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Computers and Concrete Volume 33, Number 1, January 2024 , pages 77-90 DOI: https://doi.org/10.12989/cac.2024.33.1.077 |
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An advanced machine learning technique to predict compressive strength of green concrete incorporating waste foundry sand |
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Danial Jahed Armaghani, Haleh Rasekh and Panagiotis G. Asteris
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| Abstract | ||
| Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended. | ||
| Key Words | ||
| compressive strength of concrete; extreme gradient boosting; green concrete; optimisation algorithms; waste foundry sand | ||
| Address | ||
| Danial Jahed Armaghani and Haleh Rasekh: School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR, 14121 Athens, Greece | ||