Determining the Effectiveness of Asphalt Concrete Overlays on Rigid Pavement Using Discrete-Event Simulation

Authors

  • Nathee Athigakunagorn Kasetsart University

DOI:

https://doi.org/10.4186/ej.2017.21.4.171

Abstract

The budget for preserving highway pavement conditions is increasing every year, as well as the impulse to seek transparency of maintenance decisions. This paper presents a new paradigm in applying discrete-event simulation (DES) to capture an indeterministic process of rigid pavement deterioration with an implementation of rehabilitation treatment, namely, asphalt concrete (AC) overlays. This paradigm should facilitate decision makers in establishing the most appropriate maintenance procedure. In this study, the deterioration model was constructed and run over 50 simulated years with various numbers of International Roughness Index (IRI) to determine the rehabilitation cost and average IRI over the analysis period. The result shows that when focusing only on the treatment cost, the best treatment type is Minimal Surface Preparation with 4” AC overlay (MSP). If the treatment performance is considered as the objective for decision making, Crack/Break and Seat section with 8” AC overlay (CB & S8) provides the best pavement condition. Nevertheless, when both objectives are simultaneously considered, Crack/Break and Seat section with 4” AC overlay (CB & S4) outperforms the other rehabilitation types, specifically when the treatment is implemented on a good condition pavement with a relatively low IRI. MSP is, however, the most suitable when a pavement is in a less preferable condition with a relatively high IRI.

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Author Biography

Nathee Athigakunagorn

Department of Civil Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Nakhon Pathom 73140, Thailand

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Published In
Vol 21 No 4, Jul 31, 2017
How to Cite
[1]
N. Athigakunagorn, “Determining the Effectiveness of Asphalt Concrete Overlays on Rigid Pavement Using Discrete-Event Simulation”, Eng. J., vol. 21, no. 4, pp. 171-182, Jul. 2017.