A Review on Strategic Pavement Maintenance with Machine Learning Techniques

Effective road condition evaluation is necessary to plan the maintenance program about time, technique, and economics. Keeping track of pavement's exterior and interior conditions is a serious issue as well-kept-up pavement plays a significant role in the nation's economy. The pavement potholes and cracks are fundamental indications of deficiencies and are mostly identified with full or partial manual hand help. Manual processes are ineffectual for both data collection and processing and are dominated by experts' experience. Machine learning is a quickly boosting technology and opens windows of opportunities for the utmost sustainability growth. This paper focuses on machine learning techniques used for pavement deficiency detection as Support Vector Machine, K-Nearest Neighbors, Fully Convolutional Network, Deep Convolution Neural Network, Back Propagation, Minimal Path Detection, and Artificial Neural Network. Comparative analyses are performed to provide future directions for developing novel methods to help authorities maintain the transportation system at sustainable levels.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 141-151
  • Monograph Title: Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021

Subject/Index Terms

Filing Info

  • Accession Number: 01884095
  • Record Type: Publication
  • ISBN: 9789811669354
  • Files: TRIS
  • Created Date: May 31 2023 10:54AM