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Predicting the compressive strength of SCC containing nano silica using surrogate machine learning algorithms

  • Neeraj Kumar Shukla (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Aman Garg (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Javed Bhutto (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Mona Aggarwal (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Mohamed Abbas (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Hany S. Hussein (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Rajesh Verma (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • T.M. Yunus Khan (Mechanical Engineering Department, College of Engineering, King Khalid University)
  • Received : 2023.05.14
  • Accepted : 2023.06.05
  • Published : 2023.10.25

Abstract

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

Keywords

Acknowledgement

The authors gratefully acknowledge their respective organizations for their help and support. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU), Kingdom of Saudi Arabia for funding this work through the Small Group Research Project under Grant Number RGP1/70/44.

References

  1. Afzali-Naniz, O. and Mazloom, M. (2019), "Assessment of the influence of micro- and nano-silica on the behavior of self-compacting lightweight concrete using full factorial design", Asian J. Civil Eng., 20, 57-70. https://doi.org/10.1007/s42107-018-0088-2.
  2. Ahmad, A., Ostrowski, K.A., Maslak, M., Farooq, F., Mehmood, I. and Nafees, A. (2021), "Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature", Mater. (Basel), 14, 4222. https://doi.org/10.3390/ma14154222.
  3. Al-Gburi, S.N.A., Akpinar, P. and Helwan, A. (2022), "Machine learning in concrete's strength prediction", Comput. Concrete, 29(6), 433-444. https://doi.org/10.12989/cac.2022.29.6.433.
  4. Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P. and Pilakoutas, K. (2021), "Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models", Cement Concrete Res., 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449.
  5. Aydin, A.C., Nasl, V.J. and Kotan, T. (2018) "The synergic influence of nano-silica and carbon nano tube on self-compacting concrete", J. Build. Eng., 20, 467-475. https://doi.org/10.1016/j.jobe.2018.08.013.
  6. Baloch, H., Usman, M., Rizwan, S.A. and Hanif, A. (2019), "Properties enhancement of super absorbent polymer (SAP) incorporated self-compacting cement pastes modified by nano silica (NS) addition", Constr. Build. Mater., 203, 18-26. https://doi.org/10.1016/j.conbuildmat.2019.01.096.
  7. Beigi, M.H., Berenjian, J., Lotfi Omran, O., Sadeghi Nik, A. and Nikbin, I.M. (2013), "An experimental survey on combined effects of fibers and nanosilica on the mechanical, rheological, and durability properties of self-compacting concrete", Mater. Des., 50, 1019-1029. https://doi.org/10.1016/j.matdes.2013.03.046.
  8. Bernal, J., Reyes, E., Massana, J., Leon, N. and Sanchez, E. (2018), "Fresh and mechanical behavior of a self-compacting concrete with additions of nano-silica, silica fume and ternary mixtures", Constr. Build. Mater., 160, 196-210. https://doi.org/10.1016/j.conbuildmat.2017.11.048.
  9. Cai, R., Han, T., Liao, W., Huang, J., Li, D., Kumar, A. and Ma, H. (2020), "Prediction of surface chloride concentration of marine concrete using ensemble machine learning", Cement Concrete Res., 136, 106164. https://doi.org/10.1016/j.cemconres.2020.106164.
  10. Chaabene, W.B., Flah, M. and Nehdi, M.L. (2020), "Machine learning prediction of mechanical properties of concrete: Critical review", Constr. Build. Mater., 260, 119889. https://doi.org/10.1016/j.conbuildmat.2020.119889.
  11. Chinthakunta, R., Ravella, D.P., Sri Rama Chand, M. and Janardhan Yadav, M. (2021), "Performance evaluation of self-compacting concrete containing fly ash, silica fume and nano titanium oxide", Mater. Today Proc., 43, 2348-2354. https://doi.org/10.1016/j.matpr.2021.01.681.
  12. Chun, P.J., Izumi, S. and Yamane, T. (2021), "Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine", Comput. Aid. Civil Inf., 36, 61-72. https://doi.org/10.1111/mice.12564.
  13. DeRousseau, M.A., Laftchiev, E., Kasprzyk, J.R., Rajagopalan, B. and Srubar, W.V. (2019), "A comparison of machine learning methods for predicting the compressive strength of field-placed concrete", Constr. Build. Mater., 228, 116661. https://doi.org/10.1016/j.conbuildmat.2019.08.042.
