Development of a Temperature-Sensitive Modulus Model for Flexible Pavements Based on Regional Climatic and Traffic Variations

Abstract

In order to manage the resource used in road construction and for decreasing costs to optimum levels, maintenance and repair needs should be determined correctly and required actions should be applied at the right time. The current structural and functional condition of the pavement must be evaluated for management of the pavement of the road network. Current condition can be calculated by methods determined by using non-destructive testing methods with Falling Weight Deflectometers (FWD). But in this method, the test results of asphalt effected by asphalt temperature. Different climate types are seen in different regions in Turkey and hot-cold temperature differences are more common compared to other countries. Therefore, it would not be appropriate to design the pavement with a single reference temperature. In this study, reference temperatures for different regions were calculated based on meteorological data, depending on traffic volumes. Also, in this study, the temperature effect of Hot Mix Asphalt (HMA) layers on deflectometer measurements of Flexible Pavements were investigated and a layer modulus temperature correction method was developed for bituminous HMA layers in the light of field studies conducted. This article discusses the process of updating a temperature-dependent linear elasticity modulus model using new experimental data. A model based on elasticity modulus values obtained from experiments conducted at various temperatures was initially established, and the study explores how this model can be updated with new experimental data. The update process involves defining a tolerance range around the temperature at which the new experiment was conducted. The elasticity modulus values from previous experiments that fall within this range are averaged to adjust the trend line of the model. The impact of different tolerance ranges on the trend line is examined, showing that a narrow tolerance range leads to minimal changes, while a wider range can cause significant shifts in the trend line. This method ensures that the model remains robust and adaptable to new data while accounting for variability in measurements.

Keywords

• Temperature Correction
• Deflection
• HMA
• asphalt
• reference temperature
• FWD
• HWD

References

1. KGM. Yol Ağı Bilgileri. 2022 [cited 2022 21.11.2022]; Available from: https://www.kgm.gov.tr/sayfalar/kgm/sitetr/kurumsal/yolagi.aspx.
2. KGM. 2010 Mali Yılı Performans Programı. 2010 [cited 2022 21.11.2022]; Available from: https://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Kurumsal/PerformansProgrami/10performans.pdf.
3. KGM. 2015 Yılı Performans Programı. 2015 [cited 2022 21.11.2022]; Available from: https://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Kurumsal/PerformansProgrami/15performans.pdf.
4. KGM. 2019 - 2023 Stratejik Planı. 2019 [cited 2022 21.11.2022]; Available from: https://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Kurumsal/StratejikPlan/strateji(2019-2023).pdf.
5. Nguyen, T., J.Y. Tan, and N.Y. Ho, Deflection bowl parameters for falling weight deflectometer testing: data collection and threshold benchmarking. International Journal of Pavement Engineering, 2023. 24(2): p. 2034815.
6. Li, M., et al., Surface layer modulus prediction of asphalt pavement based on LTPP database and machine learning for Mechanical-Empirical rehabilitation design applications. Construction and Building Materials, 2022. 344: p. 128303.
7. Shahin, M.Y., Pavement management for airports, roads, and parking lots. 1994.
8. Choi, J.W., et al., New layer-moduli back-calculation method based on the constrained extended Kalman filter. Journal of Transportation Engineering, 2010. 136(1): p. 20-30.

9. Nakhaei, M. and D.H. Timm, A new methodology to improve backcalculation of flexible pavements with stabilized foundations. Construction and Building Materials, 2023. 368: p. 130405.
10. Gupta, K., et al., Improved reliability of FWD test results and correlations with resilient modulus. 2022.
11. Qiao, Y., et al., Assessing impacts of climate change on flexible pavement service life based on Falling Weight Deflectometer measurements. Physics and Chemistry of the Earth, Parts A/B/C, 2020. 120: p. 102908.
12. Yang, S., et al., Comparisons between Asphalt Pavement Responses under Vehicular Loading and FWD Loading. Advances in Materials Science and Engineering, 2020. 2020: p. 5269652.
13. Fernando, E.G., W. Liu, and D. Ryu, Development of a procedure for temperature correction of backcalculated AC modulus. 2001, Texas Transportation Institute, Texas A & M University System.
14. Zheng, Y., P. Zhang, and H. Liu, Correlation between pavement temperature and deflection basin form factors of asphalt pavement. International Journal of Pavement Engineering, 2019. 20(8): p. 874-883.
15. Pais, J., et al., The adjustment of pavement deflections due to temperature variations. International Journal of Pavement Engineering, 2020. 21(13): p. 1585-1594.
16. Sağlık, A. and A. Güngör, Karayolları esnek üstyapılar projelendirme rehberi. Baskı, Karayolları Genel Müdürlüğü, Ankara, 2008.
17. Rigabadi, A., M. Rezaei Zadeh Herozi, and A. Rezagholilou, An attempt for development of pavements temperature prediction models based on remote sensing data and artificial neural network. International Journal of Pavement Engineering, 2022. 23(9): p. 2912-2921.
18. Khan, Z.H., M.R. Islam, and R.A. Tarefder, Determining asphalt surface temperature using weather parameters. Journal of Traffic and Transportation Engineering (English Edition), 2019. 6(6): p. 577-588.
19. Lukanen E.O., S., R.N., Briggs, R., LTPP Guide To Asphalt Temperature Prediction And Correction. 2002. p. 71.
20. Ntramah, S., et al., Evaluation of Selected Empirical Models for Asphalt Pavement Temperature Prediction in a Tropical Climate: The Case of Ghana. Sustainability, 2023. 15(22): p. 15846.
21. Lai, J.-C., J. Liu, and C.-W. Huang, The Application of Frequency-Temperature Superposition Principle for Back-Calculation of Falling Weight Deflectometer. Applied Sciences, 2020. 10(1): p. 132.
22. Hu, X., et al., Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate. Applied Sciences, 2023. 13(1): p. 367.
23. Walia, A., et al., Development of a temperature prediction model for asphalt pavements considering air temperature data of preceding hours. International Journal of Pavement Engineering, 2023. 24(2): p. 2132245.
24. Highways, N., DMRB: Pavement CS 229 Data for pavement assessment (formerly HD 29/08). 2020, National Highways: Birmingham UK. p. 128.
25. A/S, D.I., Dynatest FWD/HWD Test Systems Owner's Manual Version 2.8.17. 2018.
26. COST336, Use of falling weight deflectometers in pavement evaluation. 2005, European Commission, Directorate General Transport.
27. Fіdan, S. and B. Karasulu. Clustering methods comparison for optimization of adaptive neural fuzzy inference system. in 2022 30th Signal Processing and Communications Applications Conference (SIU). 2022. IEEE.
28. Honda, K., et al. A Noise Clustering-induced Robust Adaptive Network-based Fuzzy Inference System for Classification. in 2022 International Joint Conference on Neural Networks (IJCNN). 2022. IEEE.