Gradyan Güçlendirme Kullanarak Çelik Fiberli Geopolimerin Basınç Dayanımının Tahmini

Year 2024, Volume: 15 Issue: 3, 745 - 753, 30.09.2024

Necip Altay Eren

https://doi.org/10.24012/dumf.1511100

Abstract

Bu makalenin amacı, çelik fiberli Geopolimer Beton'un basınç dayanımını daha hızlı, doğru, ucuz ve zahmetsiz bir şekilde belirlemektir. Geleneksel laboratuvar testlerinin maliyetli olduğu ve zaman aldığı göz önüne alındığında, yapay zekâ uygulamalarının betonun basınç değerinin belirlenmesinde önemli alternatif yöntemlerinden birisi olabilir. Günümüzde yapay zekâ teknolojilerinin hızla gelişmesi, hassas ve hızlı sonuçlar elde edilmesine imkân tanımaktadır. Bu çalışmada, Makine Öğrenimi kullanılarak belirli bir veri seti üzerinden çelik fiberli geopolimer betonun basınç dayanımının tahmin edilmesi hedeflenmiştir. Literatürde bu konuda yapılan önceki çalışmalar incelenerek 84 veriden oluşan bir veri seti hazırlanmış ve analiz için uygun hale getirilmiştir. Veri seti, Gradyan Güçlendirme yöntemi kullanılarak Python programlama diliyle modellenmiş ve analiz edilmiştir. Yapılan çalışma sonucunda R2 değeri 0,9325 olarak elde edilmiştir. Bu sonuçlar, Gradyan Güçlendirme modelinin çelik fiberli geopolimer betonun basınç dayanımını tahmin etmede oldukça başarılı olduğunu göstermektedir. Sonuç olarak, yapay zekâ teknikleri basınç dayanım sonuçlarının daha hızlı tahmin edebilecek ve maliyetleri önemli ölçüde azaltacak imkânlar sunmaktadır. Bu çalışmanın bulguları, inşaat sektöründe gelecekteki araştırma ve uygulamalar için umut verici bir yöntem sunmaktadır.

