Investigation of the effect of global warming on the bitumen performance grading of Iran

Document Type : Research Paper

Authors

1 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Abstract

In recent decades, population growth, increasing urban development, and polluting industries have led to an increase in the production of greenhouse gases and the phenomenon of global warming in various regions of the world. Asphalt pavements, as the main infrastructure of the road transportation system, may suffer failures with high repair costs due to their rheological properties under the influence of this phenomenon. In this study, the Prophet model was used to predict the temperature for the years 1400 to 1421 in three 7-year periods under three optimistic, intermediate, and pessimistic scenarios. The prediction accuracy of this model based on the fitting coefficient was more than R2=0.8 on average. Based on the prediction, the country's demand for bitumen with different functional classifications is then presented in comparison with the available quantities. By predicting and calculating the functional classification of bitumen in 1440 and presenting the results at two confidence levels of 50% and 98% for 34 meteorological stations in the 31 provinces of Iran, a long-term overview of the temperature situation and the required functional classification of bitumen based on the meteorological information of each station is also provided. The increase in the average temperature of the studied stations at the end of the 21-year period was 0.73 and 1.094 degrees of centigrade at a confidence level of 50% and 98%, respectively. According to these results, most of the studied areas will experience a significant temperature increase in the coming years, and this phenomenon will increase the high and low temperatures of bitumen performance, which will require the use of PG76-10 and PG82-10 bitumen. The increase in high and low performance temperatures will require the use of alternative bitumens and the use of appropriate bitumen additives for all climatic conditions.

Keywords

Main Subjects

References

  1. Asefzadeh, L. Hashemian, A. Bayat, Development of statistical temperature prediction models for a test road in Edmonton, Alberta, Canada, International Journal of Pavement Research and Technology (2017), doi: http://dx.doi.org/10.1016/j.ijprt.2017.05.0. (n.d.).

Aditya Satrio, C. B., W. Darmawan, B. U. Nadia,  and N. H. (2021). Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science 179:524–32. doi:10.1016/J.PROCS.2021.01.036.

Aflaki, S., & Tabatabaee, N. (2009). Proposals for modification of Iranian bitumen to meet the climatic requirements of Iran. Construction and Building Materials, 23(6), 2141–2150. https://doi.org/10.1016/j.conbuildmat.2008.12.014

Arslan, S. (2022). A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data. PeerJ Computer Science 8:e1001. doi:10.7717/peerj-cs.1001.

Bahia, H.U., Anderson, D. A. (1993). The pressure ageing vessel (PAV): a test to simulate rheological changes due to field ageing. In: Physical Properties of Asphalt Cement Binders Symposium, Dallas, 1993.

Edenhofer O, Pichs-Madruga R, Sokona Y, Kadner S, Minx JC, Brunner S,  et al. (2014). Technical Summary. In: Climate Change 2014: Mitigation of Climate Change. Contrib Work Gr III to Fifth Assess Rep Intergov Panel Clim Chang. 33–107.

Francesco Viola, C. C. (2015). Effect of climate change on asphalt binder selection for road construction in Italy (pp. 40–47). https://doi.org/http://dx.doi.org/10.1016/j.trd.2015.04.012

Jagannathan, J., & Divya, C. (2021). Time Series Analyzation and Prediction of Climate using Enhanced Multivariate Prophet. International Journal of Engineering Trends and Technology, 69(10), 89–96. https://doi.org/10.14445/22315381/IJETT-V69I10P212.

JT, H. (2003). Climate change: causes, effects, and solutions. John Wiley & Sons.

Kennedy TW, Huber GA, Harrigan ET, Cominsky RJ, Hughes CS, Von Quintus H,  et al. (1994). Superior performing asphalt pavements (Superpave): The product of the SHRP asphalt research program.

Ma, L.,  et al. (2021). Comprehensive review on the transport and reaction of oxygen and moisture towards coupled oxidative ageing and moisture damage of bitumen. Construction and Building Materials, 283, 122632.

Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices. In Forecasting (Vol. 3, Issue 3, pp. 644–662). https://doi.org/10.3390/forecast3030040

Mohammad Daffa Haris, Didit Adytia, A. W. R. (2022). 2022 International Conference on Data Science and Its Applications (ICoDSA) | 978-1-6654-8665-1/22/$31.00 ©2022 IEEE |. https://doi.org/DOI:10.1109/ICoDSA55874.2022.9862869.

RMTO. (2022). Iran’s Road Maintenance and Transportation Organization (RMTO). Retrieved from http://www.rmto.ir.

S.A. Tabatabaie, H. Z. and M. K. (2008). Modeling Temperature and Resilient Modulus of Asphalt Pavements for Tropic Zones of Iran. Asian Journal of Scientific Research, 1. pp, 577-588. https://doi.org/10.3923/ajsr.2008.579.588

Sedighian-Fard M, S. N. (n.d.). Analysis of Regression-Based Models for Prediction of Depth Temperature of Asphalt Layers-A review. Amirkabir J. Civil Eng., 3985-4006.

Sepaspour. R, Hajikarimi. P, M. F. (2021). Investigating the effect of global warming on the functional classification of bitumens using Time series method. The 13th Bitumen, Asphalt & Machinery Conference & Exhibition, 9-11 November 2021 -Roads, Hous.

T., S. (2014). Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge university press.

Thiyagarajan, Karthick; kodagoda, sarath; Ulapane, Nalika; prasad, M. (2020). A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System. TechRxiv. Preprint.

Toni Toharudin , Resa Septiani Pontoh , Rezzy Eko Caraka , Solichatus Zahroh, Y. L. & R. C. C. (2021). Employing long short-term memory and Facebook prophet model in air temperature forecasting, Communications in Statistics - Simulation and.

Vishwas, B., and A. Patel. 2020. Hands-on Time Series Analysis with Python. From Basics to Bleeding Edge Techniques. Berkeley, CA: Apress. doi: 10.1007/978-1-4842-5992-4. (n.d.).

Yenidogan, I., A. Cayir, O. Kozan, T. Dag,  and C. A. (2018). Bitcoin forecasting using ARIMA and PROPHET. UBMK 2018 - 3rd International Conference on Computer Science and Engineering, 621–24. 10.1109/UBMK.2018. 8566476.

Zar Zar Oo, S. P. (2020). Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina. International Journal of Applied Mathematics, Electronics and Computers, 8(4), 263–267.

Journal of Transportation Infrastructure Engineering
Volume 9, Issue 2 - Serial Number 34
September 2023
Pages 17-32

Files

History

  • Receive Date: 29 March 2023
  • Revise Date: 16 May 2023
  • Accept Date: 20 May 2023

Share

How to cite

Statistics