Using Neural Networks for Prediction of Properties of Polymer Concrete with Fly Ash
Publication: Journal of Materials in Civil Engineering
Volume 24, Issue 5
Abstract
This paper presents the results of studies conducted with neural networks on determining the properties of polymer concrete with fly ash. Polymer concrete with different contents of fly ash and resin was prepared and tested for determining the influence of fly ash on the properties. Using neural networks, the experimental results were analyzed for predicting the compressive strength and flexural strength, and also on the basis of a model with given values of properties, to ascertain the composition (content of resin, aggregate, and fly ash). Eleven sets were considered for training and four for verification. Reverse modeling proves that the largest values for compressive strength and flexural strength are obtained for a resin content of approximately 15–16%, and a fly ash content of approximately 8–9%.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ashhab, M. S. (2007). “Application of neural net modeling and inverse control to the desulphurization of hot metal process.” Jordan J. Mech. Ind. Eng., 1(2), 79–84.
Babu, B. V., Ramakrishna, V. (2003). “Modeling of adsorption isotherm constants using regression analysis and neural networks.” Proc., 2nd Int. Conf. Water Quality Management, Central Board of Irrigation and Power, New Delhi, India, 1–9.
Balestrassi, P. P., Popova, E., Paiva, A. P., and Marangon Lima, J. W. (2009). “Design of experiments on neural network’s training for nonlinear time series forecasting.” NeurocomputingNRCGEO, 72(4-6), 1160–1178.
Barbuta, M., and Harja, M. (2009). “Polymer concrete.” Materials used for obtaining polymer concrete, Politehnium, Iasi, Romania, 39–51.
Barbuta, M., and Lepădatu, D. (2008). “Mechanical characteristics investigation of polymer concrete using mixture design of experiments and response surface method.” J. Appl. Sci.JASODW, 8(12), 2242–2249.
Barbuta, M., Taranu, N., and Harja, M. (2009). “Wastes used in obtaining polymer composite.” Environ. Eng. Manage. J., 8(5), 1145–1150EEMJAP.
Barbuta, M., Harja, M., and Babor, D. (2010a). “Concrete polymer with fly ash. Morphologic analysis based on scanning electron microscopic observations.” Rev. Rom. Mater., 40(1), 337–345.
Barbuta, M., Harja, M., and Baran, I. (2010b). “Comparison of mechanical properties for polymer concrete with different types of filler.” J. Mater. Civ. Eng.JMCEE7, 22(7), 696–701.
Ciobanu, G., Carja, G., and Ciobanu, O. (2008). “Use of SAPO-5 zeolite-filled polyurethane membranes in wastewater treatment.” DesalinationDSLNAH, 222(1–3), 197–201.
Cheng, Y. I. (1998). “Modeling concrete strength with augment-neuron networks.” J. Mater. Civ. Eng.JMCEE7, 10(4), 263–268.
Das, S. K., and Sabat, A. K. (2008). “Using neural networks for prediction of some properties of fly ash.” Electron. J. Geotech. Eng., 13, 1–13EJGEFK.
Das, S. K., and Basudhar, P. K. (2006). “Undrained lateral load capacity of piles in clay using artificial neural network.” Comput. Geotech.CGEOEU, 33(8), 454–459.
Das, S. K., and Basudhar, P. K. (2009). “Utilization of self-organizing map and fuzzy clustering for site characterization using piezocone data.” Comput. Geotech.CGEOEU, 36(1–2), 241–248.
Diaconescu, R., Grigoriu, A. M., and Luca, C. (2007). “Neural network modeling of the grafting process of monochlorotriazinyl-β-cyclodextrin on cellulosic supports.” Cell. Chem. Technol.CECTAH, 41(7-8), 385–390.
Diaconescu, R., and Dumitriu, E. (2005). “Applications of artificial neural networks in environmental catalysis.” Environ. Eng. Manage. J., 4(4), 473–498EEMJAP.
