Prediction of Flexural Strength of Portland–Composite Cement Mortars Substituting Metakaolin Using Fuzzy Logic

Yıl 2020, , 2377 - 2387, 29.10.2020

Burak Koçak , Yılmaz Koçak , İbrahim Yücedağ

https://doi.org/10.29130/dubited.798315

Cited By: 3

Öz

In this study, Fuzzy Logic models have been introduced to predict flexural strength values of cement mortars. For this purpose, reference cement mortar containing only Portland–composite cement, and mixtures having metakaolin replacing 5, 10, 15 and 20% by weight of the Portland–composite cement were produced. The mortars’ flexural strength values were established at 2, 7, 28 and 56-day with standard cement test. In addition, Fuzzy Logic prediction models were created by using fuzzy triangular number coefficients and Gauss membership function to predict flexural strength of cement mortars. Subsequently, experimental with fuzzy results are compared. Accordingly, the correlation coefficient of flexural strength of cement mortars for fuzzy triangular number coefficients and Gauss membership function were found 0.84 and 0.87, respectively. These results show that between experimental and fuzzy results are a good harmony, and can be successfully applied in civil engineering applications.

Anahtar Kelimeler

Portland–composite cement, Metakaolin, Flexural strength, Fuzzy Logic

Teşekkür

The authors would like to thank Cimsa Mersin Cement Factory executives and the staff for their help in mechanical test.

Metakaolin İkameli Portland Kompoze Çimento Harçlarının Eğilme Dayanımının Bulanık Mantık Yaklaşımıyla Tahmin Edilmesi

Öz

Bu çalışmada, çimento harçlarının eğilme dayanım değerlerini tahmin etmek için Bulanık Mantık tahmin modelleri geliştirilmiştir. Bu amaçla, referans ve %5, 10, 15 ve 20 oranında metakaolin ikameli çimento harçaları üretilmiştir. Üretilen bu harçların eğilme dayanım değerleri 2, 7, 28 ve 56. günlerde standart çimento deneyleri ile tespit edilmiştir. Ayrıca, çimento harçlarının eğilme dayanımlarını tahmin etmek için üçgen bulanık sayı katsayıları ve Gauss üyelik fonksiyonu kullanılarak, Bulanık Mantık tahmin modelleri oluşturulmuştur. Deneysel sonuçlar Bulanık Mantık sonuçlarıyla karşılaştırılmıştır. Sonuç olarak, bulanık üçgen sayı katsayıları ve Gauss üyelik fonksiyonu için çimento harçlarının eğilme mukavemeti korelasyon katsayıları sırasıyla 0,84 ve 0,87 olarak bulunmuştur. Bu sonuçlar, test sonuçları ile bulanık sonuçlar arasında iyi bir uyum olduğunu ve inşaat mühendisliği uygulamalarında başarıyla uygulanabileceğini göstermektedir.

