Computers and Concrete

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Application of AI models for predicting properties of mortars incorporating waste powders under Freeze-Thaw condition

  • Cihan, Mehmet T. (Department of Civil Engineering, Corlu Engineering Faculty, Tekirdag Namik Kemal University) ;
  • Arala, Ibrahim F. (Department of Civil Engineering, Corlu Engineering Faculty, Tekirdag Namik Kemal University)
  • Received : 2021.07.05
  • Accepted : 2022.03.03
  • Published : 2022.03.25
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Abstract

The usability of waste materials as raw materials is necessary for sustainable production. This study investigates the effects of different powder materials used to replace cement (0%, 5% and 10%) and standard sand (0%, 20% and 30%) (basalt, limestone, and dolomite) on the compressive strength (fc), flexural strength (fr), and ultrasonic pulse velocity (UPV) of mortars exposed to freeze-thaw cycles (56, 86, 126, 186 and 226 cycles). Furthermore, the usability of artificial intelligence models is compared, and the prediction accuracy of the outputs is examined according to the inputs (powder type, replacement ratio, and the number of cycles). The results show that the variability of the outputs was significantly high under the freeze-thaw effect in mortars produced with waste powder instead of those produced with cement and with standard sand. The highest prediction accuracy for all outputs was obtained using the adaptive-network-based fuzzy inference system model. The significantly high prediction accuracy was obtained for the UPV, fc, and fr of mortars produced using waste powders instead of standard sand (R2 of UPV, fc and ff is 0.931, 0.759 and 0.825 respectively), when under the freeze-thaw effect. However, for the mortars produced using waste powders instead of cement, the prediction accuracy of UPV was significantly high (R2=0.889) but the prediction accuracy of fc and fr was low (R2fc=0.612 and R2ff=0.334).

Keywords

Acknowledgement

The research described in this paper was financially supported by the Tekirdag Namik Kemal University.

