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Structural Engineering and Mechanics Volume 86, Number 2, April25 2023 , pages 181-195 DOI: https://doi.org/10.12989/sem.2023.86.2.181 |
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Development of an integrated machine learning model for rheological behaviours and compressive strength prediction of self-compacting concrete incorporating environmental-friendly materials |
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Pouryan Hadi, KhodaBandehLou Ashkan, Hamidi Peyman and Ashrafzadeh Fedra
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| Abstract | ||
| To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, Lbox ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice. | ||
| Key Words | ||
| compressive strength; gravitational search algorithm; machine learning; rheological properties; selfcompacting concrete | ||
| Address | ||
| Pouryan Hadi, KhodaBandehLou Ashkan, Hamidi Peyman and Ashrafzadeh Fedra: Department of Civil Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran | ||