I-Corps: Automated Pavement Evaluation Using Advanced Machine Learning

Award ID: 2229743

The broader impact/commercial potential of this I-Corps project is the development of a software suite that allows highway owner-agencies such as cities and state Departments of Transportation (DOTs) to manage their pavement networks in a more rigorous and cost-effective manner, enabling more strategic use of maintenance and rehabilitation funds. The software suite can be readily expanded to include other civil infrastructure, such as airfield pavements, bridges, and rail and transit track systems. In addition, other assets associated with or in the vicinity of transportation facilities can be located, identified, and assessed. This mapping includes road signs, guard rails, paint markings, utilities, garbage bins, etc., which can be viewed as additional layers on the data visualization platform of the software suite. Finally, measures of transportation sustainability, resilience, and environmental impact may be assessed and visualized as yet another layer on the visualization software. This visualization provides owner-agencies an unbiased, straightforward platform to assist them in systematically moving towards more sustainable and resilient infrastructure. This I-Corps project is based on the development of an automated pavement evaluation software suite. The suite will incorporate several coded and integrated machine learning and deep learning techniques used for the detection and classification of the extent and severity of critical pavement obstacles. The algorithms seek to provide highly accurate, unbiased pavement condition assessments. The machine learning-based models use a comprehensive pavement image dataset, which considers twenty different pavement distresses for both flexible and rigid pavements as verified by pavement experts. Another capability of the software is its ability to pre-screen projects using internet street view images. This capability may allow agencies to crowd-source data for their own road networks using municipal vehicle fleets equipped with the supplied video capture and road roughness sensor system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman