Pavement performance prediction model development for Tehran

Document Type: Research Paper

Authors

1 MSc. Grad.,, School of Civil Engineering, College of Engineering, University of Tehran, Iran

2 Assistant Professor, School of Civil Engineering, College of Engineering, University of Tehran, Iran

3 Research Assistant, School of Civil Engineering, College of Engineering, University of Tehran, Iran

4 Researcher, Tarahan Parseh Transportation Research Institute, Iran

5 Professor, Department of Civil Engineering, Monash University, Australia

10.22119/ijte.2020.152599.1437

Abstract

Highways and in particular their pavements are the fundamental components of the road network. They require continuous maintenance since they deteriorate due to changing traffic and environmental conditions. Monitoring methods and efficient pavement management systems are needed for optimizing maintenance operations. Pavement performance prediction models are useful tools for determining the optimal time for these actions. However, incorporating the model components into a pavement management system is highly important to ensure the model efficiency. This paper presents the existing pavement performance prediction models and introduces their components. A specific model is reproduced for Tehran traffic and environmental conditions adapted from the Pavement Health Track (PHT) model. This new model comprises four different sub-models including crocodile cracks, rutting, transverse cracking, and roughness prediction models. The study presents the software tool industrialized based on the model and presents the associated calibration and validation. Validation of the model for Tehran city shows that this new model has a high prediction accuracy. Also, it is a practical tool for pavement condition predictions across Tehran as it needs fewer data requirements compared with other complicated models. This study shows that using the new model may lead to an organized maintenance budgeting as well as a decrease in time and cost of operations.

Keywords


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