  14. Dolatabad, Y.A., Abolpour, B. and Tazangi, M.A.J. (2021), "Investigating effects of nano particles of silica on the properties of self-compacting concrete containing Perlite, Leca, and Scoria light weight aggregates", Arab. J. Geosci., 14, 862. https://doi.org/10.1007/s12517-021-07233-w.
  15. Durgun, M.Y. and Atahan, H.N. (2017), "Rheological and fresh properties of reduced fine content self-compacting concretes produced with different particle sizes of nano SiO2", Constr. Build. Mater., 142, 431-443. https://doi.org/10.1016/j.conbuildmat.2017.03.098.
  16. 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.
  17. Edward Rasmussen, C., Williams, C.K.I. and Bach, F. (2006), Gaussian Processes for Machine Learning, MIT Press, Cambridge, MI, USA.
  18. Gao, Y., Zhou, W., Zeng, W., Pei, G. and Duan, K. (2021), "Preparation and flexural fatigue resistance of self-compacting road concrete incorporating nano-silica particles", Constr. Build. Mater., 278, 122380. https://doi.org/10.1016/j.conbuildmat.2021.122380.
  19. Garg, A., Aggarwal, P., Aggarwal, Y., Belarbi, M.O., Chalak, H.D., Tounsi, A. and Gulia, R. (2022), "Machine learning models for predicting the compressive strength of concrete containing nano silica", Comput. Concrete, 30(1), 33-42. https://doi.org/10.12989/cac.2022.30.1.033.
  20. Garg, A., Mukhopadhyay, T., Belarbi, M.O., Chalak, H.D., Singh, A. and Zenkour, A.M. (2023a), "On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR", Compos. Struct., 305, 116551. https://doi.org/10.1016/j.compstruct.2022.116551.
  21. Garg, A., Mukhopadhyay, T., Belarbi, M.O. and Li, L. (2023b), "Random forest-based surrogates for transforming the behavioral predictions of laminated composite plates and shells from FSDT to elasticity solutions", Compos. Struct., 309, 116756. https://doi.org/10.1016/j.compstruct.2023.116756.
  22. Guneyisi, E., Atewi, Y.R. and Hasan, M.F. (2019), "Fresh and rheological properties of glass fiber reinforced self-compacting concrete with nanosilica and fly ash blended", Constr. Build. Mater., 211, 349-362. https://doi.org/10.1016/j.conbuildmat.2019.03.087.
  23. Guneyisi, E., Gesoglu, M., Al-Goody, A. and Ipek, S. (2015a), "Fresh and rheological behavior of nano-silica and fly ash blended self-compacting concrete", Constr. Build. Mater., 95, 29-44. https://doi.org/10.1016/j.conbuildmat.2015.07.142.
  24. Guneyisi, E., Gesoglu, M., Azez, O.A. and O z, H.O . (2016), "Effect of nano silica on the workability of self-compacting concretes having untreated and surface treated lightweight aggregates", Constr. Build. Mater., 115, 371-380. https://doi.org/10.1016/j.conbuildmat.2016.04.055.
  25. Guneyisi, E., Gesoglu, M., Azez, O.A. and O z, H.O . (2015b), "Physico-mechanical properties of self-compacting concrete containing treated cold-bonded fly ash lightweight aggregates and SiO2 nano-particles", Constr. Build. Mater., 101, 1142-1153. https://doi.org/10.1016/j.conbuildmat.2015.10.117.
  26. Hani, N., Nawawy, O., Ragab, K.S. and Kohail, M. (2018), "The effect of different water/binder ratio and nano-silica dosage on the fresh and hardened properties of self-compacting concrete", Constr. Build. Mater., 165, 504-513. https://doi.org/10.1016/j.conbuildmat.2018.01.045.
  27. Jalal, M., Mansouri, E., Sharifipour, M. and Pouladkhan, A.R. (2012), "Mechanical, rheological, durability and microstructural properties of high performance self-compacting concrete containing SiO2 micro and nanoparticles", Mater. Des., 34, 389-400. https://doi.org/10.1016/j.matdes.2011.08.037.
  28. Jalal, M., Teimortashlu, E. and Grasley, Z. (2019), "Performance-based design and optimization of rheological and strength properties of self-compacting cement composite incorporating micro/ nano admixtures", Compos. Part B Eng., 163, 497-510. https://doi.org/10.1016/j.compositesb.2019.01.028.
  29. Kumar, G.V. and Kumar, B.N. (2022), "Effect on mechanical, durability and micro structural properties of high strength self compacting concrete with inclusion of micro and nano silica", Mater. Today Proc., 60, 569-575. https://doi.org/10.1016/j.matpr.2022.02.064.
  30. Li, Z. and Yan G. (2022), "Machine learning for structural stability: Predicting dynamics responses using physics-informed neural networks", Comput. Concrete, 29(6), 419-432. https://doi.org/10.12989/cac.2022.29.6.419.
  31. Madandoust, R., Ranjbar, M.M. and Yasin Mousavi, S. (2011), "An investigation on the fresh properties of self-compacted lightweight concrete containing expanded polystyrene", Constr. Build. Mater., 25, 3721-3731. https://doi.org/10.1016/j.conbuildmat.2011.04.018.
