Prognosis of flow of fly ash and blast furnace slag-based concrete: leveraging advanced machine learning algorithms-Reference-Cited by-同舟云学术

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Prognosis of flow of fly ash and blast furnace slag-based concrete: leveraging advanced machine learning algorithms

Published:2023-11-08 Issue: Volume: Page:
ISSN:1563-0854
Container-title:Asian Journal of Civil Engineering
language:en
Short-container-title:Asian J Civ Eng

Author:

Kumar Rahul,Rathore Ayush,Singh Rajwinder,Mir Ajaz Ahmad,Tipu Rupesh Kumar,Patel Mahesh

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42107-023-00922-9.pdf

Reference46 articles.

1. Ahmad, M., Hu, J.-L., Ahmad, F., et al. (2021). Supervised learning methods for modeling concrete compressive strength prediction at high temperature. Materials, 14, 1983. https://doi.org/10.3390/ma14081983

2. Alhamzawi, R., & Ali, H. T. M. (2018). The Bayesian adaptive lasso regression. Mathematical Biosciences, 303, 75–82. https://doi.org/10.1016/J.MBS.2018.06.004

3. Azamathulla, G. A. A., Zakaria, N. A., & Guven, A. (2010). Genetic programming to predict bridge pier scour. Journal of Hydraulic Engineering, 136, 165–169. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000133

4. Azamathulla, H., Haghiabi, A., & Parsaie, A. (2016). Prediction of side weir discharge coefficient by support vector machine technique. Water Science and Technology Water Supply, 16, 1002–1016. https://doi.org/10.2166/ws.2016.014

5. Azamathulla, H. M., & Jarrett, R. D. (2013). Use of gene-expression programming to estimate Manning’s roughness coefficient for high gradient streams. Water Resources Management, 27, 715–729. https://doi.org/10.1007/s11269-012-0211-1

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