References

  1. Aral, I.F. and Cihan, M.T. (2017), "Investigation of mortars containing different aggregate waste powder under freezingthawing effect", IATS'17 8th International Advanced Technologies Symposium, Elazig, October.
  2. Aral, I.F. and Cihan, M.T. (2018), "Investigation of properties of mortars containing waste stone powder instead of sand under freezing-thawing effect", IOP Conference Series: Earth and Environmental Science, September. https://doi.org/10.1088/1755-1315/362/1/012169.
  3. Awoyera P.O., Ajith Abraham I.M., and Viloria A. (2021), "A new formulation for strength characteristics of steel slag aggregate concrete using an artificial intelligence-based approach", Comput. Concrete, 27(4), 333-341. https://doi.org/10.12989/cac.2021.27.4.333.
  4. Awoyera, P.O., Perumal, P., Ohenoja, K. and Mansouri, I. (2022), "Upcycling CO2 for enhanced performance of recycled aggregate concrete and modeling of properties", Structural Integrity of Recycled Aggregate Concrete Produced with Fillers and Pozzolans, Woodhead Publishing. https://doi.org/10.1016/B978-0-12-824105-9.00017-2.
  5. Barbhuiya, S. (2011), "Effects of fly ash and dolomite powder on the properties of self-compacting concrete", Constr. Build. Mater., 25(8), 3301-3305. https://doi.org/10.1016/j.conbuildmat.2011.03.018.
  6. Beeralingegowda, B. and Gundakalle, V. (2013), "The effect of addition of Limestone Powder on the properties of selfcompacting concrete", Int. J. Innov. Res. Sci. Eng. Tech., 2(9), 4996.
  7. Binici, H. and Aksogan, O. (2018), "Durability of concrete made with natural granular granite, silica sand and powders of waste marble and basalt as fine aggregate", J. Build. Eng., 19, 109-121. https://doi.org/10.1016/j.jobe.2018.04.022.
  8. Bonavetti, V. and Irassar, E. (1994), "The effect of stone dust content in sand", Cement Concrete Res., 24(3), 580-590. https://doi.org/10.1016/0008-8846(94)90147-3.
  9. Breiman, L. (1996), "Bagging predictors", Machine Learn., 24(2), 123-140. https://doi.org/10.1023/A:1018054314350.
  10. Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984), "Classification and regression trees", Wadsworth Int. Group, 37(15), 237-251. https://doi.org/10.1201/9781315139470.
  11. Bustnes, H., Lagerblad, B. and Forssberg, E. (2004), "The function of filler in concrete", Mater. Struct., 37, 74-81. https://doi.org/10.1007/BF02486602.
  12. Celik, T. and Marar, K. (1996), "Effects of crushed stone dust on some properties of concrete", Cement Concrete Res., 26(7), 1121-1130. https://doi.org/10.1016/0008-8846(96)00078-6.
  13. Chang, S.C., Wang, C.C. and Wang, H.Y. (2018), "Study on the engineering and electricity properties of cement mortar added with waste LCD glass and piezoelectric powders", Comput. Concrete, 21(3), 311-319. https://doi.org/10.12989/cac.2018.21.3.311.
  14. Chopra, P., Sharma, R.K. and Kumar, M. (2016), "Prediction of compressive strength of concrete using artificial neural network and genetic programming", Adv. Mater. Sci. Eng., 2016, 7648467. https://doi.org/10.1155/2016/7648467.
  15. Chopra, P., Sharma, R.K., Kumar, M. and Chopra, T. (2018), "Comparison of machine learning techniques for the prediction of compressive strength of concrete", Adv. Civil Eng., 2018, 5481705. https://doi.org/10.1155/2018/5481705.
  16. Devi, K., Saini, B. and Aggarwal, P. (2018), "Effect of accelerators with waste material on the properties of cement paste and mortar", Comput. Concrete, 22(2), 153-159. https://doi.org/10.12989/cac.2018.22.2.153.
  17. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  18. Elyamany, H.E., Elmoaty, A.E.M.A. and Mohamed, B. (2014), "Effect of filler types on physical, mechanical and microstructure of self-compacting concrete and flow-able concrete", Alexandria Eng. J., 53(2), 295-307. https://doi.org/10.1016/j.aej.2014.03.010.
  19. Enstitusu, T.S. (2000), TS EN 1015-3 Kagir Harci Deney Metotlari-Bolum 3: Taze Harc Kivaminin Tayini (Yayilma Tablasi ile), Turk Standartlari Enstitusu, Ankara, Turkiye.
