Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization

References

• Aiyer, B. G., Kim, D., Karingattikkal, N., Samui, P., & Rao, P. R. (2014). Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. Korean Society of Civil Engineers Journal of Civil Engineering, 18, 1753–1758. https://doi.org/10.1007/s12205-014-0524-0
   Google Scholar
• Amiri, M., & Hatami, F. (2022). Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs). Construction and Building Materials, 325, 126839. https://doi.org/10.1016/j.conbuildmat.2022.126839
   Web of Science ®Google Scholar
• Asim, M., Rashid, A., & Ahmad, T. (2021). Scour modeling using deep neural networks based on hyperparameter optimization. ICT Express, 8, 357–362. https://doi.org/10.1016/j.icte.2021.09.012
   Web of Science ®Google Scholar
• Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P., & Pilakoutas, K. (2021). Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research, 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449
   Web of Science ®Google Scholar
• Bello, S. A., Oyedele, L., Olaitan, O. K., Olonade, K. A., Olajumoke, A. M., Ajayi, A., Akanbi, L., Akinade, O., Sanni, M. L., & Bello, A. L. (2022) A deep learning approach to concrete water-cement ratio prediction. Results in Materials, 15(April), 100300. https://doi.org/10.1016/j.rinma.2022.100300
   Google Scholar
• Chi, L., Wang, M., Liu, K., Lu, S., Kan, L., Xia, X., & Huang, C. (2023). Machine learning prediction of compressive strength of concrete with resistivity modification. Materials Today Communications, 36(June), 106470. https://doi.org/10.1016/j.mtcomm.2023.106470
   Google Scholar
• Chou, J. S., Tsai, C. F., Pham, A. D., & Lu, Y. H. (2014). Machine learning in concrete strength simulations: multi-nation data analytics. Constrion and Building Materials, 73(2014), 771–780. https://doi.org/10.1016/j.conbuildmat.2014.09.054
   Google Scholar
• Chou, J.-S., & Pham, A.-D. (2013). Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Construction and Building Materials, 49, 554–563. https://doi.org/10.1016/j.conbuildmat.2013.08.078
   Web of Science ®Google Scholar
• Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H., Wu, X. (2018). Compressive strength prediction of recycled concrete based on deep learning. Construction and Building Materials, 175, 562–569. https://doi.org/10.1016/j.conbuildmat.2018.04.169
   Web of Science ®Google Scholar
• Dhillon, M. S., Sharif, M., Madsen, H., & Jakobsen, F. (2023). Seasonal precipitation forecasting for water management in the Kosi Basin, India using large-scale climate predictors. Journal of Water and Climate Change, 14(6), 1868–1880. https://doi.org/10.2166/wcc.2023.479
   Web of Science ®Google Scholar
• Erdal, H. I. (2013). Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Engineering Applications Artificial Intelligence, 26(7), 1689–1697. https://doi.org/10.1016/j.engappai.2013.03.014
   Web of Science ®Google Scholar
• Erdal, H. I., Karakurt, O., & Namli, E. (2013). High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications Artificial Intelligence, 26(4), 1246–1254. https://doi.org/10.1016/j.engappai.2012.10.014
   Web of Science ®Google Scholar
• 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, 1–11. https://doi.org/10.1016/j.conbuildmat.2019.117000
   Web of Science ®Google Scholar
• Hema, C., & Marquez, F. P. G. (2023). Emotional speech recognition using CNN and deep learning techniques. Applied Acoustics, 211, 1–11. https://doi.org/10.1016/j.apacoust.2023.109492
   Web of Science ®Google Scholar
• Kaloop, M. R., Kumar, D., Samui, P., Hu, J. W., & Kim, D. (2020). Compressive strength prediction of high-performance concrete using gradient tree boosting machine. Construction and Building Materials, 264, 1–11. https://doi.org/10.1016/j.conbuildmat.2020.120198
   Web of Science ®Google Scholar
• Kaloop, M. R., Samui, P., Shafeek, M., & Hu, J. W. (2020). Estimating slump flow and compressive strength of self-compacting concrete using emotional neural networks. Appl. Sci., 10(23), 8543. https://doi.org/10.3390/app10238543
   Google Scholar
• Kaya, A. (2010). Artificial neural network study of observed pattern of scour depth around bridge piers. Computers and Geotechnics 37(3), 413–418. https://doi.org/10.1016/j.compgeo.2009.10.003
   Web of Science ®Google Scholar
• Khashman, A. P., & Akpinar, P. (2017). Non-destructive prediction of concrete compressive strength using neural networks. Procedia Computer Science, 108(2017), 2358–2362. https://doi.org/10.1016/j.procs.2017.05.039
   Google Scholar
• Khursheed, S., Jagan, P., Samui, P., & Kumar, S. (2021). Compressive strength prediction of fly ash concrete by using machine learning techniques. Innovative Infrastructure Solutions 6(3), 149. https://doi.org/10.1007/s41062-021-00506-z
   Web of Science ®Google Scholar
• Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).