Anahtar Kelimeler

Portland kompoze çimento, Metakaolin, Eğilme dayanımı, Bulanık mantık

Kaynakça

• [1] D. Hatungimana, C. Taşköprü, M. İçhedef, M. M. Saç, and Ş. Yazıcı, “Compressive Strength, Water Absorption, Water Sorptivity and Surface Radon Exhalation Rate of Silica Fume and Fly Ash Based Mortar,” Journal of Building Engineering, vol. 23, pp. 369–376, 2019.
• [2] Y. Kocak, and S. Nas, “The Effect of Using Fly Ash on the Strength and Hydration Characteristics of Blended Cements,” Construction and Building Materials, vol. 73, pp. 25–32, 2014.
• [3] A. G. N. D. Darsanasiri, F. Matalkah, S. Ramli, K. Al–Jalode, A. Balachandra, and P. Soroushian, “Ternary Alkali Aluminosilicate Cement Based On Rice Husk Ash, Slag and Coal Fly Ash,” Journal of Building Engineering, vol. 19, pp. 36–41, 2018.
• [4] A. Joshaghani, “The Effect of Trass and Fly Ash in Minimizing Alkali–Carbonate Reaction in Concrete,” Construction and Building Materials, vol. 150, pp. 583–590, 2017.
• [5] Y. Kocak, E. Tascı, and U. Kaya, “The Effect of Using Natural Zeolite on the Properties and Hydration Characteristics of Blended Cements,” Construction and Building Materials, vol. 47, pp. 720–727, 2013.
• [6] H. Gerengi, Y. Kocak, A. Jażdżewska, M. Kurtay, and H. Durgun, “Electrochemical Investigations on the Corrosion Behaviour of Reinforcing Steel in Diatomite–and Zeolite–Containing Concrete Exposed to Sulphuric Acid,” Construction and Building Materials, vol. 49, pp. 471–477, 2013.
• [7] G. Asadollahfardi, P. M. Zadeh, and S. F. Saghravani, “The Effects of Using Metakaolin and Micro–Nanobubble Water on Concrete Properties,” Journal of Building Engineering, vol. 25, 2019.
• [8] R. Cai, Z. He, S. Tang, T. Wu, and E. Chen, “The Early Hydration of Metakaolin Blended Cements by Non–Contact Impedance Measurement,” Cement and Concrete Composites, vol. 92, pp. 70–81, 2018.
• [9] A. Hasanbeigi, L. Price, and E. Lin, “Emerging Energy–Efficiency and CO2 Emission–Reduction Technologies for Cement and Concrete Production: A Technical Review,” Renewable and Sustainable Energy Reviews, vol. 16, pp. 6220–6238, 2012.
• [10] X. Qian, J. Wang, L. Wang, and Y. Fang, “Enhancing the Performance of Metakaolin Blended Mortar through in–situ Production of Nano to Sub–Micro Calcium Carbonate Particles,” Construction and Building Materials, vol. 196, pp. 681–691, 2019.
• [11] P.M. Zadeh, S.F. Saghravani, and G. Asadollahfardi, “Mechanical and Durability Properties of Concrete Containing Zeolite Mixed with Meta‐Kaolin and Micro‐Nano Bubbles of Water,” Structural Concrete, vol. 20, pp. 786–797, 2019.
• [12] M. Najimi, J. Sobhani, B. Ahmadi, and M. Shekarchi, “An Experimental Study on Durability Properties of Concrete Containing Zeolite as A Highly Reactive Natural Pozzolan,” Construction and Building Materials, vol. 35, pp. 1023–1033, 2012.
• [13] A. Subaşı, and M. Emiroğlu, “Effect of Metakaolin Substitution on Physical, Mechanical and Hydration Process of White Portland Cement,” Construction and Building Materials, vol. 95, pp. 257–268, 2015.
• [14] G. Asadollahfardi, P. M. Zadeh, and S. F. Saghravani, “The Effects of Using Metakaolin and Micro–Nanobubble Water on Concrete Properties,” Journal of Building Engineering, vol. 25, 2019.
• [15] H. El–Diadamony, A.A. Amer, T.M. Sokkary, and S. El–Hoseny, “Hydration and Characteristics of Metakaolin Pozzolanic Cement Pastes,” HBRC Journal, vol. 14, no. 2, pp. 150–158, 2018.
• [16] R. Siddique, and J. Klaus, “Influence of Metakaolin on the Properties of Mortar and Concrete: A Review,” Applied Clay Science, vol. 43, no. 3–4, pp. 392–400, 2009.
• [17] M. Najimi, J. Sobhani, B. Ahmadi, M, and Shekarchi, “An Experimental Study on Durability Properties of Concrete Containing Zeolite as A Highly Reactive Natural Pozzolan,” Construction and Building Materials, vol. 35, pp. 1023–1033, 2012.
