Computers and Concrete

  • 제32권6호
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  • Pages.607-613
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  • 2023
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Comparative studies of different machine learning algorithms in predicting the compressive strength of geopolymer concrete

  • Sagar Paruthi (Department of Civil Engineering, Jamia Millia Islamia) ;
  • Ibadur Rahman (Department of Civil Engineering, Jamia Millia Islamia) ;
  • Asif Husain (Department of Civil Engineering, Jamia Millia Islamia)
  • 투고 : 2023.06.17
  • 심사 : 2023.08.09
  • 발행 : 2023.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

키워드

참고문헌

  1. Ahmad, A., Ahmad, W., Chaiyasarn, K., Ostrowski, K.A., Aslam, F., Zajdel, P. and Joyklad, P. (2021), "Prediction of geopolymer concrete compressive strength using novel machine learning algorithms", Polym., 13, 3389. https://doi.org/10.3390/polym13193389.
  2. Al-Gburi, S.N.A., Akpinar, P. and Helwan, A. (2022), "Machine learning in concrete's strength prediction", Comput. Concrere, 29, 433-444. https://doi.org/10.12989/cac.2022.29.6.433.
  3. 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.
  4. Ben Chaabene, W., 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.
  5. Cao, R., Fang, Z., Jin, M. and Shang, Y. (2022), "Application of machine learning approaches to predict the strength property of geopolymer concrete", Mater., 15, 2400. https://doi.org/10.3390/ma15072400.
  6. Chou, J.S., Tsai, C.F., Pham, A.D. and Lu, Y.H. (2014), "Machine learning in concrete strength simulations: Multi-nation data analytics", Constr. Build. Mater., 73, 771-780. https://doi.org/10.1016/j.conbuildmat.2014.09.054.
  7. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21, 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  8. Feng, D.C., Liu, Z.T., Wang, X.D., Chen, Y., Chang, J.Q., Wei, D.F. and Jiang, Z.M. (2020), "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach", Constr. Build. Mater., 230, 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000.
  9. 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, 33-42. https://doi.org/10.12989/cac.2022.30.1.033.
  10. Geurts, P., Ernst, D. and Wehenkel, L. (2006), "Extremely randomized trees", Mach. Learn., 63, 3-42. https://doi.org/10.1007/s10994-006-6226-1.
  11. Khan, K., Ahmad, W., Amin, M.N. and Ahmad, A. (2022), "A systematic review of the research development on the application of machine learning for concrete", Mater., 15, 4512. https://doi.org/10.3390/ma15134512.
  12. Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W.V, Dabo, I. and Radlinska, A. (2022), "Machine learning in concrete science: Applications, challenges, and best practices", Comput. Mater., 8, 127. https://doi.org/10.1038/s41524-022-00810-x.
  13. 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, 567-583. https://doi.org/10.12989/cac.2021.28.6.567.
  14. Nazari, A. and Sanjayan, J.G. (2015), "Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine", Ceram. Int., 41, 12164-12177. https://doi.org/10.1016/j.ceramint.2015.06.037.
  15. Nguyen, K.T., Nguyen, Q.D., Le, T.A., Shin, J. and Lee, K. (2020), "Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches", Constr. Build. Mater., 247, 118581. https://doi.org/10.1016/j.conbuildmat.2020.118581.
  16. 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, 9, 275-282. https://doi.org/10.12989/cac.2017.19.3.275.
  17. Shahmansouri, A.A., Akbarzadeh Bengar, H. and Ghanbari, S. (2020), "Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method", J. Build. Eng., 31, 101326. https://doi.org/10.1016/j.jobe.2020.101326.
  18. Unlu, R. (2020), "An assessment of machine learning models for slump flow and examining redundant features", Comput. Concrete, 25, 565-574. https://doi.org/https://doi.org/10.12989/cac.2020.25.6.565.
  19. Young, B.A., Hall, A., Pilon, L., Gupta, P. and Sant, G. (2019), "Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods", Cement Concrete Res., 115, 379-388. https://doi.org/10.1016/j.cemconres.2018.09.006.