An application of Markov chains to predict the evolution of performance indicators based on pavement historical data

Abstract

One of the main awareness for a road infrastructures manager is to increase its efficiency under limited resources. Pavement Management Systems aim, at last, to support road administrations in the decision-making process regarding its management policy and long-term strategies for maintenance and rehabilitation activities. While several road administrations are putting efforts in developing optimisation methodologies to enhance their decision making process, many still lack of data that allows the development of reliable prediction models for pavement performance. This is a key aspect to develop and test decision-making methodologies. Although there are several prediction models available in the literature, their practical applications are often limited to the very specific network from which data were retrieved at first and to a specific performance indicator (PI). This paper presents a practical application of a Markov model to predict the evolution of five PIs – cracking, skid resistance, bearing capacity, longitudinal evenness and transverse evenness – and consequent combined PIs, using historical data from an extensive pavement database. The conversion for PIs is made through a standardisation procedure proposed by an European COST Action, which may be considered a reference classification system for road administrations. The presented model is intended to be an useful input for researchers and administrations willing to develop and test different optimisation approaches.

Keywords:

• Road infrastructure management
• pavement deterioration
• global performance indicators
• Markov chains
• prediction models

Acknowledgements

The authors wish to thank to: (1) ANI – ‘Agência Nacional de Inovação’, for the financial support through the Operational Programme for Competitiveness Factors (COMPETE) for the project R&D SustIMS – Sustainable Infrastructure Management Systems (FCOMP-01-0202- FEDER-023113); (2) FCT – ‘Fundação para a Ciência e a Tecnologia’, for the PhD studentship with reference [SFRH/BD/85984/2012] and the post-doctoral Grant fellowship with reference [SFRH/BPD/94792/2013].

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Fundação para a Ciência e a Tecnologia [SFRH/BD/85984/2012; SFRH/BPD/94792/2013]; Agência Nacional de Inovação [FCOMP-01-0202-FEDER-023113].