Research Article
Uçucu kül esaslı hafif geopolimer harçların basınç ve rötre sonuçlarının istatistiksel analizi ve modellenmesi
Abstract
Çimento, yapı malzemesi üretiminde bağlayıcı malzeme olarak oldukça yüksek oranlarda kullanılmaktadır. Doğal kaynak tüketimi ve atmosfere salınan CO2 miktarı göz önüne alındığında, çimento kullanım oranının düşürülmesi amacıyla alternatif bağlayıcı malzeme arayışı ile geopolimer kompozit üretimi gerçekleşmiştir. Atık malzemeler ile uygun kimyasal malzemelerin bir araya getirilmesi sonucu oluşan geopolimer, sunduğu birçok avantaj nedeniyle malzeme alanı için oldukça umut vericidir. Bu çalışmada, hafif geopolimer harçların mekanik özelliğini yansıtan basınç dayanım testi ile şekil değiştirme durumunu yansıtan rötre testi sonucunda elde edilen verilerin istatistiksel analiz ve modelleme işlemleri genetik ekspresyon programlama (GEP) ve çoklu doğrusal regresyon (ÇDR) kullanılarak gerçekleştirilmiştir. Hafif geopolimer harçların basınç dayanımı ve rötre değerlerine ulaşılması amacıyla sodyum hidroksit molaritesi, alkali/uçucu kül oranı, sodyum silikat/sodyum hidroksit oranı ve yaş girdi parametreleri dikkate alınarak oluşturulan GEP modelleri ile laboratuvar verilerine oldukça yakın sonuçlar elde edilmiştir. Basınç dayanımı ve rötre parametreleri için hedef değerler ile tahmin değerleri arasındaki ilişkiyi gösteren korelasyon katsayıları sırasıyla 0.94 ve 0.97 olarak elde edilmiş olup, diğer istatiksel değerlendirmeler sayesinde de oluşturulan modelin uygunluğu desteklenmiştir.
Supporting Institution
Harran Üniversitesi
Project Number
18252
References
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- R. Siddique, P. Aggarwal and Y. Aggarwal, Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in engineering software, 42(10), 780-786, 2011.
- K.C. Onyelowe, F. E. Jalal, M. E. Onyia, I. C. Onuoha. and G. U. Alaneme, Application of gene expression programming to evaluate strength characteristics of hydrated-lime-activated rice husk ash-treated expansive soil. Applied Computational Intelligence and Soft Computing, 2021.
- F. Farooq, W. Ahmed, A. Akbar, F. Aslam and R. Alyousef, Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. Journal of Cleaner Production, 292, 126032, 2021.
- J. R. Koza, Genetic programming: on the programming of computers by means of natural selection. vol. 1. MIT Press, 1992.
- M. A. Khan, S. A. Memon, F. Farooq, M. F. Javed, F. Aslam, and R. Alyousef, Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Advances in Civil Engineering, 2021.
- B. S. R. Yeddula and S. Karthiyaini, Experimental investigations and GEP modelling of compressive strength of ferrosialate based geopolymer mortars. Construction and Building Materials, 236, 117602, 2020.
- A. Nazari and F. P. Torgal, Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP. Expert Systems with Applications, 40(14), 5427-5438, 2013.
- A. Shahmansouri, H. A. Bengar and S. Ghanbari, Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method. Journal of Building Engineering, 31, 101326, 2020.
- A. Nazari, Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer. Neural Computing and Applications, 31(2), 767-776, 2019.
- H. Y. Leong, D. E. L. Ong, J. G. Sanjayan and A. A. Nazari, genetic programming predictive model for parametric study of factors affecting strength of geopolymers. RSC advances, 5(104), 85630-85639, 2015.
- B. A. Fillenwarth, and S. M. Sastry, Development of a predictive optimization model for the compressive strength of sodium activated fly ash based geopolymer pastes. Fuel, 147, 141-146, 2015.
- H. H. Chu, M. A. Khan, M. Javed, A. Zafar, M. I. Khan, H. Alabduljabbar and S. Qayyum, Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Engineering Journal, 2021.
- M. A. Khan, A. Zafar, F. Farooq, M. F. Javed, R. Alyousef, H. Alabduljabbar and M. I. Khan, Geopolymer concrete compressive strength via artificial neural network, adaptive neuro fuzzy ınterface system, and gene expression programming with k-fold cross validation. Front. ater. 8: 621163, 2021.
- A. Nazari, Compressive strength of geopolymers produced by ordinary Portland cement: Application of genetic programming for design. Materials & Design, 43, 356-366, 2013.
- S. Fakhrian, H. Behbahanih and S. Mashhadi, Predicting post-fire behavior of green geopolymer mortar containing recycled concrete aggregate via GEP approach. Journal of Soft Computing in Civil Engineering, 4(2): 22-45, 2020.
- A.A. Shahmansouri, H. A. Bengar, and S. Ghanbari, Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming. Journal of Concrete Structures and Materials, 5(1), 92-117, 2020.
