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All Journal Andalasian International Journal of Applied Science, Engineering, and Technology
Ang'ang'o, Elvis
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Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering

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Prediction of the Residual Compressive Strength of Rice Husk Ash Concrete after Exposure to Elevated Temperatures Using XGBoost Machine Learning Algorithm Ang'ang'o, Elvis; Abuodha, Silvester; Mumenya, Siphila
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 4 No. 3 (2024): November 2024
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v4i3.187

Abstract

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 training and 25% for testing. Synthetic data was created using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). The model accuracy was checked using statistical scores: coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). SHAP values were used for feature importance analysis. Coding was done in Python using Jupyter Notebook. With the original data, the model produced RMSE, R2, and MAE test values of 3.351, 0.939, and 2.994, respectively, indicating excellent performance. The combined original and synthetic dataset gave RMSE, R2, and MAE values of 0.071, 0.941, and 0.053, respectively, signifying improved performance. The feature analysis identified higher temperature, unheated compressive strength, and water-cement ratio as the most significant factors in the XGBoost prediction. The exposure duration, alumina content, and iron oxide had minimal influence.
Prediction of the Residual Compressive Strength of Rice Husk Ash Concrete after Exposure to Elevated Temperatures Using XGBoost Machine Learning Algorithm Ang'ang'o, Elvis; Abuodha, Silvester; Mumenya, Siphila
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 4 No. 3 (2024): November 2024
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v4i3.187

Abstract

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 training and 25% for testing. Synthetic data was created using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). The model accuracy was checked using statistical scores: coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). SHAP values were used for feature importance analysis. Coding was done in Python using Jupyter Notebook. With the original data, the model produced RMSE, R2, and MAE test values of 3.351, 0.939, and 2.994, respectively, indicating excellent performance. The combined original and synthetic dataset gave RMSE, R2, and MAE values of 0.071, 0.941, and 0.053, respectively, signifying improved performance. The feature analysis identified higher temperature, unheated compressive strength, and water-cement ratio as the most significant factors in the XGBoost prediction. The exposure duration, alumina content, and iron oxide had minimal influence.
Co-Authors Abuodha, Silvester Mumenya, Siphila