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http://dx.doi.org/10.1007/s40069-013-0038-z

An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica-Rice Husk Ash Ternary Blended Concrete  

Najigivi, Alireza (Institute for Nanoscience & Nanotechnology (INST), Sharif University of Technology)
Khaloo, Alireza (Center of Excellence in Structure & Earthquake Engineering, Sharif University of Technology)
zad, Azam Iraji (Institute for Nanoscience & Nanotechnology (INST), Sharif University of Technology)
Rashid, Suraya Abdul (Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia)
Publication Information
International Journal of Concrete Structures and Materials / v.7, no.3, 2013 , pp. 225-238 More about this Journal
Abstract
In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of $SiO_2$ particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.
Keywords
artificial neural networks; permeability; nano silica; rice husk ash; ternary blended concrete;
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1 Adhikary, B. B., & Mutsuyoshi, H. (2006). Prediction of shear strength of steel fiber RC beams using neural networks. Construction and Building Materials, 20(9), 801-811.   DOI   ScienceOn
2 Akkurt, S., Ozdemir, S., Tayfur, G., & Akyol, B. (2003). The use of GA-ANNs in the modeling of compressive strength of cement mortar. Cement and Concrete Research, 33, 973-979.   DOI   ScienceOn
3 Alves, M. F., Cremonini, R. A., & Dal Molin, D. C. C. (2004). A comparison of mix proportioning methods for highstrength concrete. Cement and Concrete Composites, 26(6), 613-621.   DOI   ScienceOn
4 Bahia, H. U., Benson, C. H., & Kanitpong. K. (2001). Hydraulic conductivity (permeability) of laboratory-compacted asphalt mixtures (pp. 25-32). Transportation Research Record 1767. Washington, D.C.: Transportation Research Board.
5 Caijun, S. (2004). Effect of mixing proportions of concrete on its electrical conductivity and the rapid chloride permeability test (ASTM C1202 or AASHTO 227) results. Cement and Concrete Research, 34, 537-545.   DOI   ScienceOn
6 Chindaprasirt, P., Chotithanorm, C., Cao, H. T., & Sirivivatnanon, V. (2007). Influence of fly ash fineness on chloride penetration of concrete. Construction and Building Materials, 21(2), 356-361.   DOI   ScienceOn
7 Ganesan, K., Rajagopal, K., & Thangavel, K. (2008). Rice husk ash blended cement: Assessment of optimal level of replacement for strength and permeability properties of concrete. Construction and Building Materials, 22(8), 1675-1683.   DOI   ScienceOn
8 Hagan, M., Demuth, H., & Beale, M. (1996). Neural network design. Boston, MA: PWS Publishing.
9 Hornik, K., Stinchcombe, M., White, H., & Auer, P. (1994). Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives. Neural Computation, 6, 1262-1275.   DOI   ScienceOn
10 Ince, R. (2004). Prediction of fracture parameters of concrete by artificial neural networks. Engineering Fracture Mechanics, 71(15), 2143-2159.   DOI   ScienceOn
11 Jamil, M., Zain, M. F. M., & Basri, H. B. (2009). Neural network simulator model for optimization in high performance concrete mix design. European Journal of Scientific Research, 34(1), 61-68.
12 Kasperkiewicz, J., Racz, J., & Dubrawski, A. (1995). HPC strength prediction using artificial neural networks. Journal of Computing in Civil Engineering, 9(4), 279-284.   DOI   ScienceOn
13 Martys, N. S., & Ferraris, C. F. (1997). Capillary transport in mortars and concrete. Cement and Concrete Research, 27(5), 747-760.   DOI   ScienceOn
14 Metha, P. K., & Artcin, P. C. (1990). Principles underlying the production of high-performance concrete. Cement, Concrete and Aggregates, 12(2), 70-78.   DOI
15 Neville, A. M. (1995). Properties of concrete (4th ed.). Essex, U.K.: Longman Group Limited.
16 Monteiro, P. J. M., & Mehta, P. K. (1986). Improvement of the aggregate cement paste transition zone by grain refinement of hydration product. In Proceedings of the VIIIth international congress on the chemistry of cement, Vol. 2, Riode- Jeneiro, Brazil, pp. 433-437.
