PFI-TT: Using artificial intelligence to improve the accuracy of automated pavement condition data collection

Texas State University - San Marcos, San Marcos TX

Investigators

Abstract

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in enabling engineers to make sound maintenance decisions through a novel, cost-effective, accurate and reliable automated pavement condition data collection system. The proposed technology should reduce the costs and improve the quality of pavement data collection. A successful application of the new technology is expected to benefit society by improving the maintenance of transportation infrastructure and, subsequently, other infrastructure systems. One societal contribution of this project is the acquisition, accession, management, and public sharing of pavement distress images using industrial standards. Sharing the image data generated under this project may ultimately improve pavement image processing abilities. Furthermore, this project will provide entrepreneurial education and leadership development opportunities for a postdoctoral researcher and graduate and undergraduate students. The proposed project is aimed at prototyping a cost-effective, automated pavement condition data collection system. To overcome the inaccuracy and reduce the costs associated with existing methods, the proposed technology will use machine learning-based image processing algorithms, which are more sophisticated than current rules-based methods. In addition, the proposed system is expected to improve data quality assurance by using analytical capabilities to identify problematic data and reduce errors. Finally, the proposed system is expected to reduce the use of costly hardware, thereby ensuring the financial sustainability of pavement and infrastructure management. The core components of this system are: (1) cost-effective 3D (three dimensional) image acquisition; (2) machine learning-based pavement image processing algorithms; and (3) systemic design- and optimization-driven data quality assurance. 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.