting training data using GAN-FL greatly enhances minority class prediction, thereby assisting in accurate bridge condition modeling.