The Optimization of Mix Proportion of Hot Mix Asphalt for Sustainable Flexible Pavements: Experimental Study and Grey Taguchi Relational Analysis

Document Type : Regular Article

Authors

1 Professor, Civil Engineering, Department, Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India

2 Professor, Civil Engineering Department, JNTUH College of Engineering, Hyderabad, Telangana, India

Abstract

Most of the Indian black topped roads have been damaged due to adverse weather and heavy load distresses. Many researchers have focused on improving durability of Hot Mix Asphalt (HMA) pavements. The factors influencing durability of HMA are Binder content, Combined aggregate gradation, type of Filler and addition of Fiber. In order to optimize the combination of variables used in HMA mix design, Grey relational analysis by using Taguchi technique is used, where many parameters can be analyzed at a time with more accuracy. Multiple performance measurements like Stability, Flow, Bulk specific gravity of the mix (Gmb), Theoretical maximum specific gravity of the mix (Gmm), Voids in Mineral Aggregate (VMA), Air voids (Va) and Voids Filled with Bitumen (VFB) are considered. Bituminous Concrete (BC) mix was optimized using L9 Orthogonal array considering four parameters such as Fiber content, Filler combination, Binder content and Combined Aggregate Gradation, with three levels having seven performance measurements. The most significant parameter and percent contribution of each parameter of BC mix are analyzed by Analysis of Variance (ANOVA) using Grey Taguchi technique. The analysis was done by considering two Gradation conditions (Coarse gradation and Fine gradation) based on voids in mix. From the analysis, it can be concluded that all parameters are significant except bitumen content for optimizing BC mix.

