Machine learning analysis of overweight traffic impact on survival life of asphalt pavement

Jingnan Zhao, Hao Wang, Pan Lu

Research output: Contribution to journalArticlepeer-review


The objective of this study is to quantify the impact of overweight traffic on asphalt pavement life using machine learning method for survival analysis. Traffic data and field distress measurements were collected from the long-term pavement performance (LTPP) database. A random survival forest algorithm was used to establish predictive models of load-related pavement distresses considering traffic loading, pavement structure, and climate. The variable importance approach was used to select the appropriate variables in the model and reduce prediction error. The findings indicated that the explanatory variables related to axle load spectra and traffic loading were significant in explaining pavement performance degradation. The derived models were further applied to estimate the survival probability curves of asphalt pavement life at different loading scenarios and evaluate the impact of overweight traffic on the reduction of pavement life. The reduction ratio of pavement life due to alligator cracking resulting from overweight traffic was found to be greater than those due to longitudinal cracking and rutting for all the pavement sections. The study findings indicate that the proposed random survival forest model is a promising approach for quantifying the impact of traffic loading on pavement life considering axle load spectra characteristics.

Original languageEnglish (US)
Pages (from-to)606-616
Number of pages11
JournalStructure and Infrastructure Engineering
Issue number5
StatePublished - 2023

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Safety, Risk, Reliability and Quality
  • Geotechnical Engineering and Engineering Geology
  • Ocean Engineering
  • Mechanical Engineering


  • Pavements
  • axle load spectra
  • long-term pavement performance
  • overweight traffic
  • pavement life
  • pavement performance
  • random survival forest


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