Prediction of average in-depth temperature of asphalt pavement using surface temperature measured during intelligent compaction

ABSTRACT

This paper presents a novel equation (named the ‘STKAR equation’) to predict the average in-depth temperature of asphalt layers in real-time during compaction using the measured surface temperature and the estimated temperature gradient at the asphalt surface. The motivation for the development of this method is the fact that the asphalt surface temperature measured during asphalt compaction using infrared radiation (IR) sensors mounted on state-of-the-art intelligent compaction (IC) rollers does not represent the average temperature in the asphalt layer, which governs its mechanical properties. Further, current asphalt temperature prediction models require the elapsed time since asphalt paving to predict the in-depth temperature of the asphalt layer during compaction. However, the measurement of elapsed time for each spot on the asphalt layer during compaction has practical difficulties in the field. In this study, the STKAR equation was derived from the 1-D diffusion equation for the cooling process of a newly-laid hot asphalt layer during compaction in the field. The developed equation was validated using the temperature data obtained during the construction of a two-layer asphalt testbed in the field. The in-depth temperatures predicted using the STKAR equation showed excellent agreement with the in-depth temperatures measured in asphalt layers during compaction. Further, numerical simulations were carried out to examine the applicability of the STKAR equation to different field scenarios. The numerical results showed that the initial temperature of the asphalt mix, the thermal conductivity of the underlying support material and the initial temperature of the underlying support significantly affect the in-depth temperature profile of asphalt layers, while asphalt density and asphalt specific heat content have negligible effects on the in-depth temperature profile. The STKAR equation was further refined by incorporating the factors which affect the in-depth temperature profile of asphalt layers during compaction.

KEYWORDS:

• Intelligent compaction
• asphalt surface temperature
• asphalt pavements
• 1-D heat diffusion
• pavement temperature profile
• heat transfer modelling

Acknowledgements

This research work is part of a research project (Project number: IH18.05.1) sponsored by the SPARC Hub (https://sparchub.org.au) in the Department of Civil Engineering, Monash University, funded by the Australian Research Council (ARC) Industrial Transformation Research Hub (ITRH) Scheme (Project ID: IH180100010). The financial and in-kind support of AfPA, the Department of Transport (DoT), Victoria and Monash University is gratefully acknowledged. The financial support of ARC is also greatly appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Australian Research Council (ARC) Industrial Transformation Research Hub (ITRH) Scheme (grant number IH180100010).