Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs

Document Type : Research Papers


1 Department of Civil Engineering, Tabari University of Babol, Babol, Iran

2 Faculty of Civil Engineering, Babol University of Technology, Babol – Iran


In the present study, Radial Basis Function (RBF) neural networks are applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC are gathered from the literature. The data used in the networks are classified into two different sets of input parameters. The results revealed that the proposed RBF models can accurately forecast the properties of SCCs with low test error. Furthermore, a comparison between models with two different sets of inputs proves that the selected parameters as input variables, straightly impress the precision of the networks, in the prediction of the intended outputs.


Adekunle, S., Ahmad, S., Maslehuddin, M. and Al-Gahtani, H.J. (2015). “Properties of SCC prepared using natural pozzolana and industrial wastes as mineral fillers”, Cement and Concrete Composites, 62, 125-133.
Al-Khatib, M.I. and Al-Martini, S. (2019). “Predicting the rheology of self-consolidating concrete under hot weather”, Proceedings of the Institution of Civil Engineers - Construction Materials, 172(5), 235-245.
Alyousef, R. (2018). “Study and Experimental investigation on performance self-compacting concrete using different type of fibers”, Romanian Journal of Materials, 48(3), 355-361.
Ashtiani, R.S., Little, D.N. and Rashidi, M. (2018). “Neural Network based model for estimation of the level of anisotropy of unbound aggregate systems”, Transportation Geotechnics, 15, 4-12.
Badrnezhad, R. and Mirza, B. (2014). “Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach”, Journal of Industrial and Engineering Chemistry, 20(2), 528-543.
Barkhordari Bafghi, K. and Entezari Zarch, H. (2015). “Prediction of permanent earthquake-induced deformation in earth dams and embankments using Artificial Neural Networks”, Civil Engineering Infrastructures Journal, 48(2), 271-283.
Beigi, M., Berenjian, J., Lotfi Omran, O., Sadeghi Nik, A. and Nikbin, I. (2013). “An experimental survey on combined effects of fibers and nanosilica on the mechanical, rheological, and durability properties of self-compacting concrete”, Materials and Design, 50, 1019-1029.
Bhargava, C. (2019). AI techniques for reliability prediction for electronic components, IGI Global, Pennsylvania, United States.
Bielecki, A. and Wojcik, M. (2017). Hybrid system of ART and RBF neural networks for online clustering, Applied Soft Computing, 58, 1-10.
Broomhead, D.S. and Lowe, D. (1988). “Multi-variable functional interpolation and adaptive networks”, Complex Systems, 2(3), 321-355.
Celik, K. (2015). “Development and characterization of sustainable self-consolidating concrete containing high volume of limestone powder and natural or calcined pozzolanic materials”, Ph.D. Thesis, University of California.
Das, S., Pal, P. and Singh, R.M. (2015). “Prediction of concrete mix proportion using ANN technique”, International Research Journal of Engineering and Technology, 02(05), 820-825.
Dehwah, H.A.F. (2012). “Mechanical properties of self-compacting concrete incorporating quarry dust powder, silica fume or fly ash”, Construction and Building Materials, 26(1), 547-551.
Demirhan, E., Kandemirli, F., Kandemirli, M. and Kovalishyn, K. (2007). “Investigation of the physical and rheological properties of SBR-1712 rubber compounds by neural network approaches”, Materials and Design, 28(5), 1737–1741.
Ghafari, E., Bandarabadi, M., Costa, H. and Julio, E. (2015). “Prediction of fresh and hardened state properties of UHPC: Comparative study of statistical mixture design and an artificial neural network model”, Journal of Materials in Civil Engineering, 27(11), 1-11.
Goh, A. (1995). “Neural networks for evaluating CPT calibration chamber test data”, Computer-Aided Civil and Infrastructure Engineering, 10(2), 147-151.
Gupta, S. (2013). “Concrete mix design using Artificial Neural Network”, Journal on Today’s Ideas - Tomorrow’s Technologies, 1(1), 29-43.
Kamal, M.M., Safan, M.A., Etman, Z.A. and Abd-elbaki, M.A. (2015). “Effect of steel fibers on the properties of recycled self-compacting concrete in fresh and hardened state”, International Journal of Civil Engineering, 13(4A), 400-410.
Kazemi Elaki, N., Shabakhty, N., Abbasi Kia, M. and Sanayee Moghaddam, S. (2016). “Structural reliability: An assessment using a new and efficient two-phase method based on Artificial Neural Network and a Harmony Search Algorithm”, Civil Engineering Infrastructures Journal, 49(1), 1–20.
khademi, F.S. and Jamal, S.M.M. (2016). “Predicting the 28 days compressive strength of concrete using Artificial Neural Network”, i-Manager's Journal on Civil Engineering, 6(2), 1-7.
Kok, B.V., Yilmaz, M., Sengoz, B., Sengur, A. and Avci, E. (2010). “Investigation of complex modulus of base and SBS modified bitumen with artificial neural networks”, Expert Systems with Applications, 37(12), 7775-7780.