Keywords

Geopolimer, Basınç dayanımı, Makina öğrenme, Çelik fiber

References

• [1] A. Karthik, K. Sudalaimani, and C. T. Vijaya Kumar, “Investigation on mechanical properties of fly ash-ground granulated blast furnace slag based self-curing bio-geopolymer concrete,” Construction and Building Materials, vol. 149, pp. 338–349, Sep. 2017, doi: https://doi.org/10.1016/j.conbuildmat.2017.05.139.
• [2] M.A. Javeed, M.V. Kumar, H. Narendra, “Studies on mix design of sustainable geopolymer concrete”, International Journal of Innovative Research in Engineering & Management (IJIREM), Volume-2: Issue-4, 2015.
• [3] S. A. Bernal, E. D. Rodríguez, R. Mejía de Gutiérrez, M. Gordillo, and J. L. Provis, “Mechanical and thermal characterisation of geopolymers based on silicate-activated metakaolin/slag blends,” Journal of Materials Science, vol. 46, no. 16, pp. 5477–5486, Aug. 2011, doi: https://doi.org/10.1007/s10853-011-5490-z.
• [4] J. Davidovits, “Geopolymers and geopolymeric materials”, J. Therm. Anal. 1989; 35: 429–441.
• [5] P. Duxson, J. L. Provis, G. C. Lukey, and J. S. J. van Deventer, “The role of inorganic polymer technology in the development of ‘green concrete,’” Cement and Concrete Research, vol. 37, no. 12, pp. 1590–1597, Dec. 2007, doi: https://doi.org/10.1016/j.cemconres.2007.08.018.
• [6] F. Deng, Y. He, S. Zhou, Y. Yu, H. Cheng, and X. Wu, “Compressive strength prediction of recycled concrete based on deep learning,” Construction and Building Materials, vol. 175, pp. 562–569, Jun. 2018, doi: https://doi.org/10.1016/j.conbuildmat.2018.04.169.
• [7] V.A. Chenarlogh, F. Razzazi, and N. Mohammadyahya, “A Multi-View Human Action Recognition System in Limited Data Case using Multi-Stream CNN,” Dec. 2019, doi: https://doi.org/10.1109/icspis48872.2019.9066079.
• [8] M. Roshani et al., “Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products,” Nuclear Engineering and Technology, vol. 53, no. 4, pp. 1277–1283, Apr. 2021, doi: https://doi.org/10.1016/j.net.2020.09.015.
• [9] B. Pourghebleh, A. Aghaei Anvigh, A. R. Ramtin, and B. Mohammadi, “The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments,” Cluster Computing, vol. 24, no. 3, pp. 2673–2696, May 2021, doi: https://doi.org/10.1007/s10586-021-03294-4.
• [10] A. Karbassi, B. Mohebi, S. Rezaee, and P. Lestuzzi, “Damage prediction for regular reinforced concrete buildings using the decision tree algorithm,” Computers & Structures, vol. 130, pp. 46–56, Jan. 2014, doi: https://doi.org/10.1016/j.compstruc.2013.10.006.
• [11] A. Ahmad, K. Chaiyasarn, F. Farooq, W. Ahmad, S. Suparp, and F. Aslam, “Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA,” Buildings, vol. 11, no. 8, p. 324, Jul. 2021, doi: https://doi.org/10.3390/buildings11080324.
• [12] H. Song, A. Ahmad, K. A. Ostrowski, and M. Dudek, “Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach,” Materials, vol. 14, no. 16, p. 4518, Aug. 2021, doi: https://doi.org/10.3390/ma14164518.
• [13] 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, vol. 2021, pp. 1–17, Jan. 2021, doi: https://doi.org/10.1155/2021/6618407.
• [14] F. Aslam et al., “Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete,” Advances in Civil Engineering, vol. 2020, pp. 1–23, Sep. 2020, doi: https://doi.org/10.1155/2020/8850535.
• [15] H.-H. Chu, M.A. Khan, M. Javed, A. Zafar, H. Alabduljabbar, 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, May 2021, doi: https://doi.org/10.1016/j.asej.2021.03.018.
• [16] P. Kumar, S. Sharma, B. Pratap, “Prediction of Compressive Strength of Geopolymer Fiber Reinforced Concrete Using Machine Learning” Civil Engineering Infrastructures Journal, 2024, doi: 10.22059/ceij.2024.364871.1956
• [17] J. H. Friedman, “Greedy function approximation: a gradient boosting machine”, Annals of Statistics, 2001, 1189–1232
• [18] C. Bente´jac, A. Cso¨rg\Ho, G. Martı´nez-Muñoz, “A comparative analysis of gradient boosting algorithms”, Artificial Intelligence Review. 54, 1937–1967, 2021.
