Tahmin Metotları Kullanılarak Farklı Kür Koşullarında Üretilen Ferrokrom Cürufu Esaslı Geopolimer Betonların Basınç Dayanım Tahmini

Yıl 2021, Cilt: 23 Sayı: 69, 881 - 891, 15.09.2021

Yaşar Kalkan Mehmet Burhan Karakoç , Ahmet Özcan

https://doi.org/10.21205/deufmd.2021236916

Cited By: 1

Öz

Bu çalışmada farklı kür koşullarındaki ferrokrom cürufu (FS) esaslı geopolimer betonların basınç dayanımı (CS) değerleri incelenmiştir. Öğütülmüş FS sodyum hidroksit ve sodyum silikat karışımı ile aktive edilmiştir. Geopolimer beton numunelerinin silis modülü (Ms) 1.25, 1.50 ve 1.75 olarak seçilmiştir. Aynı zamanda FS yerine %0, %10 ve %20 oranlarında silis dumanı (SF) ikame edilerek numuneler hazırlanmıştır. Böylece 9 grup geopolimer beton numunesi üretilmiştir. Farklı kür sürelerinde (24, 48, 72 ve 96 saat) ve kür sıcaklıklarında (23, 40, 60, 80 ve 100 °C), numunelerin CS değerleri belirlenmiştir. Aynı zamanda, numunelerin CS tahmini için çok katmanlı algılayıcı sinir ağı (MLPNN), aşırı öğrenme makinesi sinir ağı (ELMNN) ve M5 model ağacı modellenmiştir, tahmin ve deney sonuçları karşılaştırılmıştır. Deney sonuçlarına göre, kür süresi arttıkça genellikle CS değerleri artmıştır, fakat SF ilavesi arttıkça, CS değerleri genellikle azalmıştır. %100 FS içeren, silis modülü 1.25 olan ve 24-48-72 veya 96 saat 100°C’de kür edilen numunede en büyük CS elde edişmiştir. Test aşamasındaki MLPNN, ELMNN ve M5 model ağacının R2 değerleri sırasıyla 0.956, 0.935 ve 0.922’dir. En iyi tahmin sonucunu veren MLPNN’nin test aşamasındaki ortalama karakök hatası (RMSE) 0.723 ve normalleştirilmiş kök ortalama kare hatası (NMRSE) 26.485’tir.

Anahtar Kelimeler

Geopolimer beton, Ferrokrom cürufu, Silis dumanı, Kür koşulları, ELMNN, MLPNN, M5 model ağacı

Destekleyen Kurum

İnönü Üniversitesi

Proje Numarası

2015-77

Compressive Strength Prediction of Ferrochrome Slag Based Geopolymer Concretes Produced Under Different Curing Conditions by Using Prediction Methods

Öz

In this study, compressive strength (CS) values of ferrochrome slag (FS) based geopolymer concretes in different curing conditions were investigated. Ground FS was activated with the mixture of sodium hydroxide and sodium silicate. The silica modulus (Ms) of the geopolymer concrete samples were selected as 1.25, 1.50 and 1.75. Also, samples were prepared by substituting 0%, 10% and 20% silica fume (SF) replacement the FS. Thus, 9 groups geopolymer concrete samples were produced. The CS values of the samples were determined on different curing times (24, 48, 72 and 96 hours) and curing temperatures (23, 40, 60, 80 and 100 °C). At the same time, multilayer perceptron neural network (MLPNN), extreme learning machine neural network (ELMNN) and M5 model tree were modeled for the CS prediction of the samples, the predict and experimental results were compared. According to the experiment results, it was determined that the CS values generally increased as the curing time increased, but with the addition of SF, the CS values generally decreased. The highest CS was obtained in the sample containing 100% FS that had silica modulus of 1.25 and cured at 100 °C for 24-48-72 or 96 hours. The R2 values of MLPNN, ELMNN and M5 model tree in testing phase were 0.956, 0.935 and 0.922, respectively. MLPNN, the model that gave the best predict result, had root mean square error (RMSE) of 0.723 and normalized root mean square error (NMRSE) of 26.485 in testing.

Anahtar Kelimeler

Geopolymer concrete, Ferrochrome slag, Silica fume, Curing condition, ELMNN, MLPNN, M5 model tree