Kopal, I., Harni Carova, M., Valicek, J., Krmela, J. and Luka, O. (2019). “Radial basis function Neural Network-based modeling of the dynamic thermo-mechanical response and damping behavior of thermoplastic elastomer systems”, Polymers, 11(6), 1-20.
Krishna, A. and Anil, S. (2018). “Basalt fiber reinforced self-compacting concrete”, International Research Journal of Engineering and Technology, 5(4), 3751-3754.
Li, F.X., Yu, Q.J., Wei, J.X. and Li, J.X. (2011). “Predicting the workability of self-compacting concrete using Artificial Neural Network”, Advanced Materials Research, 168-170, 1730-1734.
Lihui, M., Kunlun, X. and Suiqing, L. (2008). “Using radial basis function Neural Networks to calibrate water quality model”, International Journal of Environmental and Ecological Engineering, 2(2), 9-17.
Maarof, A., Abba, S.I. and Nuruddeen, M.M. (2017). “Self-compacting concrete, A review”, International Journal of Innovative Technology and Exploring Engineering, 6(8), 1-7.
MATLAB Software, Neural Network toolbox: Mapminmax, R2013b, Version: (, August 13 (2013).
Mechaymech, A. and Assaad, J.J. (2019). “Stability of self-consolidating concrete containing different viscosity modifiers”, Civil Engineering Infrastructures Journal, 52(2), 245-263.
Miller, B. (2011). “Application of Neural Networks for structure updating”, Computer Assisted Mechanics and Engineering Sciences, 18, 191-203.
Mlv, P. and Prasenjit, S. (2019). “Adaptive Neuro-Fuzzy inference system for predicting compressive strength of fibers self-compacting concrete”, Applied Mechanics and Materials, 892. 46-54.
Nazari, A. and Riahi, S.H. (2012). “Prediction of physical and mechanical properties of high strength concrete containing CuO nanoparticles by Artificial Neural”, International Journal of Damage Mechanics,21(2), 207-236.
Omrane, M., Kenai, S., Kadri, E. and Ait-Mokhtar, A. (2017). “Performance and durability of self-compacting concrete using recycled concrete aggregates and natural pozzolan”, Journal of Cleaner Production, 165, 415-430.
Onikeku, O., Shitote, S.M., Mwero, J., Adedeji, A.A. and Kanali, C. (2019). “Compressive strength and slump prediction of two blended agro waste materials concretes”, The Open Civil Engineering Journal, 13, 118-128.
Orak Boru, E., Aktas, M. and Boru, B. (2014). “Radial basis function network-based approach for determining interaction behavior of reinforced concrete rectangular columns”, Arabian Journal for Science and Engineering, 39, 7751-7761.
Ouchi, M., Nakamura, S., Osterson, T., Hellberg, S. and Lwin, M. (2003). “Applications of self-compacting concrete in Japan, Europe and the United States”, Proceedings of the5th International Symposium, Tokyo-Odaiba, Japan.
Ozodabas, A. (2018). “Investigation of the effect of basalt fiber on self-compacting concrete”, International Journal of Research, GRANTHAALAYAH, 6(12), 38-45.            
Prasad Meesaraganda, L.V., Saha, P. and Tarafder, N. (2019). “Artificial Neural Network for strength prediction of fibers’ self-compacting concrete”, In J. Bansal, K. Das, A. Nagar, K. Deep, and A. Ojha (Eds.), Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing, pp. 15-24, Singapore: Springer.
Rajaram, M., Ravichandran, A. and Muthadh, A. (2018). “Prediction on mechanical properties of hybrid fiber concrete using MatLab”, International Research Journal of Engineering and Technology, 05(07), 2426-2432.
Schober, F., Boer, C.H. and Schwarte, L.A. (2018). “Correlation coefficients: Appropriate use and interpretation”, Anesthesia and Analgesia, 126(5), 1763-1768.
Schwenker, F., Kestler, H.A. and Palm, G. (2001). “Three learning phases for radial-basis-function networks”, Neural Networks, 14(4-5), 439-458.
Shah, S.V., Pawar, D.A., Patil, A.S., Bhosale, P.S., Subhedar, A.S. and Bhosale, G.D. (2018). “Concrete mix design using Artificial Neural Network”, International Journal of advance Research in Science and Engineering, 07(03), 251-259.
Shmelova, T., Sikirda, Y., Rizun, N., Kucherov, D. and Dergachov, K. (2019). Automated Systems in the Aviation and Aerospace Industries, IGI Global, Pennsylvania, United States.
 Siddique, R. (2011). “Properties of self-compacting concrete containing class F fly ash”, Materials and Design, 32(3), 1501-1507.
Topçu, I.B. and Sarıdemir, M. (2007). “Prediction of properties of waste AAC aggregate concrete using Artificial Neural Network”, Computational Materials Science, 41(1), 117-125.
Tsai, C.H. and Chuang, H.T. (2004). “Dead-zone compensation based on constrained RBF Neural Network”, Journal of the Franklin Institute, 341(4), 361-374.
Xie, T., Yu, H. and Wilamowski, B. (2011). “Comparison between traditional neural networks and radial basis function networks”, Proceedings of the IEEE International Symposium on Industrial Electronics, Gdansk, Poland.
Yuan, Z., Wang, L.N. and Ji, X. (2014). “Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS”, Advances in Engineering Software, 67,156-163.
Zhang, S.L., Zhang, Z.X. and NPal, K. (2010). “Prediction of mechanical properties of waste polypropylene/waste ground rubber tire powder blends using artificial neural networks”, Materials and Design, 31(8), 3624-3629.