- H.H. Chu, M.A. Khan, M. Javed, A. Zafar, M.I. Khan, H. Alabduljabbar and S. Qayyum. Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Engineering Journal, 12(4), 3603-3617, 2021.
- M. Su, Q. Zhong, and H.Peng. Regularized multivariate polynomial regression analysis of the compressive strength of slag-metakaolin geopolymer pastes based on experimental data. Construction and Building Materials, 303, 124529, 2021.
- Ekmen, Ş. Uçucu kül esaslı hafif geopolimer harçların taze ve sertleşmiş özelliklerinin incelenmesi, modellenmesi ve optimizasyonu. Doktora tezi, Harran Üniversitesi, 129, 2021.
Year 2022,
Volume: 9 Issue: 17, 304 - 318, 31.08.2022
Abstract
Project Number
18252
References
- D. Hardjito, S.E. Wallah, D. M. Sumajouw and B.V. Rangan, Factors influencing the compressive strength of fly ash-based geopolymer concrete. Civil engineering dimension, 6(2), 88-93, 2004.
- M. L. Oliveira, M. Izquierdo, X. Querol, R. N. Lieberman, B. K. Saikia, L. F. Silva, Nanoparticles from construction wastes: a problem to health and the environment. Journal of Cleaner Production, 219, 236-243, 2019.
- R. Siddique, P. Aggarwal and Y. Aggarwal, Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in engineering software, 42(10), 780-786, 2011.
- K.C. Onyelowe, F. E. Jalal, M. E. Onyia, I. C. Onuoha. and G. U. Alaneme, Application of gene expression programming to evaluate strength characteristics of hydrated-lime-activated rice husk ash-treated expansive soil. Applied Computational Intelligence and Soft Computing, 2021.
- F. Farooq, W. Ahmed, A. Akbar, F. Aslam and R. Alyousef, Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. Journal of Cleaner Production, 292, 126032, 2021.
- J. R. Koza, Genetic programming: on the programming of computers by means of natural selection. vol. 1. MIT Press, 1992.
- M. A. Khan, S. A. Memon, F. Farooq, M. F. Javed, F. Aslam, and R. Alyousef, Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest. Advances in Civil Engineering, 2021.
- B. S. R. Yeddula and S. Karthiyaini, Experimental investigations and GEP modelling of compressive strength of ferrosialate based geopolymer mortars. Construction and Building Materials, 236, 117602, 2020.
- A. Nazari and F. P. Torgal, Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP. Expert Systems with Applications, 40(14), 5427-5438, 2013.
- A. Shahmansouri, H. A. Bengar and S. Ghanbari, Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method. Journal of Building Engineering, 31, 101326, 2020.
- A. Nazari, Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer. Neural Computing and Applications, 31(2), 767-776, 2019.
- H. Y. Leong, D. E. L. Ong, J. G. Sanjayan and A. A. Nazari, genetic programming predictive model for parametric study of factors affecting strength of geopolymers. RSC advances, 5(104), 85630-85639, 2015.
- B. A. Fillenwarth, and S. M. Sastry, Development of a predictive optimization model for the compressive strength of sodium activated fly ash based geopolymer pastes. Fuel, 147, 141-146, 2015.
- H. H. Chu, M. A. Khan, M. Javed, A. Zafar, M. I. Khan, H. Alabduljabbar and S. Qayyum, Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Engineering Journal, 2021.
- M. A. Khan, A. Zafar, F. Farooq, M. F. Javed, R. Alyousef, H. Alabduljabbar and M. I. Khan, Geopolymer concrete compressive strength via artificial neural network, adaptive neuro fuzzy ınterface system, and gene expression programming with k-fold cross validation. Front. ater. 8: 621163, 2021.
- A. Nazari, Compressive strength of geopolymers produced by ordinary Portland cement: Application of genetic programming for design. Materials & Design, 43, 356-366, 2013.
- S. Fakhrian, H. Behbahanih and S. Mashhadi, Predicting post-fire behavior of green geopolymer mortar containing recycled concrete aggregate via GEP approach. Journal of Soft Computing in Civil Engineering, 4(2): 22-45, 2020.
- A.A. Shahmansouri, H. A. Bengar, and S. Ghanbari, Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming. Journal of Concrete Structures and Materials, 5(1), 92-117, 2020.
- H.H. Chu, M.A. Khan, M. Javed, A. Zafar, M.I. Khan, H. Alabduljabbar and S. Qayyum. Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Engineering Journal, 12(4), 3603-3617, 2021.
- M. Su, Q. Zhong, and H.Peng. Regularized multivariate polynomial regression analysis of the compressive strength of slag-metakaolin geopolymer pastes based on experimental data. Construction and Building Materials, 303, 124529, 2021.
- Ekmen, Ş. Uçucu kül esaslı hafif geopolimer harçların taze ve sertleşmiş özelliklerinin incelenmesi, modellenmesi ve optimizasyonu. Doktora tezi, Harran Üniversitesi, 129, 2021.
There are 21 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Project Number | 18252 |
| Publication Date | August 31, 2022 |
| Submission Date | January 5, 2022 |
| Published in Issue | Year 2022 Volume: 9 Issue: 17 |