Proje Numarası

2015-77

Kaynakça

• [1] Zhang, Y.J., Li, S., Wang, Y.C., Xu, D.L. 2012, Microstructural and strength evolutions of geopolymer composite reinforced by resin exposed to elevated temperature. Journal of Non-Crystalline Solids, 358, 620-624. DOI: 10.1016/j.jnoncrysol.2011.11.006
• [2] Thakur, R.N., Ghosh, S. 2009, Effect of mix composition on compressive strength and microstructure of fly ash based geopolymer composites. ARPN Journal Engineering and Applied Sciences, 4(4), 68-74.
• [3] Bakri, A.M.M.A., Kamarudin, H., Mohamed, H., Ruzaidi, C.M., Rafiza, A.R., Faheem, M.T.M., Izzat, A.M. 2011, Properties and microstructural characteristics of geopolymers using fly ash with different percentages of kaolin at room temperature curing, Australian Journal of Basic and Applied Sciences, 5(10), 824-828.
• [4] Yılmaz, A., Sütaş, İ. 2008. Ferrokrom cürufunun yol temel malzemesi olarak kullanımı, İMO Teknik Dergi, 294, 4455-4470. (In Turkish)
• [5] Yadollahi, M.M., Dener, M. 2019. Investigation of elevated temperature on compressive strength and microstructure of alkali activated slag based cements, European Journal of Environmental and Civil Engineering, 1-15. DOI: 10.1080/19648189.2018.1557562
• [6] Özcan, A., Karakoç, M.B. 2019. Evaluation of sulfate and salt resistance of ferrochrome slag and blast furnace slag-based geopolymer concretes, Structural Concrete, 20, 1607-1621. DOI: 10.1002/suco.201900061
• [7] Özcan, A., Karakoç, M.B. 2019. The resistance of blast furnace slag‑ and ferrochrome slag‑based geopolymer concrete against acid attack, International Journal of Civil Engineering, 17, 1571–1583. DOI: 10.1007/s40999-019-00425-2
• [8] Özdal, M., Karakoç, M.B., Özcan, A. 2019. Investigation of the properties of two different slag-based geopolymer concretes exposed to freeze–thaw cycles, Structural Concrete, 1-9. DOI: 10.1002/suco.201900441
• [9] Türkmen, İ., Karakoç, M.B., Kantarcı, F., Maraş, M.M., Demirboğa, R. 2016. Fire resistance of geopolymer concrete produced from Elazığ ferrochrome slag, Fire and materials, 40, 836-847. DOI: 10.1002/fam.2348
• [10] Karakoç, M.B., Türkmen, İ., Maraş, M.M., Kantarcı, F., Demirboğa, R. 2016. Sulfate resistance of ferrochrome slag based geopolymer concrete, Ceramics International, 42, 1254–1260. DOI: 10.1016/j.ceramint.2015.09.058
• [11] Karakoç, M.B., Türkmen, İ., Maraş, M.M., Kantarci, F., Demirboğa, R., Toprak, M.U. 2014. Mechanical properties and setting time of ferrochrome slag based geopolymer paste and mortar, Construction and Building Materials, 72, 283-292. DOI: 10.1016/j.conbuildmat.2014.09.021
• [12] Nath, S.K. 2018. Geopolymerization behavior of ferrochrome slag and fly ash blends, Construction and Building Materials, 181, 487–494. DOI: 10.1016/j.conbuildmat.2018.06.070
• [13] Ken, P.W., Ramli, M., Ban, C.C. 2015. An overview on the influence of various factors on the properties of geopolymer concrete derived from industrial by-products, Construction and Building Materials, 77, 370-395. DOI: 10.1016/j.conbuildmat.2014.12.065
• [14] Mohajerani, A., Suter, D., Jeffrey-Bailey, T., Song, T., Arulrajah, A., Horpibulsuk, S., Law, D. 2019. Recycling waste materials in geopolymer concrete, Clean Technologies and Environmental Policy, 21, 493–515. DOI: 10.1007/s10098-018-01660-2
• [15] Öztaş, A., Pala, M., Özbay, E., Kanca, E., Çağlar, N., Bhatti, M.A. 2006. Predicting the compressive strength and slump of high strength concrete using neural network, Construction and Building Materials, 20, 769-775. DOI: 10.1016/j.conbuildmat.2005.01.054
• [16] Hadi, M.N.S., Al-Azzawi, M., Yu, T. 2018. Effects of fly ash characteristics and alkaline activator components on compressive strength of fly ash-based geopolymer mortar. Construction and Building Materials, 175, 41-54. DOI: 10.1016/j.conbuildmat.2018.04.092
• [17] Bondar, D. 2014. Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers, Journal of Materials in Civil Engineering, 26(3), 499-503. DOI: 10.1061/(ASCE)MT.1943-5533.0000829
• [18] Kamallo, A., Ganjkhanlou, Y., Aboutalebi, S.H., Nouranian, H. 2010. Modeling of compressive strength of metakaolin based geopolymers by the use of artificial neural network, International Journal of Engineering, Transactions A: Basics, 23(2), 145-152.
• [19] Deepa, C., Kumari, S.K., Sudha, V.P. 2010. Prediction of the compressive strength of high performance concrete mix using tree based modeling, International Journal of Computer Applications, 6(5), 18-24. DOI: 10.5120/1076-1406
