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Prediction of Rubberised Concrete Strength by Using Artificial Neural Networks

Authors: A. M. N. El-Khoja, A. F. Ashour, J. Abdalhmid, X. Dai, A. Khan

Abstract:

In recent years, waste tyre problem is considered as one of the most crucial environmental pollution problems facing the world. Thus, reusing waste rubber crumb from recycled tyres to develop highly damping concrete is technically feasible and a viable alternative to landfill or incineration. The utilization of waste rubber in concrete generally enhances the ductility, toughness, thermal insulation, and impact resistance. However, the mechanical properties decrease with the amount of rubber used in concrete. The aim of this paper is to develop artificial neural network (ANN) models to predict the compressive strength of rubberised concrete (RuC). A trained and tested ANN was developed using a comprehensive database collected from different sources in the literature. The ANN model developed used 5 input parameters that include: coarse aggregate (CA), fine aggregate (FA), w/c ratio, fine rubber (Fr), and coarse rubber (Cr), whereas the ANN outputs were the corresponding compressive strengths. A parametric study was also conducted to study the trend of various RuC constituents on the compressive strength of RuC.

Keywords: Rubberized concrete, compressive strength, artificial neural network, prediction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2021549

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References:


[1] Taverne, J.-P., End of life tyres-A valuable resource with growing potential. ETRma End-of-life Tyres Management Report of, 2011.
[2] Sofi, A., Effect of waste tyre rubber on mechanical and durability properties of concrete–A review. Ain Shams Engineering Journal, 2017.
[3] Rosenblatt, F., Principles of neurodynamics. 1962.
[4] Rumelhart, D., David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Nature, 1986. 323: p. 533-536.
[5] Fischer, A. and C. Igel, Training restricted Boltzmann machines: An introduction. Pattern Recognition, 2014. 47(1): p. 25-39.
[6] Lippmann, R., An introduction to computing with neural nets. IEEE Assp magazine, 1987. 4(2): p. 4-22.
[7] Garzón-Roca, J., C.O. Marco, and J.M. Adam, Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic. Engineering Structures, 2013. 48(Supplement C): p. 21-27.
[8] Nehdi, M., H. El Chabib, and M.H. El Naggar, Predicting performance of self-compacting concrete mixtures using artificial neural networks. Materials Journal, 2001. 98(5): p. 394-401.
[9] Londhe, S.N. Towards predicting water levels using artificial neural networks. in OCEANS 2009-EUROPE. 2009. IEEE.
[10] Sukontasukkul, P. and K. Tiamlom, Expansion under water and drying shrinkage of rubberized concrete mixed with crumb rubber with different size. Construction and Building Materials, 2012. 29: p. 520-526.
[11] Al-Tayeb, M.M., et al., Effect of partial replacement of sand by recycled fine crumb rubber on the performance of hybrid rubberized-normal concrete under impact load: experiment and simulation. Journal of Cleaner Production, 2013. 59: p. 284-289.
[12] Marie, I., Zones of weakness of rubberized concrete behavior using the UPV. Journal of Cleaner Production, 2016.
[13] Thomas, B.S. and R.C. Gupta, Properties of high strength concrete containing scrap tire rubber. Journal of Cleaner Production, 2015.
[14] Onuaguluchi, O. and D.K. Panesar, Hardened properties of concrete mixtures containing pre-coated crumb rubber and silica fume. Journal of Cleaner Production, 2014. 82: p. 125-131.
[15] Liu, H., et al., Experimental Investigation of the Mechanical and Durability Properties of Crumb Rubber Concrete. Materials, 2016. 9(3): p. 172.
[16] Dong, Q., B. Huang, and X. Shu, Rubber modified concrete improved by chemically active coating and silane coupling agent. Construction and Building Materials, 2013. 48: p. 116-123.
[17] Toutanji, H.A., The use of rubber tire particles in concrete to replace mineral aggregates. Cement and Concrete Composites, 1996. 18(2): p. 135-139.
[18] Meddah, A., M. Beddar, and A. Bali, Use of shredded rubber tire aggregates for roller compacted concrete pavement. Journal of Cleaner Production, 2014. 72: p. 187-192.
[19] Sukontasukkul, P., Use of crumb rubber to improve thermal and sound properties of pre-cast concrete panel. Construction and Building Materials, 2009. 23(2): p. 1084-1092.
[20] Atahan, A.O. and U.K. Sevim, Testing and comparison of concrete barriers containing shredded waste tire chips. Materials Letters, 2008. 62(21): p. 3754-3757.
[21] Thomas, B.S., et al., Strength, abrasion and permeation characteristics of cement concrete containing discarded rubber fine aggregates. Construction and Building Materials, 2014. 59: p. 204-212.
[22] Kumar, G.N., V. Sandeep, and C. Sudharani, Using tyres wastes as aggregates in concrete to form rubcrete–mix for engineering applications. International Journal of Research in Engineering and Technology, 2014. 3(11): p. 500-9.
[23] Ganjian, E., M. Khorami, and A.A. Maghsoudi, Scrap-tyre-rubber replacement for aggregate and filler in concrete. Construction and Building Materials, 2009. 23(5): p. 1828-1836.
[24] Reda Taha, M.M., et al., Mechanical, fracture, and microstructural investigations of rubber concrete. Journal of materials in civil engineering, 2008. 20(10): p. 640-649.
[25] Zheng, L., X.S. Huo, and Y. Yuan, Strength, modulus of elasticity, and brittleness index of rubberized concrete. Journal of Materials in Civil Engineering, 2008. 20(11): p. 692-699.
[26] Bashir, R. and A. Ashour, Neural network modelling for shear strength of concrete members reinforced with FRP bars. Composites Part B: Engineering, 2012. 43(8): p. 3198-3207.
[27] Chan, B., M. Bibby, and N. Holtz, Predicting HAZ hardness with artificial neural networks. Canadian metallurgical quarterly, 1995. 34(4): p. 353-356.
[28] Jadid, M.N. and D.R. Fairbairn, Neural-network applications in predicting moment-curvature parameters from experimental data. Engineering Applications of Artificial Intelligence, 1996. 9(3): p. 309-319.
[29] Ni, H.-G. and J.-Z. Wang, Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research, 2000. 30(8): p. 1245-1250.
[30] Siddique, R., P. Aggarwal, and Y. Aggarwal, Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering Software, 2011. 42(10): p. 780-786.
[31] Duan, Z.-H., S.-C. Kou, and C.-S. Poon, Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 2013. 40: p. 1200-1206.
[32] Boger, Z. and H. Guterman. Knowledge extraction from artificial neural network models. in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation, 1997 IEEE International Conference on. 1997. IEEE.
[33] Karsoliya, S., Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 2012. 3(6): p. 714-717.