Prediction of the Residual Compressive Strength of Rice Husk Ash Concrete after Exposure to Elevated Temperatures Using XGBoost Machine Learning Algorithm
The study aimed to assess the applicability of XGBoost in determining the residual compressive strength of rice husk ash concrete exposed to elevated temperature, reducing the need for costly, time-consuming laboratory experiments. Data was collected from the available literature, with 75% used for...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Universitas Andalas
2024-11-01
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| Series: | Andalasian International Journal of Applied Science, Engineering, and Technology |
| Online Access: | https://aijaset.lppm.unand.ac.id/index.php/aijaset/article/view/187 |
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