17 Naji Givi, A., Abdul Rashid, S., Aziz, F. N. A., & Salleh, M. A. M. (2010a). Assessment of the effects of rice husk ash particle size on strength, water permeability and workability of binary blended concrete. Construction and Building Materials, 24(11), 2145-2150.   DOI   ScienceOn
18 Naji Givi, A., Abdul Rashid, S., Aziz, F. N. A., & Salleh, M. A. M. (2010b). Experimental investigation of the size effects of $SiO_2$ nano-particles on the mechanical properties of binary blended concrete. Composites: Part B. doi: 10.1016/j.compositesb.2010.08.003
19 Oztas, A., Pala, M., Ozbay, E., Kanca, E., Cagar, N., & Bhatti, M. A. (2006). Predicting the compressive strength and slump of high strength concrete using neural networks. Construction and Building Materials, 21(2), 384-394.
20 Pala, M., Ozbay, E.,Oztas, A., & Yuce, M. I. (2007a). Appraisal of long term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construction and Building Materials, 20(9), 769-775.
21 Pala, M., Ozbay, E., Oztas, A., & Yuce, M. I. (2007b). Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construction and Building Materials, 21(2), 384-394.   DOI   ScienceOn
22 Parichatprecha, R., & Nimityongskul, P. (2009). Analysis of durability of high performance concrete using artificial neural networks. Construction and Building Materials, 23, 910-917.   DOI   ScienceOn
23 Philleo, R. E. (1986). Freezing and thawing resistance of highstrength concrete. CNHRP Synthesis of Highway Practice, 129. Washington, D.C.: Transportation Research Boards.
24 Powers, T. C. (1968). Properties of fresh concrete. New York, NY: Wiley.
25 Rumellhert, D., Hinto, G., & Williams, R. (1986). Learning internal representations by error propagation. Cambridge, MA: MIT Press.
26 Powers, T. C., Copeland, L. E., & Mann, H. M. (1959). Capillary continuity or discontinuity in cement paste. Journal of the PCA Research and Development Laboratories, 1(2), 38-48.
27 Prabir, B. C. (2001). High performance concrete: mechanism and application. ICI Journal, 2(1), 15-38.
28 Ransinchung, G. D., Kumar, B., & Kumar, V. (2009). Assessment of water absorption and chloride ion penetration of pavement quality concrete admixed with wollastonite and microsilica. Construction and Building Materials, 23(2), 1168-1177.   DOI   ScienceOn
29 Sirivivatnanon, V., & Cao, H. T. (1998). Binder dependency of durability properties of HPC. In Canadian international symposium of HPC and reactive powder concrete, Canada, pp. 227-240.
30 Sobhani, J., et al. (2010). Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Construction and Building Materials, 24(5), 709-718.   DOI   ScienceOn
31 Tasdemir, C. (2003). Combined effects of mineral admixtures and curing conditions on the sorptivity coefficients of concrete. Cement and Concrete Research, 33, 1637-1642.   DOI   ScienceOn
32 Topcu, I. B., & Saridemir, M. (2007). Prediction of properties of waste AAC aggregate concrete using artificial neural network. Computational Materials Science, 41(1), 117-125.   DOI   ScienceOn
33 Topcu, I. B., & Saridemir, M. (2008a). Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Construction and Building Materials, 22(4), 532-540.   DOI   ScienceOn
34 Topcu, I. B., & Saridemir, M. (2008b). Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Computational Materials Science, 41(3), 305-311.   DOI   ScienceOn
35 Zain, M. F. M., Islam, M. N., & Basri, I. H. (2005). An expert system for mix design of high performance concrete. Advances in Engineering Software, 36, 325-377.   DOI   ScienceOn
36 Wee, T. H., Suryavanshi, J. A., & Tin, S. S. (2000). Evaluation of rapid chloride permeability test (RCPT) results for concrete containing mineral admixtures. ACI Materials Journal, 97(2), 221-232.
37 Yeh, I. C. (1999). Design of high-performance concrete mixture using neural networks and nonlinear programming. Journal of Computing in Civil Engineering, 13(1), 36-42.   DOI   ScienceOn
38 Yeh, Y. C., Kuo, Y. H., & Hsu, D. H. (1993). Building KBES for diagnostic PC Pile with ANN. Journal of Computing in Civil Engineering, 7, 71-93.   DOI
39 Zhao, T. J., Zhou, Z. H., Zhu, J. Q., & Feng, N. Q. (1998). An alternating test method for concrete permeability. Cement and Concrete Research, 28, 7-12.   DOI   ScienceOn