  20. Enstitusu, T.S. (2012), TS EN 12504-4 Beton Deneyleri, Bolum4: Ultrasonik Atimli Dalga Hizinin Tayini, Turk Standartlari Enstitusu, Ankara, Turkiye.
  21. Enstitusu, T.S. (2016), TS EN 196-1 Cimento deney metotlari- Bolum 1: Dayanim tayini, Turk Standartlari Enstitusu, Ankara, Turkiye.
  22. Erdem, R.T. and O zturk, A.U. (2012), "Mermer tozu katkisinin cimento harci donma-cozunme ozellikleri uzerine etkisi", Bitlis Eren U niversitesi Fen Bilimleri Dergisi, 1(2), 85-91.
  23. Etli, S., Cemalgil, S. and Onat, O. (2021), "Effect of pumice powder and artificial lightweight fine aggregate on selfcompacting mortar", Comput. Concrete, 27(3), 241-252. https://doi.org/10.12989/cac.2021.27.3.241.
  24. Friedman, J.H. (1991), "Multivariate adaptive regression splines", Annal. Statistics, 19(1), 1-67. https://doi.org/10.1214/aos/1176347963.
  25. Golafshani, E.M. and Pazouki, G. (2018), "Predicting the compressive strength of self-compacting concrete containing fly ash using a hybrid artificial intelligence method", Comput. Concrete, 22(4), 419-437. https://doi.org/10.12989/cac.2018.22.4.419.
  26. Haykin, S. (2007), "Neural networks: A Comprehensive foundation", Prentice-Hall Inc, Upper Saddle River, NJ, USA.
  27. Ilangovana, R., Mahendrana, N. and Nagamanib, K. (2008), "Strength and durability properties of concrete containing quarry rock dust as fine aggregate", ARPN J. Eng. Appl. Sci., 3(5), 20-26.
  28. Jackiewicz-Rek, W., Zalegowski, K., Garbacz, A. and Bissonnette, B. (2015), "Properties of cement mortars modified with ceramic waste fillers", Proc. Eng., 108, 681-687. https://doi.org/10.1016/j.proeng.2015.06.199.
  29. Jang, J.S. (1993), "ANFIS: adaptive-network-based fuzzy inference system", IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541.
  30. Kabay, N., Tufekci, M.M., Kizilkanat, A.B. and Oktay, D. (2015), "Properties of concrete with pumice powder and fly ash as cement replacement materials", Constr. Build. Mater., 85, 1-8. https://doi.org/10.1016/j.conbuildmat.2015.03.026.
  31. Kankam, C.K., Meisuh, B.K., Sossou, G. and Buabin, T.K. (2017), "Stress-strain characteristics of concrete containing quarry rock dust as partial replacement of sand", Case Stud. Constr. Mater., 7, 66-72. https://doi.org/10.1016/j.cscm.2017.06.004.
  32. Kelestemur, O., Yildiz, S., Gokcer, B. and Arici, E. (2014), "Statistical analysis for freeze-thaw resistance of cement mortars containing marble dust and glass fiber", Mater. Des., 60, 548-555. https://doi.org/10.1016/j.matdes.2014.04.013.
  33. Khademi, F., Akbari, M., Jamal, S.M. and Nikoo, M. (2017), "Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete", Front. Struct. Civil Eng., 11(1), 90-99. https://doi.org/10.1007/s11709-016-0363-9.
  34. Khashman, A. and Akpinar, P. (2017), "Non-destructive prediction of concrete compressive strength using neural networks", Proc. Comput. Sci., 108, 2358-2362. https://doi.org/10.1016/j.procs.2017.05.039.
  35. Kisi, O., Mansouri, I., Awoyera, P.O. and Lee, C.H. (2021), "Modeling flexural overstrength factor for steel beams using heuristic soft-computing methods", Struct., 34, 3238-3246. https://doi.org/10.1016/j.istruc.2021.09.075.
  36. Li, H., Huang, F., Cheng, G., Xie, Y., Tan, Y., Li, L. and Yi, Z. (2016), "Effect of granite dust on mechanical and some durability properties of manufactured sand concrete", Constr. Build. Mater., 109, 41-46. https://doi.org/10.1016/j.conbuildmat.2016.01.034.
  37. Liaw, A. and Wiener, M. (2002), "Classification and regression by randomForest", R News, 2(3), 18-22.
  38. Mansouri, I., Ostovari, M., Awoyera, P.O. and Hu, J.W. (2021), "Predictive modeling of the compressive strength of bacteriaincorporated geopolymer concrete using a gene expression programming approach", Comput. Concrete, 27(4), 319-332. https://doi.org/10.12989/cac.2021.27.4.319.
  39. Meisuh, B.K., Kankam, C.K. and Buabin, T.K. (2018), "Effect of quarry rock dust on the flexural strength of concrete", Case Stud. Constr. Mater., 8, 16-22. https://doi.org/10.1016/j.cscm.2017.12.002.
