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Civil & Environmental Engineering

Machine learning prediction and optimization of compressive strength for blended concrete by applying ANN and genetic algorithm

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Article: 2376914 | Received 31 May 2024, Accepted 02 Jul 2024, Published online: 11 Jul 2024

References

  • Ahmad, A., Ahmad, W., Chaiyasarn, K., Ostrowski, K. A., Aslam, F., Zajdel, P., & Joyklad, P. (2021). Prediction of geopolymer concrete compressive strength using novel machine learning algorithms. Polymers, 13(19), 3389. https://doi.org/10.3390/polym13193389
  • Alavala, C. R. (2008). Fuzzy logic and neural networks: basic concepts and applications. New Age International.
  • Awolusi, T. F., Oke, O. L., Akinkurolere, O. O., Sojobi, A. O., & Aluko, O. G. (2019). Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete. Heliyon, 5(1), e01115. https://doi.org/10.1016/j.heliyon.2018.e01115
  • Beskopylny, A. N., Stel’makh, S. A., Shcherban, E. M., Mailyan, L. R., Meskhi, B., Razveeva, I., Chernil’nik, A., & Beskopylny, N. (2022). Concrete strength prediction using machine learning methods CatBoost, k-nearest neighbors, support vector regression. Applied Sciences, 12(21), 10864. https://doi.org/10.3390/app122110864
  • BIS. (1959). IS: 516-1959. Method of tests for strength of concrete. Bureau of Indian Standards.
  • BIS. (2019). IS: 10262-2019. Concrete mix proportioning guidelines (second revision). Bureau of Indian Standards.
  • CPC X software, neural power user guide. (2003). Available from: http://www.geocities.com/neural power, https://www2.southeastern.edu/, Academics/Faculty/pmcdowell/matlab_nnet_help.pdf.
  • Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200–1206. https://doi.org/10.1016/j.conbuildmat.2012.04.063
  • Ebrahimpour, A., Abd Rahman, R. N. Z. R., Ean Ch’ng, D. H., Basri, M., & Salleh, A. B. (2008). A modeling study by response surface methodology and artificial neural network on culture parameters optimization for thermostable lipase production from a newly isolated thermophilic Geobacillus sp. strain ARM. BMC Biotechnology, 8(1), 96. https://doi.org/10.1186/1472-6750-8-960
  • Farooq, F., Ahmed, W., Akbar, A., Aslam, F., & Alyousef, R. (2021). Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. Journal of Cleaner Production, 292, 126032. https://doi.org/10.1016/j.jclepro.2021.126032
  • Feng, D.-C., Liu, Z.-T., Wang, X.-D., Chen, Y., Chang, J.-Q., Wei, D.-F., & Jiang, Z.-M. (2020). Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Construction and Building Materials, 230, 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000
  • Golafshani, E. M., Behnood, A., & Arashpour, M. (2020). Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer. Construction and Building Materials, 232, 117266. https://doi.org/10.1016/j.conbuildmat.2019.117266
  • Jiao, H., Wang, Y., Li, L., Arif, K., Farooq, F., & Alaskar, A. (2023). A novel approach in forecasting compressive strength of concrete with carbon nanotubes as nanomaterials. Materials Today Communications, 35, 106335. https://doi.org/10.1016/j.mtcomm.2023.106335
  • Karim, R., Islam, M. H., Datta, S. D., & Kashem, A. (2024). Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses. Case Studies in Construction Materials, 20, e02828. https://doi.org/10.1016/j.cscm.2023.e02828
  • Kashem, A., Karim, R., Das, P., Datta, S. D., & Alharthai, M. (2024). Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses. Case Studies in Construction Materials, 20, e03030. https://doi.org/10.1016/j.cscm.2024.e03030
  • Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using Artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment, 5(2), 355–369. https://doi.org/10.1016/j.ijsbe.2016.09.003
  • Khashman, A., & Akpinar, P. (2017). Non-destructive prediction of concrete compressive strength using neural networks. Procedia Computer Science, 108, 2358–2362. https://doi.org/10.1016/j.procs.2017.05.039
  • Kioumarsi, M., Dabiri, H., Kandiri, A., & Farhangi, V. (2023). Compressive strength of concrete containing furnace blast slag: Optimized machine learning-based models. Cleaner Engineering and Technology, 13, 100604. https://doi.org/10.1016/j.clet.2023.100604
