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Portland Kompoze Çimentosunun Priz Süresine Metakaolin Etkisinin Bulanık Mantıkla Tahmini

Year 2021, Volume: 2 Issue: 2, 29 - 34, 31.05.2021

Abstract

Bu çalışmada, Portland kompoze çimento ile içerisine metakaolin (%5-10-15-20) ikame edilerek toplam beş farklı çimento elde edilmiş ve metakaolinin, priz başlama ve priz sonu sürelerine etkisi araştırılmıştır. Elde edilen sonuçlara göre tüm karışım oranlarında priz başlama ve priz sonu sürelerinin kısaldığı belirlenmiştir. Ayrıca metakaolinin ikame miktarına bağlı olarak deneysel çalışmaların dışında bulanık mantıkla tahmin edilebilmesi için bir model oluşturulmuştur. Oluşturulan bu modelle elde edilen korelasyon katsayısının priz başlama süresi için 0.9851, priz sonu süresi için 0.9693 olduğu tespit edilmiştir. Sonuçlar, geliştirilen bu modelin çimento ve beton endüstrisinde başarılı bir şekilde uygulanabileceğini göstermiştir.

Thanks

Deneylerdeki katkıları için Mersin Çimento Fabrikası Yetkililerine ve Laboratuvar Çalışanlarına çok teşekkür ederiz.

References

  • [1] Fu X, Wang Z, Tao W, Yang C, Hou W, Dong Y, Wu X. (2002). Studies on blended cement with a large amount of fly ash. Cement and Concrete Research 32 (79): 1153-1159.
  • [2] Worrell E, Martin N, Price L. (2000). Potentials for energy efficiency improvement in the US cement industry. Energy 25 (12): 1189-1214.
  • [3] Yilmaz B, Ucar A, Oteyaka B, Uz V. (2007). Properties of zeolitic tuff (clinoptilolite) blended portland cement. Building and Environment 42: 3808-3815.
  • [4] Yilmaz B. (2008). A study on the effects of diatomite blend in natural pozzolan blended cements. Advances in Cement Research 20: 13-21.
  • [5] Yıldız, S., Balaydın, İ., Ulucan, Z. Ç. (2007). Prinç kabuğu külünün beton dayanımına etkisi. Fırat Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19(1), 85-91.
  • [6] Aruntaş, H. Y., Tokyay, M. (1996). Katkılı çimento üretiminde diatomitin puzolanik malzeme olarak kullanılabilirliği. Çimento ve Beton Dünyası, 1(4), 33-41.
  • [7] Shiqun L, Della MR. (1986). Investigation of relations between porosity, pore structure, and C1− diffusion of fly ash and blended cement pastes. Cement and Concrete Research 16 (5): 749-759.
  • [8] Prigione SG. (1987). Portland-zeolite-cement for minimizing alkali-aggregate expansion. Cement and Concrete Research 17 (3): 404-410.
  • [9] Saraswathy V, Muralidharan S, Thangavel K, Srinivasan S. (2003). Influence of activated fly ash on corrosion-resistance and strength of concrete. Cement and Concrete Composites 25 (7): 673-680.
  • [10] Homwuttiwong CS, Sirivivatnanon V. (2004). Influence of fly ash fineness on strength, drying shrinkage and sulfate resistance of blended cement mortar. Cement and Concrete Research 34 (7): 1087-1092.
  • [11] Simsek O. (2000). Yapı Malzemeleri II, Ankara University Yayınları, Ankara.
  • [12] TS EN 197-1. Çimento- Bölüm 1: Genel ÇimentolarBileşim, Özellikler ve Uygunluk Kriterleri. Türk Standartları, Ankara, 2012.
  • [13] Kocak, Y., Nas, S. (2014). The effect of using fly ash on the strength and hydration characteristics of blended cements. Construction and Building Materials, 73, 25-32.
  • [14] Topçu, İ. B., Karakurt, C. (2007). Uçucu kül ve yüksek fırın cürufunun çimento üretiminde katkı olarak kullanımı. Ulusal Beton Kongresi, 395-404.
  • [15] Özdemir, İ., Koçak, Y. (2020). Pirinç Kabuğu Külü İkameli Çimentoların Fiziksel ve Mekanik Özelliklerinin Araştırılması. El-Cezeri Journal of Science and Engineering, 7(1), 160-168.
  • [16] Demir, I., Güzelkücük, S., Sevim, Ö. (2018). Effects of sulfate on cement mortar with hybrid pozzolan substitution. Engineering Science and Technology, an International Journal, 21(3), 275-283.
  • [17] Ozcan, G., Kocak, Y., Gulbandilar, E. (2017). Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models. Computers and Concrete, 19(3), 275-282.
  • [18] Sarıdemir M. (2009). Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural Networks. Advances in Engineering Software 40: 350–355.
  • [19] Ashrafi, H. R., Jalal, M., Garmsiri, K. (2010). Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. Expert Systems with Applications, 37(12), 7663-7668.
  • [20] Subaşı, S. (2009). Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique. Scientific research and essays, 4(4), 289-297.
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  • [22] Ozcan, G., Kocak, Y., Gulbandilar, E. (2018). Compressive strength estimation of concrete containing zeolite and diatomite: an expert system implementation. Computers and Concrete, 21(1), 21-30.
  • [23] Boğa, A. R., Öztürk, M., Topcu, I. B. (2013). Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Composites Part B: Engineering, 45(1), 688-696.
  • [24] Sadrmomtazi, A., Sobhani, J., Mirgozar, M. A. (2013). Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction and Building Materials, 42, 205-216.
  • [25] Özcan, F., Atiş, C. D., Karahan, O., Uncuoğlu, E., Tanyildizi, H. (2009). Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Advances in Engineering Software, 40(9), 856-863.
  • [26] Topcu IB, Saridemir M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science 41: 305-311.
  • [27] Gulbandilar, E., Kocak, Y. (2013). Prediction of the effects of fly ash and silica fume on the setting time of Portland cement with fuzzy logic. Neural Computing and Applications, 22(7), 1485-1491.
  • [28] Tanyildizi H. (2009). Fuzzy logic model for the prediction of bond strength of high-strength lightweight concrete. Advances in Engineering Software 40: 161-169.
  • [29] Koçak, B., Koçak, Y., Yücedağ, İ. (2020). Prediction of Flexural Strength of Portland–Composite Cement Mortars Substituting Metakaolin Using Fuzzy Logic. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(4), 2377-2387.
  • [30] Guler I, Tunca A, Gulbandilar E. (2008). Detection of traumatic brain injuries using fuzzy logic algorithm. Expert Systems with Applications 34(2): 1312-1317.
  • [31] TS EN 196-3. Çimento deney metotları- Bölüm 3: Priz süresi ve hacim genleşme tayini. Türk Standartları, Ankara, 2002.
  • [32] Uçar, A., Karaca S., Gulbandilar E. (2016). Kayaçların İş İndeksinin Bulanık Mantık ile Tahmin Edilmesi, 67-72, 8.Uluslararası Kırmataş Sempozyumu, Kütahya, 13-14 Ekim 2016.
  • [33] Hsu, Y. L., Lee, C. H., Kreng, V. B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419-425.
  • [34] Ajayi, A. O., Aderounmu, G. A., Soriyan, H. A., David, A. (2010). An intelligent quality of service brokering model for e-commerce. Expert Systems with Applications, 37(1), 816-823.