  40. Nguyen, H.A., Chang, T.P., Shih, J.Y. and Djayaprabha, H.S. (2018), "Enhancement of low-cement self-compacting concrete with dolomite powder", Constr. Build. Mater., 161, 539-546. https://doi.org/10.1016/j.conbuildmat.2017.11.148.
  41. Nikoo, M., Torabian Moghadam, F. and Sadowski, L. (2015), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng., 2015, 849126. https://doi.org/10.1155/2015/849126.
  42. Nisnevich, M., Sirotin, G. and Eshel, Y. (2003), "Lightweight concrete containing thermal power station and stone quarry waste", Mag. Concrete Res., 55(4), 313-320. https://doi.org/10.1680/macr.2003.55.4.313.
  43. Nunes, C. and Slizkova, Z. (2016), "Freezing and thawing resistance of aerial lime mortar with metakaolin and a traditional water-repellent admixture", Constr. Build. Mater., 114, 896-905. https://doi.org/10.1016/j.conbuildmat.2016.04.029.
  44. Rana, A., Kalla, P. and Csetenyi, L.J. (2015), "Sustainable use of marble slurry in concrete", J. Clean. Prod., 94, 304-311. https://doi.org/10.1016/j.jclepro.2015.01.053.
  45. Sadek, D.M., El-Attar, M.M. and Ali, H.A. (2016), "Reusing of marble and granite powders in self-compacting concrete for sustainable development", J. Clean. Prod., 121, 19-32. https://doi.org/10.1016/j.jclepro.2016.02.044.
  46. Szybilski, M. and Nocun-Wczelik, W. (2015), "The effect of dolomite additive on cement hydration", Proc. Eng., 108, 193-198. https://doi.org/10.1016/j.proeng.2015.06.136.
  47. Taylor, K.E. (2001), "Summarizing multiple aspects of model performance in a single diagram", J. Geophys. Res. Atmospheres, 106(D7), 7183-7192. https://doi.org/10.1029/2000JD900719.
  48. Team, R.C. (2013), "R: A language and environment for statistical computing".
  49. Cihan, M.T. (2019), "Prediction of concrete compressive strength and slump by machine learning methods", Adv. Civil Eng., 2019, 3069046. https://doi.org/10.1155/2019/3069046.
  50. Tiryaki, S. and Aydin, A. (2014), "An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model", Constr. Build. Mater., 62, 102-108. https://doi.org/10.1016/j.conbuildmat.2014.03.041.
  51. Uncik, S. and Kmecova, V. (2013), "The effect of basalt powder on the properties of cement composites", Proc. Eng., 65, 51-56. https://doi.org/10.1016/j.proeng.2013.09.010.
  52. Uysal, M. and Sumer, M. (2011), "Performance of selfcompacting concrete containing different mineral admixtures", Constr. Build. Mater., 25(11), 4112-4120. https://doi.org/10.1016/j.conbuildmat.2011.04.032.
  53. Uysal, M. and Yilmaz, K. (2011), "Effect of mineral admixtures on properties of self-compacting concrete", Cement Concrete Compos., 33(7), 771-776. https://doi.org/10.1016/j.cemconcomp.2011.04.005.
  54. Unlu, R. (2020), "An assessment of machine learning models for slump flow and examining redundant features", Comput. Concrete, 25(6), 565-574. https://doi.org/10.12989/cac.2020.25.6.565.
  55. Vapnik, V. (2013), The Nature of Statistical Learning Theory, Springer Science Business Media, NY, USA.
  56. Vijayalakshmi, M. and Sekar, A. (2013), "Strength and durability properties of concrete made with granite industry waste", Constr. Build. Mater., 46, 1-7. https://doi.org/10.1016/j.conbuildmat.2013.04.018.
  57. Wang, C.C., Ho, C.L., Wang, H.Y. and Tang, C. (2019), "Assessment of compressive strength of cement mortar with glass powder from the early strength", Comput. Concrete, 24(2), 151-158. https://doi.org/10.12989/cac.2019.24.2.151.
  58. Wankhade, M. and Kambekar, A. (2013), "Prediction of compressive strength of concrete using artificial neural network", Int. J. Sci. Res. Rev., 2(2), 11-26.
  59. Wold, S., Ruhe, A., Wold, H. and Dunn, I., WJ. (1984), "The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses", SIAM J. Sci. Statist. Comput., 5(3), 735-743. https://doi.org/10.1137/0905052.
  60. Zhu, W. and Gibbs, J.C. (2005), "Use of different limestone and chalk powders in self-compacting concrete", Cement Concrete Res., 35(8), 1457-1462. https://doi.org/10.1016/j.cemconres.2004.07.001.