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http://dx.doi.org/10.12989/cac.2018.21.1.103

Hybrid fuzzy model to predict strength and optimum compositions of natural Alumina-Silica-based geopolymers  

Nadiri, Ata Allah (Department of Earth Sciences, Faculty of Natural Science, University of Tabriz)
Asadi, Somayeh (Department of Architectural Engineering, Pennsylvania State University)
Babaizadeh, Hamed (LADOTD)
Naderi, Keivan (Department of Earth Sciences, Faculty of Natural Science, University of Tabriz)
Publication Information
Computers and Concrete / v.21, no.1, 2018 , pp. 103-110 More about this Journal
Abstract
This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of $Al_2O_3/SiO_2$, $Na_2O/Al_2O_3$, $Na_2O/H_2O$ and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.
Keywords
compressive strength; geopolymer; fuzzy model; Artificial Neural Network (ANN); Supervised Committee Fuzzy Logic (SCFL);
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1 Ahmadi-Nedushan, B. (2012), "An optimized instance based learning algorithm for estimation of compressive strength of concrete", J. Eng. Appl. Artif. Intel., 25(5), 1073-1081.   DOI
2 Asadi, S., Hassan, M., Nadiri, A.A. and Dylla, H. (2014), "Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification", Environ. Sci. Poll. Res., 21, 8847-8857.   DOI
3 ASCE (2000a), "Task committee on application of artificial neural networks in hydrology, artificial neural network in hydrology. I: Preliminary concepts", J. Hydrolog. Eng., 5(2), 115-123.   DOI
4 Bohlooli, H., Nazari, A., Khalaj, G., Kaykha, M.M. and Riahi, S. (2012), "Experimental investigations and fuzzy logic modeling of compressive strength of geopolymers with seeded fly ash and rice husk bark ash", Compos. Part B: Eng., 43(3), 1293-1301.   DOI
5 Panagiotopoulou, C., Kontori, E., Perraki, T. and Kakali, G. (2007), "Dissolution of aluminosilicate minerals and byproducts in alkaline media", J. Mater. Sci., 42(9), 2967-2973.   DOI
6 Pulido Calvo, I. and Gutierrez Estrada, J.C. (2009), "Improved irrigation water demand forecasting using a soft-computing hybrid model", Biosyst. Eng., 102, 202-218.   DOI
7 Roviello, G., Ricciotti, L., Ferone, C., Colangelo, F. and Tarallo, O., (2015), "Fire resistant melamine based organic-geopolymer hybrid composites", Cement Concrete Compos., 59, 89-99.   DOI
8 Subear, S. and Van Riessen, A. (2007), "Thermechanical and micro-structure of unconfined compressive of sodium-poly (sialate-siloxo) (Na-PSS) geopolymers", J. Mater. Sci., 42(9), 3117-3123.   DOI
9 Sugeno, M. (1985), Industrial Application of Fuzzy Control, North-Holland, New York.
10 Tayfur, G. and Nadiri, A.A. (2014), "Supervised intelligent committee machine for hydraulic conductivity estimation", Water Resour. Manage., 28, 1173-1184.   DOI
11 Van Jaarsveld, J.G.S., Van Deventer, J.S.J. and Lorenzen, L. (1997), "The potential use of geopolymeric materials to immobilise toxic metals: Part I. Theory and applications", Miner. Eng., 10(7), 659-669.   DOI
12 Wang, H., Li, H. and Yan, F. (2005), "Synthesis and mechanical properties of metakaolinite-based geopolymer", Coll. Surf. A: Physicochem. Eng. Aspect., 268(1-3), 1-6.   DOI
13 Xu, H., van Deventer, J.S.J. and Lukey, G.C. (2001), "Effect of alkali metals on the preferential geopolymerization of Stilbite/Kaolinite mixtures", Indust. Eng. Chem. Res., 40(17), 3749-3756.   DOI
14 Chen, M.S. and Wang, S.W. (1999), "Fuzzy clustering analysis for optimizing fuzzy membership functions", Fuzzy Set. Syst., 103(2), 239-254.   DOI
15 Bondar, D. (2014), "Use of a neural network to predict strength and optimum composition of natural alumina-silica-based geopolymers", J. Mater. Civil Eng., 26, 499-504.   DOI
16 Bondar, D., Lynsdale, C. and Milestone, N. (2012), "Simplified Model for Prediction of Compressive Strength of Alkali-Activated Natural Pozzolans", J. Mater. Civil Eng., 24(4), 391-400.   DOI
17 Chen, C.H. and Lin, Z.S. (2006), "A committee machine with empirical formulas for permeability prediction", Comput. Geosci., 32(4), 485-496.   DOI
18 Cheng, M.Y. and Cao, M.T. (2014), "Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams", J. Eng. Appl. Artif. Intel., 28, 86-96.   DOI
19 Chitsazan, N., Nadiri, A.A. and Tsai, F.F.C. (2015), "Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging", J. Hydrol., 528, 52-62.   DOI
20 Chiu, S.L. (1994), "Fuzzy model identification based on cluster estimation", J. Intel. Fuzzy Syst., 2, 267-278.   DOI
21 de Castilho, V.C., El Debs, M.K. and Nicoletti, M. (2007), "Using a modified genetic algorithm to minimize the production costs for slabs of precast prestressed concrete joists", J. Appl. Artif. Itel., 20(4), 519-530.   DOI
22 Gutierrez-Estrada, J.C., De Pedro-Sanz, E., Lopez-Luque, R. and Pulido-Calvo, I. (2004), "Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla.) Intensive rearing system", Aquacult. Eng., 31, 183-203.   DOI