• [18] O. Keleştemur, and B. Demirel, “Effect of Metakaolin on the Corrosion Resistance of Structural Lightweight Concrete,” Construction and Building Materials, vol. 81, pp. 172–178, 2015.
• [19] H. S. Al–alaily, and A. A. Hassan, “Time–Dependence of Chloride Diffusion for Concrete Containing Metakaolin,” Journal of Building Engineering, vol. 7, pp. 159–169, 2016.
• [20] H.–S. Kim, S.–H. Lee ve H.–Y. Moon, “Strength Properties and Durability Aspects of High Strength Concrete Using Korean Metakaolin,” Construction and Building Materials, vol. 21, no. 6, pp. 1229–1237, 2007.
• [21] H. Yaprak, A. Karaci, and I. Demir, “Prediction of the Effect of Varying Cure Conditions and w/c Ratio on the Compressive Strength of Concrete Using Artificial Neural Networks,” Neural Computing and Application, vol. 22, pp. 133–141, 2013.
• [22] G. Eyyup, and Y. Kocak, “Application of Expert Systems in Prediction of Flexural Strength of Cement Mortars,” Computers and Concrete, vol. 18, no. 1, pp. 1-16, 2016.
• [23] S.A. Emamian, and H. Eskandari-Naddaf, “Effect of Porosity on Predicting Compressive and Flexural Strength of Cement Mortar Containing Micro and Nano-Silica by ANN and GEP,” Construction and Building Materials, vol. 218, pp. 8-27, 2019.
• [24] Y. Kocak, E. Gulbandilar, and M. Akcay, “Predicting the Compressive Strength of Cement Mortars Containing FA and SF by MLPNN,” Computers and Concrete, vol. 15, no. 5, pp. 759-770, 2015.
• [25] M. Jalal, Z. Grasley, N. Nassir, and H. Jalal, “Strength and Dynamic Elasticity Modulus of Rubberized Concrete Designed with ANFIS Modeling and Ultrasonic Technique,” Construction and Building Materials, vol. 240, 2020.
• [26] M.M. Khotbehsara, B.M. Miyandehi, F. Naseri, T. Ozbakkaloglu, F. Jafari, and E. Mohseni, “Effect of SnO2, ZrO2, and CaCO3 Nanoparticles on Water Transport and Durability Properties of Self-Compacting Mortar Containing Fly Ash: Experimental Observations and ANFIS Predictions,” Construction and Building Materials, vol. 158, pp. 823-834, 2018.
• [27] S. Motamedi, S. Shamshirband, D. Petković, and R. Hashim, “Application of Adaptive Neuro-Fuzzy Technique to Predict the Unconfined Compressive Strength of PFA-Sand-Cement Mixture,” Powder Technology, vol. 278, pp. 278-285, 2015.
• [28] G. Ozcan, Y. Kocak, and E. Gulbandilar, “Estimation of Compressive Strength of BFS and WTRP Blended Cement Mortars with Machine Learning Models,” Computers and Concrete, vol. 19, no. 3, pp. 275-282, 2017.
• [29] I. Guler, A. Tunca, E. Gulbandilar, “Detection of Traumatic Brain Injuries Using Fuzzy Logic Algorithm,” Expert Systems with Applications, vol. 34, no. 2, pp. 1312–1317, 2008.
• [30] E. Gulbandilar, and Y. Kocak, “Prediction the Effects of Fly Ash and Silica Fume on the Setting Time of Portland–Composite Cement with Fuzzy Logic,” Neural Computing and Applications, vol. 22, pp. 1485–1491, 2013.
• [31] H. Tanyildizi, “Fuzzy Logic Model for the Prediction of Bond Strength of High-Strength Lightweight Concrete,” Advances in Engineering Software, vol. 40, no. 3, pp. 161-169, 2009.
• [32] F. Demir, “A New Way of Prediction Elastic Modulus of Normal and High Strength Concrete—Fuzzy Logic,” Cement and Concrete Research, vol. 35, no. 8, pp. 1531-1538, 2005.
• [33] K. Güler, F. Demir, and F. Pakdamar, “Stress–Strain Modelling of High Strength Concrete by Fuzzy Logic Approach,” Construction and Building Materials, vol. 37, pp. 680-684, 2012.
• [34] TS EN 196-1, “Methods of Testing Cement-Part 1: Determination of Strength,” Turkish Standards, Ankara, Turkey, 2009.
• [35] Y.L. Hsu, C.H. Lee, and V.B. Kreng, “The Applicationof Fuzzy Delphi Method and Fuzzy AHP in Lubricant Regenerative Technology Selection,” Expert Systems with Applications, vol. 37, pp. 419–425, 2010.
• [36] A.O. Ajayi, G.A. Aderounmu, H.A. Soriyan, and A. David, “An Intelligent Quality of Service Brokering Model for e-Commerce,” Expert Systems with Applications, vol. 37, pp. 816–823, 2010.