@Article{fdmp.2023.029545,
AUTHOR = {Shanqing Shao, Aimin Gong, Ran Wang, Xiaoshuang Chen, Jing Xu, Fulai Wang, Feipeng Liu},
TITLE = {A Machine-Learning Approach for the Prediction of Fly-Ash Concrete Strength},
JOURNAL = {Fluid Dynamics \& Materials Processing},
VOLUME = {19},
YEAR = {2023},
NUMBER = {12},
PAGES = {3007--3019},
URL = {http://www.techscience.com/fdmp/v19n12/54402},
ISSN = {1555-2578},
ABSTRACT = {The composite exciter and the CaO to Na<sub>2</sub>SO<sub>4</sub> dosing ratios are known to have a strong impact on the mechanical
strength of fly-ash concrete. In the present study a hybrid approach relying on experiments and a machine-learning technique has been used to tackle this problem. The tests have shown that the optimal admixture of CaO and
Na<sub>2</sub>SO<sub>4</sub> alone is 8%. The best 3D mechanical strength of fly-ash concrete is achieved at 8% of the compound
activator; If the 28-day mechanical strength is considered, then, the best performances are obtained at 4% of
the compound activator. Moreover, the 3D mechanical strength of fly-ash concrete is better when the dosing ratio
of CaO to Na<sub>2</sub>SO<sub>4</sub> in the compound activator is 1:1; the maximum strength of fly-ash concrete at 28-day can be
achieved for a 1:1 ratio of CaO to Na<sub>2</sub>SO<sub>4</sub> by considering a 4% compound activator. In this case, the compressive
and flexural strengths are 260 MPa and 53.6 MPa, respectively; the mechanical strength of fly-ash concrete at
28-day can be improved by a 4:1 ratio of CaO to Na<sub>2</sub>SO<sub>4</sub> by considering 8% and 12% compound excitants. It
is shown that the predictions based on the aforementioned machine-learning approach are accurate and reliable.},
DOI = {10.32604/fdmp.2023.029545}
}