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

Abstract

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.

Keywords

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

Project Number

2015-77

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

Abstract

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.

Keywords

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

Supporting Institution

İnönü Üniversitesi

Project Number

2015-77

References

1. [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. [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. [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. [4] Yılmaz, A., Sütaş, İ. 2008. Ferrokrom cürufunun yol temel malzemesi olarak kullanımı, İMO Teknik Dergi, 294, 4455-4470. (In Turkish)
5. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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