• [20] Nazari, A., Torgal, F.P. 2013. Predicting compressive strength of different geopolymers by artificial neural networks, Ceramics International, 39, 2247-2257. DOI: 10.1016/j.ceramint.2012.08.070
• [21] Yadollahi, M.M., Benli, A., Demirboğa, R. 2015. Prediction of compressive strength of geopolymer composites using an artificial neural network, Materials Research Innovations, 19(6), 453-458. DOI: 10.1179/1433075X15Y.0000000020
• [22] Yadollahi, M.M., Benli, A., Demirboğa, R. 2017. Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites, Neural Computing and Applications, 28, 1453-1461. DOI: 10.1007/s00521-015-2159-6
• [23] Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S., Nehdi, M.L. 2018. Predicting compressive strength of lightweight foamed concrete using extreme learning machine model, Advances in Engineering Software, 115, 112-125. DOI: 10.1016/j.advengsoft.2017.09.004
• [24] Nadiri, A.A., Asadi, S., Babaizadeh, H., Naderi, K. 2018. Hybrid fuzzy model to predict strength and optimum compositions of natural Alumina-Silica-based geopolymers, Computers and Concrete, 21(1), 103-110. DOI: 10.12989/cac.2018.21.1.103
• [25] Al-Shamiri, A.K., Kim, J.H., Yuan, T.-F., Yoon, Y.S. 2019. Modeling the compressive strength of high-strength concrete: An extreme learning approach, Construction and Building Materials, 208, 204-219. DOI: 10.1016/j.conbuildmat.2019.02.165
• [26] Dao, D.V., Ly, H.-B., Trinh, S.H., Le, T.-T., Pham, B.T. 2019. Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete, Materials, 12, 983. DOI: 10.3390/ma12060983
• [27] ASTM C39/C39M-18 2018. Standard test method for compressive strength of cylindrical concrete specimens. ASTM International, West Conshohocken, PA. DOI: 10.1520/C0039_C0039M-18
• [28] Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R. 2015. Extreme learning machine: algorithm, theory and applications, Artificial Intelligence Review, 44, 103-115. DOI: 10.1007/s10462-013-9405-z
• [29] Huang, G.-B., Zhu, Q.-Y., Siew, C.-K. 2006. Extreme learning machine: Theory and applications, Neurocomputing, 70, 489-501. DOI: 10.1016/j.neucom.2005.12.126
• [30] Huang, G., Huang, G.-B., Song, S., You, K. 2015. Trends in extreme learning machines: A review, Neural Networks, 61, 32-48. DOI: 10.1016/j.neunet.2014.10.001
• [31] Cabaneros, S.M., Calautit, J.K., Hughes, B.R. 2019. A review of artificial neural network models for ambient air pollution prediction, Environmental Modelling & Software, 119, 285-304. DOI: 10.1016/j.envsoft.2019.06.014
• [32] Tang, X., Zhang, L., Ding, X. 2019. SAR image despeckling with a multilayer perceptron neural network, International Journal of Digital Earth, 12(3), 354-374. DOI: 10.1080/17538947.2018.1447032
• [33] Mansouri, I., Ozbakkaloglu, T., Kisi, O., Xie, T. 2016. Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques, Materials and Structures, 49, 4319-4334. DOI: 10.1617/s11527-015-0790-4
• [34] Bhattacharya, B., Solomatine, D.P. 2005. Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing, 63, 381-396. DOI: 10.1016/j.neucom.2004.04.016
• [35] Naeej, M., Bali, M., Naeej, M.R., Amiri, J.V. 2013. Prediction of lateral confinement coefficient in reinforced concrete columns using M5’ machine learning method, KSCE Journal of Civil Engineering, 17(7), 1714-1719. DOI: 10.1007/s12205-013-0214-3
• [36] Yaman, M.A., Elaty, M.A., Taman, M. 2017. Predicting the ingredients of self compacting concrete using artificial neural network, Alexandria Engineering Journal, 56, 523-532. DOI: 10.1016/j.aej.2017.04.007
• [37] Görhan, G., Kürklü, G. 2014. The influence of the NaOH solution on the properties of the fly ash-based geopolymer mortar cured at different temperatures, Composites Part B: Engineering, 58, 371–377. DOI: 10.1016/j.compositesb.2013.10.082
• [38] Vijai, K., Kumutha, R., Vishnuram, B.G. 2010. Effect of types of curing on strength of geopolymer concrete, International Journal of The Physical Sciences, 5(9),1419-1423. DOI:
• [39] Atiş, C.D., Görür, E.B., Karahan, O., Bilim, C., İlkentapar, S., Luga, E. 2015. Very high strength (120 MPa) class F fly ash geopolymer mortar activated at different NaOH amount, heat curing temperature and heat curing duration, Construction and Building Materials, 96, 673–678. DOI: 10.1016/j.conbuildmat.2015.08.089
• [40] Swanepoel, J.C., Strydom, C.A. 2002. Utilisation of fly ash in a geopolymeric material, Applied Geochemistry, 17, 1143-1148. DOI: 10.1016/S0883-2927(02)00005-7
• [41] Okoye, F.N., Durgaprasad, J., Singh, N.B. 2015. Mechanical properties of alkali activated flyash/kaolin based geopolymer concrete, Construction and Building Materials, 98, 685–691. DOI: 10.1016/j.conbuildmat.2015.08.009