Cost-Effective Pavement Condition Survey for Municipal Road Networks

Abstract

One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.