• [19] P. Bu¨hlmann, T. Hothorn, “Boosting algorithms: Regularization, prediction and model fitting”, 2007.
• [20] T. Chen and C. Guestrin, “XGBoost: a Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, pp. 785–794, 2016, doi: https://doi.org/10.1145/2939672.2939785.
• [21] V.Q. Tran, V.Q. Dang, L.S. Ho, “Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach”, Construction and Building Materials, 323, 126578, 2022.
• [22] T.B. Redmond, M.H. Allen, “Compressive strength of composite brick and concrete masonry walls, MASONRY: Past and Present. Philadelphia: ASTM”, 195–232, 1975.
• [23] J. O. Ogutu, H.-P. Piepho, and T. Schulz-Streeck, “A comparison of random forests, boosting and support vector machines for genomic selection,” BMC Proceedings, vol. 5, no. S3, May 2011, doi: https://doi.org/10.1186/1753-6561-5-s3-s11.
• [24] Y. Huang, Z. Huo, G. Ma, L. Zhang, F. Wang, and J. Zhang, “Multi-objective optimization of fly ash-slag based geopolymer considering strength, cost and CO2 emission: A new framework based on tree-based ensemble models and NSGA-II,” Journal of Building Engineering, vol. 68, pp. 106070–106070, Jun. 2023, doi: https://doi.org/10.1016/j.jobe.2023.106070.
• [25] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, “Scikit-learn: machine learning in python”, J. Mach. Learn. Res. 12, 2825– 2830, 2011.
• [26] M. C. Kang, D. Y. Yoo, R. Gupta, “Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete”, Construction and Building Materials, 266, 121117, 2021. https://doi.org/10.1016/j.conbuildmat.2020.121117
• [27]https://medium.com/machine-learning t%C3%BCrkiye/korelasyon-katsay%C4%B1s%C4%B1-python-uygulamas%C4%B1-de83ea37ff23
• [28] T. Hastie, R. Tibshirani, J. Friedman, “The Elements of Statistical Learning”, Springer, 2009. ISBN : 978-0-387-84857-0
• [29] P. Zhang, J. Wang, Q. Li, J. Wan, and Y. Ling, “Mechanical and fracture properties of steel fiber-reinforced geopolymer concrete,” Science and Engineering of Composite Materials, vol. 28, no. 1, pp. 299–313, Jan. 2021, doi: https://doi.org/10.1515/secm-2021-0030.
• [30] Y. Ding and Y.-L. Bai, “Fracture Properties and Softening Curves of Steel Fiber-Reinforced Slag-Based Geopolymer Mortar and Concrete,” Materials, vol. 11, no. 8, p. 1445, Aug. 2018, doi: https://doi.org/10.3390/ma11081445.
• [31] Y. I. A. Aisheh, D. S. Atrushi, M. H. Akeed, S. Qaidi, and B. A. Tayeh, “Influence of polypropylene and steel fibers on the mechanical properties of ultra-high-performance fiber-reinforced geopolymer concrete,” Case Studies in Construction Materials, vol. 17, p. e01234, Dec. 2022, doi: https://doi.org/10.1016/j.cscm.2022.e01234.
• [32] K. Vijai, R. Kumutha, B.G. Vishnuram, “Effect of inclusion of steel fibres on the properties of geopolymer concrete composites”, Asian Journal of Civil Engineering, 13(3), 381-389, 2012.
• [33] S. F. A. Shah, B. Chen, S. Y. Oderji, M. Aminul Haque, and M. R. Ahmad, “Comparative study on the effect of fiber type and content on the performance of one-part alkali-activated mortar,” Construction and Building Materials, vol. 243, p. 118221, May 2020, doi: https://doi.org/10.1016/j.conbuildmat.2020.118221.
• [34] R. R. Bellum, “Influence of steel and PP fibers on mechanical and microstructural properties of fly ash-GGBFS based geopolymer composites”, Ceramics International, 48(5), 6808-6818, 2022.
• [35] N. A. Eren, R. Alzeebaree, A. Çevik, A. Niş, A. Mohammedameen, M. E. Gülşan, “Fresh and hardened state performance of self-compacting slag based alkali activated concrete using nanosilica and steel fiber,” Journal of composite materials, vol. 55, no. 28, pp. 4125–4139, Aug. 2021, doi: https://doi.org/10.1177/00219983211032390.
• [36] K. Z. Farhan, M. A. M. Johari, R. Demirboğa, “Evaluation of properties of steel fiber reinforced GGBFS-based geopolymer composites in aggressive environments,” Construction and Building Materials, vol. 345, p. 128339, Aug. 2022, doi: https://doi.org/10.1016/j.conbuildmat.2022.128339.