  • Kwak, S., Kim, J., Ding, H., Xu, X., Chen, R., Guo, J., & Fu, H. (2022). Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model. Journal of Materials Research and Technology, 18, 520–530. https://doi.org/10.1016/j.jmrt.2022.02.108
  • Li, D., Tang, Z., Kang, Q., Zhang, X., & Li, Y. (2023). Machine learning-based method for predicting compressive strength of concrete. Processes, 11(2), 390. https://doi.org/10.3390/pr11020390
  • Lin, C.-J., & Ju, W. N. (2021). An ANN model for predicting the compressive strength of concrete. Applied Sciences, 11(9), 3798. https://doi.org/10.3390/app11093798
  • Mai, H.-V. T., Nguyen, T.-A., Ly, H.-B., & Tran, V. Q. (2021). Investigation of ANN model containing one hidden layer for predicting compressive strength of concrete with blast-furnace slag and fly ash. Advances in Materials Science and Engineering, 2021, 1–17. https://doi.org/10.1155/2021/5540853
  • Mater, Y., Kamel, M., Karam, A., & Bakhoum, E. (2023). ANN-Python prediction model for the compressive strength of green concrete. Construction Innovation, 23(2), 340–359. https://doi.org/10.1108/CI-08-2021-0145
  • Muthupriya, P., Subramanian, K., & Vishnuram, B. G. (2011). Prediction of compressive strength anddurability of high performance concrete by artificial neural networks. International Journal of Optimization in Civil Engineering, 1, 189–e209.
  • Naderpour, H., Rafiean, A. H., & Fakharian, P. (2018). Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16, 213–219. https://doi.org/10.1016/j.jobe.2018.01.007
  • Özcan, F., Atiş, C. D., Karahan, O., Uncuoğlu, E., & Tanyildizi, H. (2009). Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Advances in Engineering Software, 40(9), 856–863. https://doi.org/10.1016/j.advengsoft.2009.01.005
  • Panneerselvam, K., Aravindan, S., & Noorul Haq, A. (2009). Hybrid of ANN with genetic algorithm for optimization of frictional vibration joining process of plastics. The International Journal of Advanced Manufacturing Technology, 42(7–8), 669–677. https://doi.org/10.1007/s00170-008-1641-z
  • Paudel, S., Pudasaini, A., Shrestha, R. K., & Kharel, E. (2023). Compressive strength of concrete material using machine learning techniques. Cleaner Engineering and Technology, 15, 100661. https://doi.org/10.1016/j.clet.2023.100661
  • Pereira, F. D., Fonseca, S. C., Oliveira, E. H. T., Oliveira, D. B. F., Cristea, A. I., & Carvalho, L. S. G. (2020). Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study. Revista Brasileira de Informática na Educação, 28, 723–748. http://br-ie.org/pub/index.php/rbie. https://doi.org/10.5753/rbie.2020.28.0.723
  • Pilkington, J. L., Preston, C., & Gomes, R. L. (2014). Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annual. Industrial Crops and Products, 58, 15–e24. https://doi.org/10.1016/j.indcrop.2014.03.016
  • Rathakrishnan, V., Bt. Beddu, S., & Ahmed, A. N. (2022). Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms. Scientific Reports, 12(1), 9539. https://doi.org/10.1038/s41598-022-12890-2
  • Shang, M., Li, H., Ahmad, A., Ahmad, W., Ostrowski, K. A., Aslam, F., Joyklad, P., & Majka, T. M. (2022). Predicting the mechanical properties of RCA-based concrete using supervised machine learning algorithms. Materials, 15(2), 647. https://doi.org/10.3390/ma15020647
  • Sobhani, J., Najimi, M., Pourkhorshidi, A. R., & Parhizkar, T. (2010). Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Construction and Building Materials, 24(5), 709–718. https://doi.org/10.1016/j.conbuildmat.2009.10.037
  • Song, Y., Zhao, J., Ostrowski, K. A., Javed, M. F., Ahmad, A., Khan, M. I., Aslam, F., & Kinasz, R. (2021). Prediction of compressive strength of fly-ash-based concrete using ensemble and non-ensemble supervised machine-learning approaches. Applied Sciences, 12(1), 361. https://doi.org/10.3390/app12010361
  • Thai, H.-T. (2022). Machine learning for structural engineering: A state-of-the-art review. Structures, 38, 448–491. https://doi.org/10.1016/j.istruc.2022.02.003
  • Turkey, F. A., Beddu, S. B., Ahmed, A. N., & Al-Hubboubi, S. (2022). Concrete compressive strength prediction using machine learning algorithms. Research Square. https://doi.org/10.21203/rs.3.rs-1665395/v1
  • Yuan, Z., Wang, L.-N., & Ji, X. (2014). Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS. Advances in Engineering Software, 67, 156–163. https://doi.org/10.1016/j.advengsoft.2013.09.004