Modeling Transition Probabilities and Maintenance Schedules for Pavement Networks Using a Markov Model

Author's Department

Construction Engineering Department

Second Author's Department

Construction Engineering Department

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https://doi.org/10.1007/978-3-031-60419-5_3

All Authors

M. Kotb, O. Hosny

Document Type

Research Article

Publication Title

Lecture Notes in Civil Engineering

Publication Date

1-1-2024

doi

10.1007/978-3-031-60419-5_3

Abstract

Pavement deterioration is an inevitable part of the road network lifecycle that typically results from several natural and artificial factors. Non-destructive evaluation methods such as visual inspections, sensors, and others are used to evaluate road network conditions and are usually presented by the pavement condition index (PCI). However, the unique conditions of different roads and the scarcity of data introduce challenges in predicting the future PCI of a road. This paper introduces a new method for developing a pavement network's transition probability matrix (TPM) using Markovian chain to predict roads’ future conditions. Data from the Long-Term Pavement Performance database on 331 roads in the United States were used to develop the TPM. A maintenance, repair, and replacement (MRR) schedule was developed by optimizing the transition probabilities in the generated TPM. The current PCI, budget, and cost of different maintenance strategies are leveraged to create the MRR schedule. This work also introduces a new approach to generating the MRR schedule by combining targeted and random rehabilitation strategies with the model's TPM. The costs of targeted and random rehabilitation strategies are introduced to the model, allowing it to choose between both options depending on the outcome. By doing so, the probable outcomes of the schedule increase, allowing the model to produce an optimized MRR schedule that maximizes the overall average PCI of the network while sticking to budget constraints. The predicted PCIs from the developed TPM showed a reduced error of 45%, compared to previous works.

First Page

27

Last Page

41

Comments

Conference Paper. Record derived from SCOPUS.

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