23 Duxson, P. Provis, J.L., Lukey, G.C., Mallicoat, S.W., Kriven, W.M. and van Deventer, J.S.J. (2005), "Understanding the relationship between geopolymer composition, microstructure and mechanical properties", Coll. Surf. A: Physicochem. Eng. Aspect., 269(1-3), 47-58.   DOI
24 Duxson, P., Fernandez-Jimenez, A., Provis, J.L., Lukey, G.C., Palomo, A. and van Deventer, J.S.J. (2007), "Geopolymer technology: the current state of the art", J. Mater. Sci., 42(9), 2917-2933.   DOI
25 Duxson, P., Provis, J.L., Lukey, G.C. and van Deventer, J.S.J. (2007), "The role of inorganic polymer technology in the development of "green concrete"", Cement Concrete Res., 37(12), 1590-1597.   DOI
26 Ferone, C., Roviello, G., Colangelo, F., Cioffi, R. and Tarallo, O. (2013), "Novel hybrid organic-geopolymer materials", Appl. Clay Sci., 73, 42-50.   DOI
27 Grande, J.A., Andujar, J.M., Aroba, J., Beltran, R., De La Torre, M.L., Ceron, J.C. and Gomez, T. (2010), "Fuzzy modeling of the spatial evolution of the chemistry in the Tinto River (SW Spain)", Water Resour. Manage., 24(12), 3219-3235.   DOI
28 Kadkhodaie-Ilkhchi, A. and Amini, A. (2009), "A fuzzy logic approach to estimating hydraulic flow units from well log data: A case study from the Ahwaz oilfield, South Iran", J. Petrol. Geol., 32(1), 67-78.   DOI
29 Gutierrez Estrada, J.C., Pulido Calvo, I. and Bilton, D.T. (2013), "Consistency of fuzzy rules in an ecological context", Ecolog. Model., 251, 187-198.   DOI
30 Haykin, S.S. (1998), Neural Networks: A Comprehensive Foundation, Prentice Hall.
31 Khater, H.M. (2016), "Nano-Silica effect on the physicomechanical properties of geopolymer composites", Adv. Nano Res., 4(3), 181-195.   DOI
32 Mamdani, E.H. (1976), "Advances in the linguistic synthesis of fuzzy controllers", Int. J. Man-Mach. Stud., 8(6), 669-678.   DOI
33 Labani, M.M., Kadkhodaie-Ilkhchi, A. and Salahshoor, K. (2010), "Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin", J. Petrol. Sci. Eng., 72(1-2), 175-185.   DOI
34 Larsen, P.M. (1980), "Industrial application of fuzzy logic control", Int. J. Man-Mach. Stud., 12, 3-10.   DOI
35 Li, H., Chen, C.L.P. and Huang, H.P. (2000), Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Application in Engineering, CRC Press LLC.
36 Mamdani, E.H. (1977), "Application of fuzzy logic to approximate reasoning using linguistic synthesis", IEEE Tran. Comput., 26(12), 1182-1191.
37 Mamdani, E.H. and Assilian, S. (1975), "An experiment in linguistic synthesis with a fuzzy logic controller", Int. J. Man-Mach. Stud., 7(1), 1-13.   DOI
38 Nadiri, A.A., Chitsazan, N., Tsai, F.T.C. and Asghari Moghaddam, A.A. (2014), "Bayesian artificial intelligence model averaging for hydraulic conductivity estimation", J. Hydrolog. Eng., 19(3), 520-532.   DOI
39 Motamedi, S., Shamshirband, S., Petkovic, D. and Hashim, R. (2015), "Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sandcement mixture", Powder Technol., 278, 278-285.   DOI
40 Nadiri, A.A. (2015), Application of Artificial Intelligence Methods in Geosciences and Hydrology, OMICS Publisher.
41 Nadiri, A.A., Fijani, E., Tsai, F.T.C. and Asghari Moghaddam, A.A. (2013), "Supervised committee machine with artificial intelligence for prediction of fluoride concentration", Hydroinform. J., 15(4), 1474-1490.   DOI
42 Nadiri, A.A., Sedghi, Z., Khatibi, R. and Gharekhani, M. (2017c), "Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures", Sci. Total Environ., 593-594, 75-90.   DOI
43 Nadiri, A.A., Gharekhani, M., Khatibi, R. and Asghari Moghaddam, A. (2017b), "Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models", Environ. Sci. Poll. Res., 24(9), 8562-8577.   DOI
44 Nadiri, A.A., Gharekhani, M., Khatibi, R., Sadeghfam, S. and Asghari Moghaddam, A. (2017a), "Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)", Sci. Total Environ., 574, 691-706.   DOI
45 Nadiri, A.A., Hassan, M.M. and Asadi, S. (2015), "Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification", Tran. Res. Record: J. Tran. Res. Board, 2528, 96-105.   DOI
46 Nazari, A., Pacheco-Torgal, F., Cevik, A. and. Sanjayan, J.G. (2015), "Prediction of the compressive strength of alkaliactivated geopolymeric concrete binders by neuro-fuzzy modeling: a case studys", Handbook of Alkali-Activated Cements, Mortars and Concretes.
47 Nourani, V., Asghari Mogaddam, A., Nadiri, A.A. and Sing, V.P. (2008), "Forecasting spatiotemporal water levels of Tabriz aquifer", Trend. Appl. Sci. Res., 3(4), 319-329.   DOI
48 Palomo, A., Grutzeck, M.W. and Blanco, M.T. (1999), "Alkaliactivated fly ashes: A cement for the future", Cement Concrete Res., 29(8), 1323-1329.   DOI
49 Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. and Tanyildizi, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Softw., 40(9), 856-863.   DOI