Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Methodology
2.2. Decision Tree
- Start with the root node, which includes all of the cases.
- One of the predictors, , is subjected to a test at each of the tree’s internal nodes.
- Observations are placed into the tree right or left sub-region (branch), depending on how the test turns out.
- In order to make a prediction, keep going back to Step 3 until a terminal leaf or node is reached.
2.3. Random Forests (RF)
- For to :
- From the training data, draw a bootstrap sample with size N.
- The following steps should be repeated recursively for each terminal node of the tree, until the minimum node size is attained to grow a RF tree according to the bootstrapped data.
- From the total variables, choose variables randomly.
- Among the variables, choose the best one.
- Generate two subregions by splitting the node.
- Output the ensemble of trees, .
2.4. Tuning RF Hyperparameters Using GridSearchCV
2.5. Performance Metrics
3. Database Used
4. Model Result
4.1. Hyperparameter Optimization: GridSearchCV
4.2. Evaluation of RF Model
4.3. Comparison between RF with Linear Regression
4.4. Comparison between RF with Previously Developed Models
5. SHAP Analysis
6. Conclusions
- The suggested RF model showed a high coefficient of determination of 0.9757 on the test set, indicating that it is highly accurate in forecasting. Additionally, no overfitting was generated, as concluded by the extremely low RMSE values on the training and testing sets.
- The generated model capacity to predict outcomes was contrasted with the one generated by previously proposed models in [34] and [59], which were: multivariable regression model (MLSR), multi-gen genetic programming (MGGP), and multivariable regression (MVR). According to the statistical analysis, the suggested RF model outperformed the current white-box models regarding relative errors and determination coefficients.
- Shap analysis was used to demonstrate the implemented RF’s strong integrity and reliability.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Statistics | (PI) (%) | S (%) | FA(%) | (M) (mol/L) | (A/B) | (Na/Al) | (Si/Al) | UCS (MPa) |
---|---|---|---|---|---|---|---|---|
Standard deviation | 30.73 | 12.92 | 4.66 | 2.73 | 0.14 | 0.44 | 0.35 | 6.49 |
Mean | 38.83 | 15.90 | 2.12 | 12.42 | 0.62 | 1.17 | 1.70 | 5.77 |
Median | 14.07 | 16.00 | 0.00 | 12.00 | 0.65 | 1.18 | 1.49 | 2.91 |
Maximum | 88.46 | 50.00 | 20.00 | 15.00 | 0.85 | 1.98 | 2.49 | 24.26 |
Minimum | 14.07 | 0.00 | 0.00 | 4.00 | 0.45 | 0.24 | 1.49 | 0.00 |
Kurtosis | −1.28 | 0.30 | 4.97 | 2.57 | −1.03 | −0.62 | 0.36 | −0.47 |
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Zeini, H.A.; Al-Jeznawi, D.; Imran, H.; Bernardo, L.F.A.; Al-Khafaji, Z.; Ostrowski, K.A. Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil. Sustainability 2023, 15, 1408. https://doi.org/10.3390/su15021408
Zeini HA, Al-Jeznawi D, Imran H, Bernardo LFA, Al-Khafaji Z, Ostrowski KA. Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil. Sustainability. 2023; 15(2):1408. https://doi.org/10.3390/su15021408
Chicago/Turabian StyleZeini, Husein Ali, Duaa Al-Jeznawi, Hamza Imran, Luís Filipe Almeida Bernardo, Zainab Al-Khafaji, and Krzysztof Adam Ostrowski. 2023. "Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil" Sustainability 15, no. 2: 1408. https://doi.org/10.3390/su15021408
APA StyleZeini, H. A., Al-Jeznawi, D., Imran, H., Bernardo, L. F. A., Al-Khafaji, Z., & Ostrowski, K. A. (2023). Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil. Sustainability, 15(2), 1408. https://doi.org/10.3390/su15021408