Prediction the Effects of Metakaolin on the Setting Time of Portland Composite Cement with Fuzzy Logic

Year 2021, Volume: 2 Issue: 2, 29 - 34, 31.05.2021

Abstract

In this study, a total of five different cements were obtained by substituting Portland composite cement with metakaolin (5-10-15-20%) and the affects of metakaolin on the initial and final setting times were investigated. According to the results, it was determined that the initial and final setting times were shortened in all mixing ratios. In addition, a model was created to predict metakaolin with fuzzy logic outside of experimental studies, depending on the amount of substitution. The correlation coefficient obtained with this model was determined to be 0.9851 for the initial setting time and 0.9693 for the final setting time. The results showed that this developed model can be applied successfully in the cement and concrete industry.

References

  • [1] Fu X, Wang Z, Tao W, Yang C, Hou W, Dong Y, Wu X. (2002). Studies on blended cement with a large amount of fly ash. Cement and Concrete Research 32 (79): 1153-1159.
  • [2] Worrell E, Martin N, Price L. (2000). Potentials for energy efficiency improvement in the US cement industry. Energy 25 (12): 1189-1214.
  • [3] Yilmaz B, Ucar A, Oteyaka B, Uz V. (2007). Properties of zeolitic tuff (clinoptilolite) blended portland cement. Building and Environment 42: 3808-3815.
  • [4] Yilmaz B. (2008). A study on the effects of diatomite blend in natural pozzolan blended cements. Advances in Cement Research 20: 13-21.
  • [5] Yıldız, S., Balaydın, İ., Ulucan, Z. Ç. (2007). Prinç kabuğu külünün beton dayanımına etkisi. Fırat Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19(1), 85-91.
  • [6] Aruntaş, H. Y., Tokyay, M. (1996). Katkılı çimento üretiminde diatomitin puzolanik malzeme olarak kullanılabilirliği. Çimento ve Beton Dünyası, 1(4), 33-41.
  • [7] Shiqun L, Della MR. (1986). Investigation of relations between porosity, pore structure, and C1− diffusion of fly ash and blended cement pastes. Cement and Concrete Research 16 (5): 749-759.
  • [8] Prigione SG. (1987). Portland-zeolite-cement for minimizing alkali-aggregate expansion. Cement and Concrete Research 17 (3): 404-410.
  • [9] Saraswathy V, Muralidharan S, Thangavel K, Srinivasan S. (2003). Influence of activated fly ash on corrosion-resistance and strength of concrete. Cement and Concrete Composites 25 (7): 673-680.
  • [10] Homwuttiwong CS, Sirivivatnanon V. (2004). Influence of fly ash fineness on strength, drying shrinkage and sulfate resistance of blended cement mortar. Cement and Concrete Research 34 (7): 1087-1092.
  • [11] Simsek O. (2000). Yapı Malzemeleri II, Ankara University Yayınları, Ankara.
  • [12] TS EN 197-1. Çimento- Bölüm 1: Genel ÇimentolarBileşim, Özellikler ve Uygunluk Kriterleri. Türk Standartları, Ankara, 2012.
  • [13] Kocak, Y., Nas, S. (2014). The effect of using fly ash on the strength and hydration characteristics of blended cements. Construction and Building Materials, 73, 25-32.
  • [14] Topçu, İ. B., Karakurt, C. (2007). Uçucu kül ve yüksek fırın cürufunun çimento üretiminde katkı olarak kullanımı. Ulusal Beton Kongresi, 395-404.
  • [15] Özdemir, İ., Koçak, Y. (2020). Pirinç Kabuğu Külü İkameli Çimentoların Fiziksel ve Mekanik Özelliklerinin Araştırılması. El-Cezeri Journal of Science and Engineering, 7(1), 160-168.
  • [16] Demir, I., Güzelkücük, S., Sevim, Ö. (2018). Effects of sulfate on cement mortar with hybrid pozzolan substitution. Engineering Science and Technology, an International Journal, 21(3), 275-283.
  • [17] Ozcan, G., Kocak, Y., Gulbandilar, E. (2017). Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models. Computers and Concrete, 19(3), 275-282.
  • [18] Sarıdemir M. (2009). Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural Networks. Advances in Engineering Software 40: 350–355.
  • [19] Ashrafi, H. R., Jalal, M., Garmsiri, K. (2010). Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. Expert Systems with Applications, 37(12), 7663-7668.
  • [20] Subaşı, S. (2009). Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique. Scientific research and essays, 4(4), 289-297.
  • [21] Kocak, Y., Gulbandilar, E., Akcay, M. (2015). Predicting the compressive strength of cement mortars containing FA and SF by MLPNN. Computers and Concrete, 15(5), 759-770.
  • [22] Ozcan, G., Kocak, Y., Gulbandilar, E. (2018). Compressive strength estimation of concrete containing zeolite and diatomite: an expert system implementation. Computers and Concrete, 21(1), 21-30.
  • [23] Boğa, A. R., Öztürk, M., Topcu, I. B. (2013). Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Composites Part B: Engineering, 45(1), 688-696.
  • [24] Sadrmomtazi, A., Sobhani, J., Mirgozar, M. A. (2013). Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Construction and Building Materials, 42, 205-216.
  • [25] Özcan, F., Atiş, C. D., Karahan, O., Uncuoğlu, E., Tanyildizi, H. (2009). Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Advances in Engineering Software, 40(9), 856-863.
  • [26] Topcu IB, Saridemir M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science 41: 305-311.
  • [27] Gulbandilar, E., Kocak, Y. (2013). Prediction of the effects of fly ash and silica fume on the setting time of Portland cement with fuzzy logic. Neural Computing and Applications, 22(7), 1485-1491.
  • [28] Tanyildizi H. (2009). Fuzzy logic model for the prediction of bond strength of high-strength lightweight concrete. Advances in Engineering Software 40: 161-169.
  • [29] Koçak, B., Koçak, Y., Yücedağ, İ. (2020). Prediction of Flexural Strength of Portland–Composite Cement Mortars Substituting Metakaolin Using Fuzzy Logic. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(4), 2377-2387.
  • [30] Guler I, Tunca A, Gulbandilar E. (2008). Detection of traumatic brain injuries using fuzzy logic algorithm. Expert Systems with Applications 34(2): 1312-1317.
  • [31] TS EN 196-3. Çimento deney metotları- Bölüm 3: Priz süresi ve hacim genleşme tayini. Türk Standartları, Ankara, 2002.
  • [32] Uçar, A., Karaca S., Gulbandilar E. (2016). Kayaçların İş İndeksinin Bulanık Mantık ile Tahmin Edilmesi, 67-72, 8.Uluslararası Kırmataş Sempozyumu, Kütahya, 13-14 Ekim 2016.
  • [33] Hsu, Y. L., Lee, C. H., Kreng, V. B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419-425.
  • [34] Ajayi, A. O., Aderounmu, G. A., Soriyan, H. A., David, A. (2010). An intelligent quality of service brokering model for e-commerce. Expert Systems with Applications, 37(1), 816-823.
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Uğur Güvenç 0000-0002-5193-7990

Burak Koçak 0000-0002-8640-1758

Yılmaz Koçak 0000-0002-5281-5450

Publication Date May 31, 2021
Submission Date April 26, 2021
Acceptance Date May 27, 2021
Published in Issue Year 2021 Volume: 2 Issue: 2

Cite

IEEE U. Güvenç, B. Koçak, and Y. Koçak, “Portland Kompoze Çimentosunun Priz Süresine Metakaolin Etkisinin Bulanık Mantıkla Tahmini”, Journal of ESTUDAM Information, vol. 2, no. 2, pp. 29–34, 2021.

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