Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content

Опубліковано:
Номер: Випуск 30(2024)
Розділ: Будівництво та цивільна інженерія
Cторінковий інтервал статті: 157-173
Ключові слова: foamed bitumen; warm mix asphalt; Neural Network; support vector regression model; Machine learning.
Як цитувати статтю: Ali Saleh, László Gáspár. Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content. Dorogi і mosti [Roads and bridges]. Kyiv, 2024. Issue 30. P. 157–173 [in Ukrainian].
Як цитувати статтю (references): Ali Saleh, László Gáspár. Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content. Dorogi і mosti [Roads and bridges]. Kyiv, 2024. Issue 30. P. 157–173 [in Ukrainian].

Автори

Széchenyi István University, Faculty of Civil Engineering, Győr, Hungary
https://orcid.org/0000-0002-6575-0297
KTI Hungarian Institute for Transport Sciences and Logistics Non-Profit Ltd., Budapest, Hungary
https://orcid.org/0000-0002-0574-4100

Анотація

Introduction. There is a world-wide trend to also increase the sustainability of the road sector. The growing use of various industrial by-products, together with economical and eco-friendly construction and maintenance techniques can be observed in many countries.

Problem Statement. The utilization of warm mix asphalt and the use of relatively high share of reclaimed asphalt materials in new asphalt mixtures can have negative features, as well.

Purpose. Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content was aimed at based on Hungarian laboratory test series.

Materials and Methods. Three models were developed for the prediction of indirect tensile strength, this important asphalt mechanical parameter of warm mix asphalt as a function of Foamed Bitumen Content (FBC) and the RAP share in the new asphalt mixture. Among others, linear regression analysis and support vector regression (SVR) models were applied.

Results. A comparison performed between Random Forest and Neural Network models illustrates and proves the versatility of machine learning techniques in predicting asphalt indirect tensile strength values both in wet and dry conditions. The research work enhances our understanding of the multifaceted dynamics influencing the performance of asphalt mixtures, offering valuable insights for optimizing pavement design and construction practices in diverse environmental conditions. The model developed successfully captures the relationship between the ITS (wet and dry) metric and its contributing factors, Foamed Bitumen Content (FBC) and RAP, with a high R-squared value.

