A Bayesian decision support system for optimizing pavement management programs

Babitha Philip, Hamad AlJassmi

Research output: Contribution to journalArticlepeer-review


Over time, the pavement deteriorates due to traffic and the environment, resulting in poor riding quality and structural inadequacies. Evaluating pavement condition over time is thus a critical component of any pavement management system (PMS) to extend the service life of pavements. However, the uncertainty associated with the pavement deterioration process due to the heterogeneous nature of the pavement degradation factors makes the process difficult. The current work addresses this challenge of pavement management by developing an expert system framework based on Bayesian Belief Networks (BBN). This framework integrates data on existing road deterioration factors with knowledge gained from pavement experts to produce optimal decisions. The advantages of the BBN techniques lie in their ability to capture uncertainty, and probabilistically infer the values of variables in the domain, especially in the case of incomplete information where we only have data about some and not all variables. This has motivated the adoption of BBN in this study to optimize pavement maintenance decisions, on the basis of inferred road deterioration interpretations drawn from partial knowledge about road distress variables. This study presents the adoption of Bayesian methods to assist pavement maintenance engineers in determining the most successful and efficient maintenance and repair (M&R) tactics and the best time to apply them by means of a decision-support system. Data collected from 32 road sections in the United Arab Emirates in relation to road distress parameters (rutting, deflection, cracking, and international roughness index), as well as road characteristics, traffic, and environment data, has been used to demonstrate the applicability of the proposed decision-support tool.

Original languageEnglish
Article numbere25625
Issue number3
Publication statusPublished - Feb 15 2024


  • Bayesian belief networks
  • Decision-support
  • Machine-learning
  • Uncertainty

ASJC Scopus subject areas

  • General


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