Modeling of Compressive Strength of Geopolymers by a Hybrid ANFIS-ICA Approach
Publication: Journal of Materials in Civil Engineering
Volume 27, Issue 5
Abstract
A hybrid adaptive neuro-fuzzy interfacial systems–imperialist competitive algorithm (ANFIS-ICA) was presented to determine the effect of concentration of alkali solution, alkali binder to alkali solution weight ratio, alkali activator to ordinary portland cement (OPC) weight ratio, oven curing temperature, and age of curing on the compressive strength of OPC-based geopolymers. Optimization of the type and number of membership functions was carried out by ICA while the training, testin,g and validating of the collected data sets was conducted by ANFIS. The obtained results indicated that the proposed ANFIS-ICA model is capable to predict the compressive strength of geopolymeric specimens well and suitably determine the effect of each parameter on this property. A parametric study is presented to show the effect of each parameter predicted by the model on compressive strength of the specimens.
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References
Ahmadi, M. A., Ebadi, M., Shokrollahi, A., and Majidi, S. M. J. (2013). “Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir.” Appl. Soft Comput., 13(2), 1085–1098.
Ahmadi-Nedushan, B. (2012). “Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models.” Constr. Build. Mater., 36, 665–673.
Amani, J., and Moeini, R. (2012). “Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network.” Scientia Iranica, 19(2), 242–248.
Atashpaz-Gargari, E., and Lucas, C. (2007). “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition.” IEEE Congress on Evolutionary Computation, 2007. CEC 2007, IEEE, New Brunswick, NJ, 4661–4667.
Boğa, A. R., Öztürk, M., and Topçu, İ. B. (2013). “Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI.” Compos. Part B Eng., 45(1), 688–696.
Cüneyt Aydin, A., Tortum, A., and Yavuz, M. (2006). “Prediction of concrete elastic modulus using adaptive neuro-fuzzy inference system.” Civ. Eng. Environ. Syst., 23(4), 295–309.
Dilmaç, H., and Demir, F. (2013). “Stress-strain modeling of high-strength concrete by the adaptive network-based fuzzy inference system (ANFIS) approach.” Neural Comput. Appl., 23(1), 385–390.
Duan, H., and Huang, L. (2014). “Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning.” Neurocomputing, 125, 166–171.
Emiroğlu, M., Beycioğlu, A., and Yildiz, S. (2012). “ANFIS and statistical based approach to prediction the peak pressure load of concrete pipes including glass fiber.” Expert Syst. Appl., 39(3), 2877–2883.
Kaveh, A., and Talatahari, S. (2010). “Optimum design of skeletal structures using imperialist competitive algorithm.” Comput. Struct., 88(21–22), 1220–1229.
MATLAB R2013a [Computer software]. Natick, MA, Mathworks.
Nazari, A., and Ghafouri Safarnejad, M. (2013). “Prediction early age compressive strength of OPC-based geopolymers with different alkali activators and seashell powder by gene expression programming.” Ceram. Int., 39(2) 1433–1442.
Nazari, A., Khanmohammadi, H., Amini, M., Hajiallahyari, H., and Rahimi, A. (2012). “Production geopolymers by portland cement: Designing the main parameters’ effects on compressive strength by Taguchi method.” Mater. Des., 41, 43–49.
Onisei, S., et al. (2012). “Synthesis of inorganic polymers using fly ash and primary lead slag.” J. Hazard. Mater., 205–206, 101–110.
Poulesquen, A., Frizon, F., and Lambertin, D. (2013). “Rheological behavior of alkali-activated metakaolin during geopolymerization.” Cement-based materials for nuclear waste storage, Springer, New York, 225–238.
Razmjooy, N., Mousavi, B. S., and Soleymani, F. (2013). “A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation.” Math. Comput. Modell., 57(3), 848–856.
Rickard, W. D., Williams, R., Temuujin, J., and van Riessen, A. (2011). “Assessing the suitability of three Australian fly ashes as an aluminosilicate source for geopolymers in high temperature applications.” Mater. Sci. Eng. A, 528(9), 3390–3397.
Sadrmomtazi, A., Sobhani, J., and Mirgozar, M. A. (2013). “Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS.” Constr. Build. Mater., 42, 205–216.
Sobhani, J., Najimi, M., Pourkhorshidi, A. R., and Parhizkar, T. (2010). “Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models.” Constr. Build. Mater., 24(5), 709–718.
Somna, K., Jaturapitakkul, C., Kajitvichyanukul, P., and Chindaprasirt, P. (2011). “NaOH-activated ground fly ash geopolymer cured at ambient temperature.” Fuel, 90(6), 2118–2124.
Sonebi, M., and Cevik, A. (2009). “Prediction of fresh and hardened properties of self-consolidating concrete using neurofuzzy approach.” J. Mater. Civ. Eng., 672–679.
Talatahari, S., Kaveh, A., and Sheikholeslami, R. (2012). “Chaotic imperialist competitive algorithm for optimum design of truss structures.” Struct. Multi. Optim., 46(3), 355–367.
Tsai, H. C., and Lin, Y. H. (2011). “Modular neural network programming with genetic optimization.” Expert Syst. Appl., 38(9), 11032–11039.
Williams, R. P., and van Riessen, A. (2011). “Development of alkali activated borosilicate inorganic polymers (AABSIP).” J. Eur. Ceram. Soc., 31(8), 1513–1516.
Yuan, Z., Wang, L. N., and Ji, X. (2014). “Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS.” Adv. Eng. Softw., 67, 156–163.
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© 2014 American Society of Civil Engineers.
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Received: Apr 1, 2014
Accepted: May 9, 2014
Published online: Aug 7, 2014
Discussion open until: Jan 7, 2015
Published in print: May 1, 2015
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