AUT Journal of Civil Engineering

AUT Journal of Civil Engineering

A Goal Programming-Based Framework for Multi-Objective Optimization of Sustainable Concrete Pavements Mix Design

Document Type : Research Article

Authors
ali akbar shirzadi javid 1
Mohammad Ali Etebari Ghasbeh 2
1 School of Civil Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Narmak, Tehran, Iran.
2 School of Civil Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Narmak, Tehran, Iran
10.22060/ajce.2026.25066.5962
Abstract
The use of concrete pavement has significantly increased due to its advantages over asphalt pavement. Determining the concrete mix ratio for asphalt is a vital and crucial step in the construction process. Concrete should be strong, durable, and resistant to environmental degrading factors. This means it needs to withstand freezing, thawing, shrinkage, and harsh environmental conditions (such as heat and cold. These characteristics result in concrete having a longer lifespan and creating robust structures). One of the challenges in design is balancing quality and cost, which comes with its own complexities.. Over the past few years, the application of models and algorithms for multi-objective problems has been a focus of research. This study introduces adaptive neuro-fuzzy inference system goal programming (NFGPM) and fuzzy-goal programming model (FGPM) as expanded variants of basic goal programming models, as alternative tools for allocating asphalt concrete pavement mixtures proportions with multiple, varied objectives. The actual data laboratory experiment datasets were generated and used to develop proposed models. The outcomes of the proposed NFGPM’s mixture proportions and its prediction of concrete properties —such as slump, flexural strength, abrasion resistance, shrinkage and freeze–thaw behavior— are compared with experimental data. The study confirms that the adaptive neuro-fuzzy inference system goal programming model proposed herein can deliver the most cost-effective and optimally performing concrete pavement mixture proportions.
Keywords
Concrete Pavement
Mixture Proportioning
Multi-Objective
Goal Programming
Adaptive Neuro-fuzzy Inference System
Subjects
[1] Alaneme G.U., Olonade K.A., Esenogho E. Critical review on the application of artificial intelligence techniques in the production of geopolymer-concrete. SN Appl. Sci. 2023; 5: 217. https://doi.org/10.1007/s42452-023-05447-z
[2] Delatte N.J. Concrete pavement design, construction, and performance. CRC Press; 2014.
[3] Kohn S., Tayabji S., Okamoto P., Rollings R., Detwiller R., Perera R., Barzegar H. Best Practices for Airport Portland Cement Concrete Pavement Construction (Rigid Airport Pavement). Federal Aviation Administration (FAA); 2003.
[4] Li B., Ke G., Zhou M. Influence of manufactured sand characteristics on strength and abrasion resistance of pavement cement concrete. Constr. Build. Mater. 2011; 25(10): 3849-3853. https://doi.org/10.1016/j.conbuildmat.2011.04.004
[5] Lee J.H., Yoon Y.S., Kim J.H. A new heuristic algorithm for mix design of high-performance concrete. KSCE J. Civ. Eng. 2012; 16(6): 974-979. https://doi.org/10.1007/s12205-012-1011-0
[6] Kosmatka S.H., Kerkhoff B., Panarese W.C. Design and control of concrete mixtures. Portland Cement Assoc; 2011.
[7] Herbold K.D. Using Monte Carlo simulation for pavement cost analysis. Public Roads. 2000; 64(3): 2-6.
[8] Sarıdemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Adv. Eng. Softw. 2009; 40(9): 920-927. https://doi.org/10.1016/j.advengsoft.2008.12.008
[9] Mohammadi Golafshani E., Kim T., Behnood A., Ngo T., Kashani A. Sustainable mix design of recycled aggregate concrete using artificial intelligence. J. Clean. Prod. 2024; 442: 140994. https://doi.org/10.1016/j.jclepro.2024.140994
[10] Özcan F., Atiş C.D., Karahan O., Uncuoğlu E., Tanyildizi H. Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv. Eng. Softw. 2009; 40(9): 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005
[11] Sobhani J., Najimi M., Pourkhorshidi A.R., Parhizkar T. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Constr. Build. Mater. 2010; 24(5): 709-718. https://doi.org/10.1016/j.conbuildmat.2009.10.037
[12] Gonzalez M., Tighe S.L., Hui K., Rahman S., de Oliveira Lima A. Evaluation of freeze/thaw and scaling response of nanoconcrete for Portland cement concrete (PCC) pavements. Constr. Build. Mater. 2016; 120: 465-472. https://doi.org/10.1016/j.conbuildmat.2016.05.043