   Google Scholar
• Kong, Q., He, C., Liao, L., Xu, J., & Yuan, C. (2023). Hyperparameter optimization for interfacial bond strength prediction between fiber-reinforced polymer and concrete. Structures, 51, 573–601. https://doi.org/10.1016/j.istruc.2023.03.082
   Web of Science ®Google Scholar
• Kumar, S., Goyal, M. K., Deshpande, V., & Agarwal, M. (2023). Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods. Ocean Engineering, 270, 1–15. https://doi.org/10.1016/j.oceaneng.2022.113611
   Web of Science ®Google Scholar
• Larrard, F., & Sedran, T. (2002). Mixture-proportioning of high-performance concrete. Cement and Concrete Research, 32(11), 1699–1704. https://doi.org/10.1016/S0008-8846(02)00861-X
   Web of Science ®Google Scholar
• Li, Q., & Song, Z. (2022). High-performance concrete strength prediction based on ensemble learning. Construction and Building Materials, 324, 1–18. https://doi.org/10.1016/j.conbuildmat.2022.126694
   Web of Science ®Google Scholar
• Lin, C. J., & Wu, N. J. (2021) An ANN model for predicting the compressive strength of concrete. Applied Sciences, 11(9), 1–13. https://doi.org/10.3390/app11093798
   Google Scholar
• Liu, K., Alam, M. S., Zhu, J., Zheng, J., & Chi, L. (2021). Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms. Construction and Building Materials, 301, 1–15. https://doi.org/10.1016/j.conbuildmat.2021.124382
   Web of Science ®Google Scholar
• Ly, H. B., Nguyen, T. A., Thi Mai, H. V., & Tran, V. Q. (2021). Development of deep neural network model to predict the compressive strength of rubber concrete. Construction and Building Materials, 301, 124081. https://doi.org/10.1016/j.conbuildmat.2021.124081
   Web of Science ®Google Scholar
• Nguyen, H., Vu, T., Vo, T. P., & Thai, H. T. (2021) Efficient machine learning models for prediction of concrete strengths. Constrion and Building Materials, 10(266), 1–17. https://doi.org/10.1016/j.conbuildmat.2020.120950
   Google Scholar
• O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., & Invernizzi, L. (2019). Keras Tuner. Retrieved from https://github.com/keras-team/keras-tuner
   Google Scholar
• Omran, B. A., Chen, Q., & Jin, R. (2016). Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete. J. Computing in Civil Engineering., 30(6), 04016029.
   Web of Science ®Google Scholar
• Pandey, M., Zakwan, M., Sharma, P. K., & Ahmad, Z. (2018). Multiple linear regression and genetic algorithm approaches to predict temporal scour depth near circular pier in non-cohesive sediment. ISH Journal of Hydraulic Engineering, 26, 96–103. https://doi.org/10.1080/09715010.2018.1457455
   Google Scholar
• Pham, A. D., Hoang, N. D., & Nguyen, Q. T. (2015). Predicting compressive strength of high-performance concrete using metaheuristic-optimized least squares support vector regression. Journal of Computing in Civil Engineering, 30(3), 06015002.
   Web of Science ®Google Scholar
• Shishegaran, A., Varaee, H., Rabczuk, T., & Shishegaran, G. (2021). High correlated variables creator machine: Prediction of the compressive strength of concrete. Computers & Structures, 247(106479), 1–12. https://doi.org/10.1016/j.compstruc.2021.106479
   Google Scholar
• Stoński, M. (2010). A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks. Computers and Structures, 88(21), 1248–1253. https://doi.org/10.1016/j.compstruc.2010.07.003
   Google Scholar
• Tran, V. Q., Dang, V. Q., & Ho, L. S. (2022). Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach. Construction and Building Materials, 323, 1–20. https://doi.org/10.1016/j.conbuildmat.2022.126578
   Web of Science ®Google Scholar
• UCI (2017). Machine Learning Repository. Retrieved from Online dataset resources https://archive.ics.uci.edu/ml/datasets.html
   Google Scholar
• Wang, Q., Pan, C., Liang, Y., Gan, W., & Ho, J. (2023). Pumping lightweight aggregate concrete into high-rise buildings. Journal of Building Engineering, 80, 108069, https://doi.org/10.1016/j.jobe.2023.108069
   Web of Science ®Google Scholar
• Wang, Z., & Wu, B. (2023). A mix design method for self-compacting recycled aggregate concrete targeting slump-flow and compressive strength. Construction and Building Materials, 404, 133309. https://doi.org/10.1016/j.conbuildmat.2023.133309
   Web of Science ®Google Scholar
• Wei, X., Xiao, L., & Li, Z. (2012). Prediction of standard compressive strength of cement by the electrical resistivity measurement. Construction and Building Materials, 31, 341–346. https://doi.org/10.1016/j.conbuildmat.2011.12.111
   Web of Science ®Google Scholar
• Yeh, I.-C. (1998b). Modeling of strength of highperformance concrete using artificial neural networks. Cement and Concrete Research, 28(12), 1797–1808.
   Web of Science ®Google Scholar
• Yuan, Z., Wang, L., & 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
   Web of Science ®Google Scholar
• Zheng, H., Li, X., Sun, T., Huang, Z., & Xie, C. (2023). Multiaxial fatigue life prediction of metals considering loading paths by image recognition and machine learning. Engineering Failure Analysis, 143(Part B), 106851. https://doi.org/10.1016/j.engfailanal.2022.106851
   Google Scholar