An intelligent decision-making framework for asphalt pavement maintenance using the clustering-PageRank algorithm

Abstract

With ever-increasing road mileages worldwide, more pavement deterioration and ageing present great challenges to the maintenance and rehabilitation (M&R) of road pavement. In this study, an intelligent decision-making framework is developed for pavement maintenance using the clustering-PageRank algorithm (CPRA) based on historical big data. The proposed model is applied to a 3.5 km pavement (500 road sections) and leads to recommendations for the optimal pavement maintenance plans with appropriate possibilities. The results indicate that seven plans are the same as those obtained by the experience-based maintenance approach, while the other three are similar. The framework is also verified by comparison with the experience-based maintenance activities and is found to have limited reliability when dealing with a small quantity of solutions. The method and results of this study are expected to serve as a reference for decision makers to make well-informed project decisions on the optimum M&R activities.

KEYWORDS:

• Pavement maintenance
• clustering-PageRank algorithm
• big data
• optimization
• reliability

Acknowledgements

The authors gratefully acknowledge the field data support by Central-Southern Safety & Environment Technology Institute, as well as Mr Meng Cheng for his helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by National Natural Science Foundation of China [grant numbers 51308429 and 51708436] and the STRIP-WUT [grant number 20171049702004].