Application of Machine Learning Based Technology in Pavement Condition Assessment and Prediction

A novel approach using machine learning based technology is presented for pavement condition assessment and prediction. A year long vibration data collected in the I-10 corridors located in Pheonix, Arizona USA was obtained for analysis. All vibration data were analyzed through three steps: cluster analysis, resampling, implementing and evaluating machine learning algorithms. Cluster analysis eliminates high correlation parameters to improve algorithm efficiency. Resampling avoids the possible over-fitting results of an unbalanced dataset. The authors implement and evaluate commonly used machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), for the evaluation of three pavement conditions (i.e., slight, moderate, and severe) and future predictions. Among the four computing algorithms, it is found that Random Forest achieved the best performance for condition assessments and performance predictions of highway pavements with its rated accuracy of 98%, a Matthews correlation coefficient of 76.54%, a precision of 95%, and a recall of 77%. The results show that machine learning algorithms based on random forest can provide accurate pavement condition detection. The methodology presented in the paper could help highway agencies and research institutions to determine the levels of pavement deterioration and develop road maintenance plans.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 13p

Subject/Index Terms

Filing Info

  • Accession Number: 01763474
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03949
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:01AM