Посилання

  1. Roberts, F. L., Mohammad, L. N.,Wang, L. B.: History of Hot Mix Asphalt Mixture Design in the United States. 2011 [in English].
  2. Cooper, S. B. et al.: Balanced asphalt mixture design through specification modification: Louisiana’s experience, Transportation Research Record 247 (1), 2014. DOI: https://doi.org/ 10.3141/2447-10 [in English].
  3. Baghaee M. T., Baaj, H.: Application of compressible packing model for optimization of asphalt concrete mix design. Construction and Building Materials, 159, 2018. P. 530–539 [in English].
  4. Kar, S. S. et al.: Impact of low viscosity grade bitumen on foaming characteristics. Journal of the South African Institution of Civil Engineering. 60 (2), 2018. P. 40–52 [in English].
  5. Csanyi, L. H.: Foamed asphalt in bituminous paving mixtures. Highway Research Board Bulletin, 160, 1957. P. 108–122 [in English].
  6. Wirtgen, G.: Wirtgen cold recycling technology. 2012 [in English].
  7. Williams, B. A., Copeland, A., Ross, T. C.: Asphalt pavement industry survey on recycled materials and warm-mix asphalt usage: 2017. 2018 [in English].
  8. Iwanski, M. M., Chomicz-Kowalska, A., Maciejewski, K.: Resistance to moisture-induced damage of half-warm-mix asphalt concrete with foamed bitumen. Materials. 13 (3), 2020, 654. DOI: http://doi.org/ma13030654 [in English].
  9. Kar, S. S. et al.: Impact of binder on properties of foamed bituminous mixtures. Proceedings of Institution of Civil Engineers: Construction Materials. 170 (4), 2017. P. 194–204 [in English].
  10. Hoy, M., Horpibulsuk, S., Arulrajah, A.: Strength development of Recycled Asphalt Pavement - Fly ash geopolymer as a road construction material. Construction and Building Materials, 117, 2016. P. 209–219 [in English].
  11. Dong, F. et al.: Comparison of high temperature performance and microstructure for foamed WMA and HMA with RAP binder. Construction and Building Materials, 134, 2017. P. 594–601 [in English].
  12. Li, J., Fu, W., Zang, H.: Design Method for Proportion of Cement-Foamed Asphalt Cold Recycled Mixture. MATEC Web of Conferences, 142, 2018, 02002 [in English].
  13. Li, Z. et al.: Effect of cement on the strength and microcosmic characteristics of cold recycled mixtures using foamed asphalt. Journal of Cleaner Production, 230, 2019, P. 956–965 [in English].
  14. Bala, N., Napiah, M., Kamaruddin, I.: Nanosilica composite asphalt mixtures performance-based design and optimisation using response surface methodology. International Journal of Pavement Engineering. 21 (1), 2020. P. 29–40 [in English].
  15. Abreu, L. P. F. et al.: Suitability of different foamed bitumens for warm mix asphalts with increasing recycling rates. Construction and Building Materials, 142, 2017. P. 342–353 [in English].
  16. Arefin, M. S. et al.: Effect of short-term and long-term ageing on dynamic modulus of foamed warm mix asphalt. International Journal of Pavement Engineering. 21 (4), 2020. P. 524–536 [in English].
  17. Kar, S. S. et al.: Impact of Chemical Composition on Foaming Characteristics of Asphalt Binder. Journal of Transportation Engineering, Part B: Pavements. 146 (3), 2020, 04020045 [in English].
  18. Bairgi, B. K., Mannan, U. A., Tarefder, R. A.: Influence of foaming on tribological and rheological characteristics of foamed asphalt. Construction and Building Materials. 205, 2019. P. 186–195 [in English].
  19. Hasan M. R. et al.: Characterizations of foamed asphalt binders prepared using combinations of physical and chemical foaming agents. Construction and Building Materials, 204, 2019. P. 94–104 [in English].
  20. Taziani, E. A. et al.: Investigation on the combined effect of fibers and cement on the mechanical performance of foamed bitumen mixtures containing 100 % RAP. Advances in Materials Science and Engineering, 2016 [in English].
  21. Chomicz-Kowalska, A., Ramiaczek, P.: Comparative Evaluation and Modification of Laboratory Compaction Methods of Road Base Mixtures Manufactured in Low-emission CIR Technology with Foamed Bitumen and Bitumen Emulsion. In: Procedia Engineering. Elsevier Ltd. 2017. P. 560–569 [in English].
  22. Hou, Y. et al.: Dynamic Characteristics of Warm Mix Foamed Asphalt Mixture in Seasonal Frozen Area. Advances in Materials Science and Engineering, 1825643, 2019 DOI: https://doi.org/10.1155/20191825643 [in English].
  23. Guatimosim, F. V. et al.: Laboratory and field evaluation of cold recycling mixture with foamed asphalt. Road Materials and Pavement Design, 19 (2), 2018. Р. 385–399 [in English].
  24. Sánchez, D. B., Airey, G., Caro, S., Grenfell, J.: Effect of foaming technique and mixing temperature on the rheological characteristics of fine RAP-foamed bitumen mixtures. Road Materials and Pavement Design, 21 (8) 2020, 2143–2159. DOI: https://doi.org/10.1080/14680629. 2019. 159322 [in English].
  25. Gandhi, T., Rogers, W., Amirkhanian, S.: Laboratory evaluation of warm mix asphalt ageing characteristics. International Journal of Pavement Engineering, 11 (2), 2010. Р. 133–142 [in English].
  26. Kamran, F. et al.: Performance evaluation of stabilized base course using asphalt emulsion and asphaltenes derived from Alberta oil sands. In: Transportation Research Record. SAGE Publications Ltd. 2675 (10), 2021. Р. 764–775. DOI: https://doi.org/10.1177/03611981211012692 [in English].
  27. Vapnik, V.: The nature of statistical learning theory. Springer Science & Business Media. 1999 [in English].
  28. Gopalakrishnan, A., Kim, S.: Support Vector Machines Approach to HMA Stiffness Prediction. Journal of Engineering Mechanics, 137 (2), 2010. Р. 324–335. DOI: https://doi.org/10.1061/ (ASCE)EM.1943-7889. 0000214 [in English].
  29. Maalouf, M., Khoury, N., Trafalis, T. B.: Support vector regression to predict asphalt mix performance. International Journal for Numerical and Analytical Methods in Geomechanics, 32 (16), 2008. Р. 1989–1996 [in English].
  30. Nazemi, M., Heidaripanah, A.: Support vector machine to predict the indirect tensile strength of foamed bitumen-stabilised base course materials. Road Materials and Pavement Design, 17 (3), 2016. Р. 768–778 [in English].
  31. Zhao, Y. et al.: Prediction of air voids of asphalt layers by intelligent algorithm. Construction and Building Materials, 317, 2022. 125908 [in English].
  32. Ziari, H. et al.: Prediction of pavement performance: Application of support vector regression with different kernels. Transportation Research Record, 2589, 2016. Р. 135–145 [in English].
  33. Karballaeezadeh, N. et al.: Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Engineering Applications of Computational Fluid Mechanics, 13 (1), 2019. Р. 188–198.
  34. Huang, Y., Li, J., Fu, J.: Review on application of artificial intelligence in civil engineering, CMES - Computer Modeling in Engineering and Sciences, 121 (3), Tech Science Press, 2019. Р. 845–875. DOI: https://doi.org/10.32604/cmes.2019.07653 [in English].
  35. Gong, H. et al.: Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 2018. Р. 890–897 [in English].
  36. Fathi, A. et al.: Parametric Study of Pavement Deterioration Using Machine Learning Algorithms. Proceedings of International Airfield and Highway Pavements Conference, 2019. Р. 12. DOI: http://dx.doi.org/10.1061/9780784482476.004 [in English].
  37. Gong, H. et al.: Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests. Construction and Building Materials, 204, 2019. Р. 203–212 [in English].
  38. Zhan, Y. et al.: Effect of aggregate properties on asphalt pavement friction based on random forest analysis. Construction and Building Materials, 292. 2021 [in English].
  39. Daneshvar, D., Behnood, A.: Estimation of the dynamic modulus of asphalt concretes using random forests algorithm. International Journal of Pavement Engineering, 23 (2), 2022. Р. 250–260 [in English].