[13] Atiş C., Celik O. Relation between abrasion resistance and flexural strength of high-volume fly ash concrete. Mater. Struct. 2002; 35(4): 257-260. https://doi.org/10.1007/BF02533087
[14] Muthukumar M., Mohan D. Optimization of mechanical properties of polymer concrete and mix design recommendation based on design of experiments. J. Appl. Polym. Sci. 2004; 94(3): 1107-1116. https://doi.org/10.1002/app.21008
[15] Park C.H., Lee W.I., Han W.S., Vautrin A. Simultaneous optimization of composite structures considering mechanical performance and manufacturing cost. Compos. Struct. 2004; 65(1): 117-127. https://doi.org/10.1016/j.compstruct.2003.10.010
[16] Sahab M., Ashour A., Toropov V. Cost optimisation of reinforced concrete flat slab buildings. Eng. Struct. 2005; 27(3): 313-322. https://doi.org/10.1016/j.engstruct.2004.10.002
[17] Gao J., Wang C., Chu S.H. Mix design of sustainable concrete using generative models. J. Build. Eng. 2024; 96: 110618. https://doi.org/10.1016/j.jobe.2024.110618
[18] Cao Y., Su F., Antwi-Afari M.F., Lei J., Wu X., Liu Y. Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm. J. Clean. Prod. 2024; 465: 142746. https://doi.org/10.1016/j.jclepro.2024.142746
[19] Wang M., Du M., Zhuang X., et al. Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods. Front. Struct. Civ. Eng. 2025; 19: 143–161. https://doi.org/10.1007/s11709-025-1152-0
[20] Najim K.B. Strength evaluation of concrete structures using ISonReb linear regression models: Laboratory and site (case studies) validation. Constr. Build. Mater. 2017; 149: 639-647. https://doi.org/10.1016/j.conbuildmat.2017.04.162
[21] Fereig S. An application of linear programming to bridge design with standard prestressed girders. Comput. Struct. 1994; 50(4): 455-469. https://doi.org/10.1016/0045-7949(94)90016-7
[22] Tan Y., Shen L., Lu W., Shen Q. Multiple-objective bidding strategy using goal programming technique. Manag. Decis. 2008; 46(4): 656-672. https://doi.org/10.1108/00251740810865111
[23] Afroughsabet V., Ozbakkaloglu T. Mechanical and durability properties of high-strength concrete containing steel and polypropylene fibers. Constr. Build. Mater. 2015; 94: 73-82. https://doi.org/10.1016/j.conbuildmat.2015.06.051
[24] Ziari H., Hayati P., Sobhani J. Airfield self-consolidating concrete pavements (ASCCP): Mechanical and durability properties. Constr. Build. Mater. 2014; 72: 174-181. https://doi.org/10.1016/j.conbuildmat.2014.08.047
[25] Eren Ö., Celik T. Effect of silica fume and steel fibers on some properties of high-strength concrete. Constr. Build. Mater. 1997; 11(7-8): 373-382. https://doi.org/10.1016/S0950-0618(97)00058-5
[26] Ji T., Lin T., Lin X. A concrete mix proportion design algorithm based on artificial neural networks. Cem. Concr. Res. 2006; 36(7): 1399-1408. https://doi.org/10.1016/j.cemconres.2006.01.009
[27] Chatterji S. Freezing of air-entrained cement-based materials and specific actions of air-entraining agents. Cem. Concr. Compos. 2003; 25(7): 759-765. https://doi.org/10.1016/S0958-9465(02)00099-9
[28] Nobili A., Lanzoni L., Tarantino A. Experimental investigation and monitoring of a polypropylene-based fiber reinforced concrete road pavement. Constr. Build. Mater. 2013; 47: 888-895. https://doi.org/10.1016/j.conbuildmat.2013.05.077
[29] Başyiğit C., Çomak B., Kılınçarslan Ş., Üncü İ.S. Assessment of concrete compressive strength by image processing technique. Constr. Build. Mater. 2012; 37: 526-532. https://doi.org/10.1016/j.conbuildmat.2012.07.055
[30] Easa S.M. Earthwork allocations with nonconstant unit costs. J. Constr. Eng. Manag. 1987; 113(1): 34-50. https://doi.org/10.1061/(ASCE)0733-9364(1988)114:4(641)
[31] Ghoddousi P., Shirzadi Javid A., Sobhani J. A Fuzzy System Methodology for Concrete Mixture Design Considering Maximum Packing Density and Minimum Cement Content. Arab. J. Sci. Eng. 2015; 40(8). https://doi.org/10.1007/s13369-015-1731-9
[32] Seyedpoor S.M., Salajegheh J., Salajegheh E., Gholizadeh S. Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks. Eng. Optim. 2009; 41: 473-493. https://doi.org/10.1080/03052150802596076
[33] Yuan Z., Wang L.N., Ji X. Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS. Adv. Eng. Softw. 2014; 67: 156-163. https://doi.org/10.1016/j.advengsoft.2013.09.004
[34] Dong S., Hao P., Tighe S.L. A diagnostic method for evaluating the condition index of cement-stabilised base using TS fuzzy neural network. Int. J. Pavement Eng. 2017; 1-14. https://doi.org/10.1080/10298436.2017.1394097
AUT Journal of Civil Engineering
Volume 10, Issue 3
Summer 2026
Pages 193-210