Diaconescu, R., Secula, M. S., and Petrescu, S. (2009). “Study of gas drying by adsorption on composite materials using neural networks.” Rev. Chim. (Bucharest)RCBUAU, 60(10), 1065–1069.
European Standard. (2005a). “Testing hardened concrete-Part 3: Flexural strength of test specimen.” SR EN 12390-3/2005, ASRO, Bucuresti, Romania.
European Standard. (2005b). “Testing hardened concrete-Part 5: Compressive strength of test specimen.” SR EN 12390-5/2005, ASRO, Bucuresti, Romania.
Gosden, R. G., et al. (2001). “Living polymerization reactors: Molecular weight distribution control using inverse neural network models.” Polym. React. Eng.PREEEG, 9(4), 249–270.
Griffiths, K. N. (2010). “The application of artificial neural networks for filtration optimization in drinking water treatment.” M.Sc. thesis, Univ. of Toronto, Toronto.
Harja, M., Barbuta, M., and Gavrilescu, M. (2009a). “Study of morphology for geopolymer materials obtained from fly ash.” Environ. Eng. Manage. J., 8(5), 1021–1027EEMJAP.
Harja, M., Barbuta, M., and Gavrilescu, M. (2009b). “Utilization of coal fly ash from power plants II. Geopolymer obtaining.” Environ. Eng. Manage. J., 8(3), 513–520EEMJAP.
Harja, M., Barbuta, M., and Rusu, L. (2009c). “Obtaining and characterization of polymer concrete with fly ash.” J. Appl. Sci.JASODW, 9(1), 88–96.
Harja, M., Barbuta, M., Rusu, L., Munteanu, C., Buema, G., and Doniga, E. (2011). “Simultaneous removal of astrazone blue and lead onto low cost adsorbents based on power plant ash.” Environ. Eng. Manage. J., 10(3), 341–347EEMJAP.
Maruyama, T., Noguchi, M., and Kanematsu, A. (2008). “Optimization of the concrete mix proportions centered on fresh properties through the G. algorithm.” 〈http://www.degas.nuac.nagoya-u.ac.jp/ippei/paper_e/2002_MixGAi.pdf〉 (Mar. 19, 2011).
Özbay, E., Öztaş, A., Baykasoğlu, A., and Özbebek, H. (2009). “Investigating mix proportions of high strength self compacting concrete by using Taguchi method.” Constr. Build. Mater.CBUMEZ, 23(2),694–702.
Ozgan, E., and Ozturk, A. (2007). “Optimization of the hardened concrete properties with GA and LP.” J. Appl. Sci.JASODW, 7(24), 3918–3926.
Pannirselvam, N., Raghunath, P. N., and Suguna, K. (2008). “Neural network for performance of glass fibre reinforced polymer plated RC beams.” Am. J. Eng. Appl. Sci., 1(1), 82–88.
Skapura, D. M. (1996). Building neural networks Addison-Wesley, New York.
Tange Jepsen, M. (2002). “Predicting concrete durability by using artificial neural network.” Proc., Durability of Exposed Concrete Containing Secondary Cementitious Materials, Nordic Concrete Federation, Oslo, Norway, 277–288.
Topcu, I. B., and Sarıdemir, M. (2008). “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic.” Comput. Mater. Sci.CMMSEM, 41(3), 305–311.
Veneziano, D. (2005). Probability and statistics in engineering, MIT OpenCourseWare, Massachusetts Institute of Technology, Cambridge, MA.
Wang, Z., Tham, M. T., and Morris, A. J. (1992). “Multilayer feedforward neural networks: A canonical form approximation of nonlinearity.” Int. J. ControlIJCOAZ, 56(3), 655–672.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 24 • Issue 5 • May 2012
Pages: 523 - 528
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Nov 4, 2010
Accepted: Oct 27, 2011
Published online: Nov 3, 2011
Published in print: May 1, 2012
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.