Keywords

Main Subjects


[1]     Arifuzzaman M. Advanced ANN Prediction of Moisture Damage in CNT Modified Asphalt Binder. J Soft Comput Civ Eng 2017;1:1–11. https://doi.org/10.22115/scce.2017.46317.
[2]     Leiva-Villacorta F, Vargas-Nordcbeck A. Neural Network Based Model to Estimate Dynamic Modulus E* for Mixtures in Costa Rica. J Soft Comput Civ Eng 2019;3:1–15. https://doi.org/10.22115/SCCE.2019.188006.1110.
[3]     Karanjule DB, Bhamare SS, Rao TH. Process Parameter Optimization for Minimizing Springback in Cold Drawing Process of Seamless Tubes Using Advanced Optimization Algorithms. J Soft Comput Civ Eng 2018;2:72–90. https://doi.org/10.22115/SCCE.2018.136009.1072.
[4]     Mukhopadhyay A, Sahoo S. A Grey-Fuzzy Based Approach for the Optimization of Corrosion Resistance of Rebars Coated with Ternary Electroless Nickel Coatings. J Soft Comput Civ Eng 2022;6:107–27. https://doi.org/10.22115/SCCE.2022.326903.1401.
[5]     Rezazadeh Eidgahee D, Jahangir H, Solatifar N, Fakharian P, Rezaeemanesh M. Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches. Neural Comput Appl 2022;34:17289–314. https://doi.org/10.1007/s00521-022-07382-3.
[6]     Hu J, Ma T, Yin T, Zhou Y. Foamed warm mix asphalt mixture containing crumb rubber: Foaming optimization and performance evaluation. J Clean Prod 2022;333:130085. https://doi.org/10.1016/j.jclepro.2021.130085.
[7]     Daneshvar MH, Saffarian M, Jahangir H, Sarmadi H. Damage identification of structural systems by modal strain energy and an optimization-based iterative regularization method. Eng Comput 2022:1–21. https://doi.org/10.1007/s00366-021-01567-5.
[8]     Ren Z, Tan Y, Huang L, Yu H. Optimization of automatic extraction procedure for particles in asphalt mixture towards superior robustness and accuracy. Constr Build Mater 2022;342:128002. https://doi.org/10.1016/j.conbuildmat.2022.128002.
[9]     Onyelowe KC, Rezazadeh Eidgahee D, Jahangir H, Aneke FI, Nwobia LI. Forecasting Shear Parameters, and Sensitivity and Error Analyses of Treated Subgrade Soil. Transp Infrastruct Geotechnol 2022. https://doi.org/10.1007/s40515-022-00225-7.
[10]   Polo-Mendoza R, Martinez-Arguelles G, Peñabaena-Niebles R. A multi-objective optimization based on genetic algorithms for the sustainable design of Warm Mix Asphalt (WMA). Int J Pavement Eng 2022:1–21. https://doi.org/10.1080/10298436.2022.2074417.
[11]   Ozbay E, Oztas A, Baykasoglu A, Ozbebek H. Investigating mix proportions of high strength self compacting concrete by using Taguchi method. Constr Build Mater 2009;23:694–702. https://doi.org/10.1016/j.conbuildmat.2008.02.014.
[12]   Nirmala DB, Raviraj S. Experimental study of optimal self compacting concrete with spent foundry sand as partial replacement for M-sand using Taguchi approach. Sel Sci Pap - J Civ Eng 2016;11:119–30. https://doi.org/10.1515/sspjce-2016-0013.
[13]   Abdullah WS, Obaidat MT, Abu-Sa’da NM. Influence of Aggregate Type and Gradation on Voids of Asphalt Concrete Pavements. J Mater Civ Eng 1998;10:76–85. https://doi.org/10.1061/(asce)0899-1561(1998)10:2(76).
[14]   Shen S, Yu H. Analysis of Aggregate Gradation and Packing for Easy Estimation of Hot-Mix-Asphalt Voids in Mineral Aggregate. J Mater Civ Eng 2011;23:664–72. https://doi.org/10.1061/(asce)mt.1943-5533.0000224.
[15]   Awuah-Offei K, Askari-Nasab H. Aggregate Cost Minimization in Hot-Mix Asphalt Design. J Mater Civ Eng 2011;23:554–61. https://doi.org/10.1061/(asce)mt.1943-5533.0000211.
[16]   Rahman A, Ali SA, Adhikary SK, Hossain QS. Effect of fillers on bituminous paving mixes: An experimental study. J Eng Sci 2012;3:121–7.
[17]   Sung Do H, Hee Mun P, Suk keun R. A study on engineering characteristics of asphalt concrete using filler with recycled waste lime. Waste Manag 2008;28:191–9. https://doi.org/10.1016/j.wasman.2006.11.011.
[18]   Tapkin S. The effect of polypropylene fibers on asphalt performance. Build Environ 2008;43:1065–71. https://doi.org/10.1016/j.buildenv.2007.02.011.
[19]   Tortum A, Çelik C, Cüneyt Aydin A. Determination of the optimum conditions for tire rubber in asphalt concrete. Build Environ 2005;40:1492–504. https://doi.org/10.1016/j.buildenv.2004.11.013.
[20]   Tzeng CJ, Lin YH, Yang YK, Jeng MC. Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. J Mater Process Technol 2009;209:2753–9. https://doi.org/10.1016/j.jmatprotec.2008.06.046.
[21]   Ministry of Road Transport and Highways (MoRTH). Specifications for road and bridge works, Indian Roads Congress, New Delhi, India (Fifth Revision). 2013.
[22]   IS: 2386 (Part I) – 1963, Indian standard code of practice- Methods of test for aggregates for concrete particle size and shape, Bureau of Indian Standards, New Delhi, India (eleventh reprint august 1997). 1997.
[23]   IS: 2386 (Part III) – 1963, Indian standard code of practice- Methods of test for aggregates for concrete specific gravity, density, voids, absorption and bulking, Bureau of Indian Standards, New Delhi, India (eighth reprint march 1997).
[24]   IS: 2386 (Part IV) – 1963, Indian standard code of practice- Methods of test for aggregates for concrete mechanical properties, Bureau of Indian Standards, New Delhi, India (tenth reprint march 1997).
[25]   IS: 1201 – 1978 To IS: 1220 – 1978, Indian standard code of practice - Methods for testing tar and bituminous materials, Bureau of Indian Standards, New Delhi, India (First Revision).
[26]   American Society for Testing and Materials D1559-76, Standard test method for resistance to plastic flow of bituminous mixtures using Marshall apparatus.