Simulating Federated Learning with Data Augmentation for Culvert Condition Prediction in Utah: A Case Study

Transportation agencies are increasingly adopting cutting-edge solutions to enhance infrastructure management strategies. Infrastructure condition prediction models, particularly important for optimizing inspection resources, are at the forefront of this trend. Machine learning (ML) algorithms play a crucial role in these models, but traditional centralized ML models often struggle with data scarcity, privacy, and transferability. This paper introduces a decentralized approach using Federated Learning (FL) for infrastructure condition prediction, simulating its effectiveness with culvert inventories in Utah and five other states in the United States. We analyzed two FL models—one with data augmentation and one without—against a traditional centralized model. Our results demonstrated that FL-based models improved prediction accuracy by 30%, ensured data privacy, and reduced data transmission overheads. Moreover, due to its limited data, Utah benefited from federated insights, illustrating FL's potential to effectively enhance infrastructure management. The simulation highlights the advantages of utilizing FL in real-world scenarios.