• [37] A. Pham, K. T. Nguyen, T. A. Le, K. Lee, “Investigation of impact behavior of innovative non-curing steel fiber geopolymer composites,” Case studies in construction materials, vol. 16, pp. e01011–e01011, Jun. 2022, doi: https://doi.org/10.1016/j.cscm.2022.e01011
• [38] A. C. R. da Silva, B. M. Almedia, M. M. Lucas, V. S. Candido, K. S. P. da Cruz, M. S. Oliveira, A. R. G. de Azevedo, S. N. Monteiro, “Fatigue behavior of steel fiber reinforced geopolymer concrete,” Case Studies in Construction Materials, vol. 16, p. e00829, Jun. 2022, doi: https://doi.org/10.1016/j.cscm.2021.e00829.
• [39] H. A. Goaiz, H. A. Shamsaldeen, M. A. Abdulrehman, and T. S. Al-Gasham, “Evaluation of Steel Fiber Reinforced Geopolymer Concrete Made of Recycled Materials,” International Journal of Engineering, vol. 35, no. 10, pp. 2018–2026, 2022, doi: https://doi.org/10.5829/ije.2022.35.10a.19.
• [40] W. Chen, J. Pan, B. Zhu, X. Ma, Y. Zhang, Y. Chen, X. Li, L. Meng, J. Cai, “Improving mechanical properties of 3D printable ‘one-part’ geopolymer concrete with steel fiber reinforcement,” Journal of Building Engineering, vol. 75, pp. 107077–107077, Sep. 2023, doi: https://doi.org/10.1016/j.jobe.2023.107077.
• [41] L. Qin, Z. Xu, Q. Liu, Z. Bai, C. Wang, Q. Luo, Y. Yuan, “Experimental study on mechanical properties of coal gangue base geopolymer recycled aggregate concrete reinforced by steel fiber and nano-Al2O3,” Reviews on advanced materials science, vol. 62, no. 1, Jan. 2023, doi: https://doi.org/10.1515/rams-2023-0343.
• [42] J. M. Their and M. Özakça, “Developing geopolymer concrete by using cold-bonded fly ash aggregate, nano-silica, and steel fiber,” Construction and Building Materials, vol. 180, pp. 12–22, Aug. 2018, doi: https://doi.org/10.1016/j.conbuildmat.2018.05.274.
• [43] S. A. Elkholy, H. I. El-Hassan, “Mechanical and micro-structure characterization of steel fiber-reinforced geopolymer concrete”, InProceedings of the ISEC, 2019.
• [44] Z. Xu, Q. Liu, H. Long, H. Deng, Z. Chen, and D. Hui, “Influence of nano-SiO2 and steel fiber on mechanical and microstructural properties of red mud-based geopolymer concrete,” Construction and Building Materials, vol. 364, p. 129990, Jan. 2023, doi: https://doi.org/10.1016/j.conbuildmat.2022.129990
• [45] S. C. Moghaddam, R. Madandoust, M. Jamshidi, I. M. Nikbin, “Mechanical properties of fly ash-based geopolymer concrete with crumb rubber and steel fiber under ambient and sulfuric acid conditions,” Construction and Building Materials, vol. 281, p. 122571, Apr. 2021, doi: https://doi.org/10.1016/j.conbuildmat.2021.122571
• [46] M. Ibraheem, F. Butt, R. M. Waqas, K. Hussain, R. F. Tufail, N. Ahmad, K. Usanova, M. A. Musarat, “Mechanical and Microstructural Characterization of Quarry Rock Dust Incorporated Steel Fiber Reinforced Geopolymer Concrete and Residual Properties after Exposure to Elevated Temperatures,” Materials, vol. 14, no. 22, pp. 6890–6890, Nov. 2021, doi: https://doi.org/10.3390/ma14226890.
• [47] J. Zheng, L. Qi, Y. Zheng, and L. Zheng, “Mechanical properties and compressive constitutive model of steel fiber-reinforced geopolymer concrete,” Journal of Building Engineering, vol. 80, pp. 108161–108161, Dec. 2023, doi: https://doi.org/10.1016/j.jobe.2023.108161.
• [48] A. K. Parashar, A. Kumar, P. Singh, N. Gupta, “Study on the mechanical properties of GGBS-based geopolymer concrete with steel fiber by cluster and regression analysis,” Asian journal of civil engineering, vol. 25, no. 3, pp. 2679–2686, Dec. 2023, doi: https://doi.org/10.1007/s42107-023-00937-2.
• [49] C. P. Devika, R. N. Deepthi, “Study of Flexural Behavior of Hybrid Fibre Reinforced Geopolymer Concrete Beam,” International Journal of Science and Research (IJSR), vol. 4, Issue 7, July 2015.
• [50] X. Gao, Q. L. Yu, H. J. H. Brouwers, “Evaluation of hybrid steel fiber reinforcement in high performance geopolymer composites,” Materials and Structures, vol. 50, 165, 2017. https://doi.org/10.1617/s11527-017-1030-x