  32. Mahapatra, C.K. and Barai, S.V (2018), "Hybrid fiber reinforced self compacting concrete with fly ash and colloidal nano silica: A systematic study", Constr. Build. Mater., 160, 828-838. https://doi.org/10.1016/j.conbuildmat.2017.11.131.
  33. Mahdikhani, M. and Ramezanianpour, A.A. (2014), "Mechanical properties and durability of self consolidating cementitious materials incorporating nano silica and silica fume", Comput. Concrete, 14(2), 175-191. https://doi.org/10.12989/cac.2014.14.2.175.
  34. Mashhadban, H., Kutanaei, S.S. and Sayarinejad, M.A. (2016), "Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network", Constr. Build. Mater., 119, 277-287. https://doi.org/10.1016/j.conbuildmat.2016.05.034.
  35. Massana, J., Reyes, E., Bernal, J., Leon, N. and Sanchez-Espinosa, E. (2018), "Influence of nano- and micro-silica additions on the durability of a high-performance self-compacting concrete", Constr. Build. Mater., 165, 93-103. https://doi.org/10.1016/j.conbuildmat.2017.12.100.
  36. Miladirad, K., Golafshani, E.M., Safehian, M. and Sarkar, A. (2021), "Modeling the mechanical properties of rubberized concrete using machine learning methods", Comput. Concrete, 28(6), 567-583. https://doi.org/10.12989/cac.2021.28.6.567.
  37. Mohammed, B.S., Achara, B.E., Nuruddin, M.F., Yaw, M. and Zulkefli, M.Z. (2017), "Properties of nano-silica-modified self-compacting engineered cementitious composites", J. Clean. Prod., 162, 1225-1238. https://doi.org/10.1016/j.jclepro.2017.06.137.
  38. Mohseni, E., Miyandehi, B.M., Yang, J. and Yazdi, M.A. (2015), "Single and combined effects of nano-SiO2, nano-Al2O3 and nano-TiO2 on the mechanical, rheological and durability properties of self-compacting mortar containing fly ash", Constr. Build. Mater., 84, 331-340. https://doi.org/10.1016/j.conbuildmat.2015.03.006.
  39. Nandhini, K. and Ponmalar, V. (2021), "Effect of blending micro and nano silica on the mechanical and durability properties of self-compacting concrete", Silicon, 13, 687-695. https://doi.org/10.1007/s12633-020-00475-5.
  40. Nasr, D., Behforouz, B., Borujeni, P.R., Borujeni, S.A. and Zehtab, B. (2019), "Effect of nano-silica on mechanical properties and durability of self-compacting mortar containing natural zeolite: Experimental investigations and artificial neural network modeling", Constr. Build. Mater., 229, 116888. https://doi.org/10.1016/j.conbuildmat.2019.116888.
  41. Nguyen, H. and Hoang, N.D. (2022), "Computer vision-based classification of concrete spall severity using metaheuristic-optimized extreme gradient boosting machine and deep convolutional neural network", Autom. Constr., 140, 104371. https://doi.org/10.1016/j.autcon.2022.104371.
  42. Ozcan, G., Kocak, Y. and Gulbandilar, E. (2017), "Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models", Comput. Concrete, 19(3), 275-282. https://doi.org/10.12989/cac.2017.19.3.275.
  43. Rahim, N.I., Mohammed, B.S., Abdulkadir, I. and Dahim, M. (2022), "Effect of crumb rubber, fly ash, and nanosilica on the properties of self-compacting concrete using response surface methodology", Mater. (Basel), 15, 1501. https://doi.org/10.3390/ma15041501.
  44. Ramkumar, K.B., Kannan Rajkumar, P.R., Noor Ahmmad, S. and Jegan, M. (2020), "A review on performance of self-compacting concrete - use of mineral admixtures and steel fibres with artificial neural network application", Constr. Build. Mater., 261, 120215. https://doi.org/10.1016/j.conbuildmat.2020.120215.
  45. Rao, S., Silva, P. and de Brito, J. (2015), "Experimental study of the mechanical properties and durability of self-compacting mortars with nano materials (SiO2 and TiO2)", Constr. Build. Mater., 96, 508-517. https://doi.org/10.1016/j.conbuildmat.2015.08.049.
  46. Ren, Q., Li, M. and Shen, Y. (2021), "A new interval prediction method for displacement behavior of concrete dams based on gradient boosted quantile regression", Struct. Health Monit., 29(1), e2859. https://doi.org/10.1002/stc.2859.
  47. Sadeghi Nik, A. and Lotfi Omran, O. (2013), "Estimation of compressive strength of self-compacted concrete with fibers consisting nano-SiO2 using ultrasonic pulse velocity", Constr. Build. Mater., 44, 654-662. https://doi.org/10.1016/j.conbuildmat.2013.03.082.
  48. 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.
  49. Zhang, W., Lee, D., Ju, H. and Wang, L. (2022), "Identification of shear transfer mechanisms in RC beams by using machine-learning technique", Comput. Concrete, 30(1), 43-74. https://doi.org/10.12989/cac.2022.30.1.043.
  50. Ziolkowski, P. and Niedostatkiewicz, M. (2019), "Machine learning techniques in concrete mix design", Mater. (Basel), 12(8), 1256. https://doi.org/10.3390/ma12081256.