Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions
Abstract
:1. Introduction
2. Research Significance
3. Brief Methodology for Prediction
3.1. SVM Bagging and AdaBoost Model
3.1.1. Determination of the Training Dataset
3.1.2. Choosing Kernel Function
3.2. Gradient Boosting and XG Boost Models
3.3. Model Interpretability with SHAP
4. Data Collection
Dataset Description
5. Results and Discussion
5.1. SVM-AdaBoost Models
5.2. SVM-Bagging Models
5.3. DT-Gradient Boosting Models
5.4. DT-Extreme Gradient Boosting (XG-Boost) Models
6. Model Comparison and Performance Evaluation
7. Enhanced ML Models Explain Ability
8. Conclusions
- An optimised model was developed to forecast the RCAC compressive property with considerably enhanced accuracy among all ensemble machine learning models.
- The suitability of the developed models to be used in the pre-design of RCAC is highly favourable due to the higher level of model accuracy to forecast the compressive strength of RCAC.
- The DT-Gradient Boosting model outperformed all other models in terms of prediction with greater R2 and lower error levels. SVM-AdaBoost, SVM-Bagging, DT-Gradient Boosting, and DT-XG Boost models had R2 values of 0.94, 0.95, 0.98, and 0.94, respectively. However, the ensemble model results of the DT-Gradient Boosting model, followed by SVM-Bagging, are also acceptable.
- The mean square error values of the SVM-AdaBoost, SVM-Bagging, DT-Gradient Boosting, and DT-XG Boost models were 7.7, 7.4, 4.7, and 7.7 MPa, respectively. DT-Gradient Boosting obtained predictions with higher precision and a low error rate for the compressive strength of RCAC.
- The k-fold cross-validation technique and statistical analysis revealed satisfactory DT-Gradient Boosting and SVM-Bagging outcomes. These tests also showed that the DT-Gradient Boosting model outperformed the SVM-AdaBoost, SVM-Bagging, and DT- XG Boost models.
- Upon comparison, it is concluded that the DT-Gradient Boosting model with a regression coefficient (R2) closer to 1 is preferable to all other models.
- The water content has the highest impact on compressive strength prediction, followed by cement and recycled coarse aggregates, as shown by the importance of features. After these features, natural aggregates and the RA density have comparable influence.
- The feature interaction plot using SHAP analysis shows that with increasing water and recycled coarse aggregates content in RCAC, the compressive strength decreases.
- The consumption of natural resources and energy to process these materials for the manufacturing of concrete leads to environmental degradation with CO2 emissions. Further, landfill contamination due to construction and demolition waste also impacts the environment negatively. Hence, incorporating RA in concrete can significantly play a role in its environment-friendly development by reducing landfill pollution.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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SR # | Parameters | Mean | Standard Error | Median | Mode | Range | Minimum | Maximum |
---|---|---|---|---|---|---|---|---|
Input | ||||||||
1 | Water (kg/m3) | 185 | 1.50 | 180 | 220 | 153 | 118 | 271 |
2 | Cement (kg/m3) | 385 | 4.59 | 380 | 380 | 442 | 158 | 600 |
3 | Sand (kg/m3) | 703 | 10.37 | 706 | 693 | 1010 | 0 | 1010 |
4 | Natural Aggregates (kg/m3) | 412 | 20.96 | 489 | 0 | 1448 | 0 | 1448 |
5 | RA (kg/m3) | 629 | 20.26 | 543 | 138 | 1726 | 52 | 1778 |
6 | SP (kg/m3) | 1.35 | 0.12 | 0 | 0 | 8 | 0 | 8 |
7 | RA Max. Size (mm) | 19.90 | 0.23 | 20 | 20 | 22 | 10 | 32 |
8 | RA Density (kg/m3) | 2373 | 6.50 | 2370 | 2320 | 651 | 2010 | 2661 |
9 | RA WA (%) | 5.25 | 0.10 | 5 | 5 | 9 | 2 | 11 |
Output | ||||||||
10 | CS (MPa) | 43.56 | 0.71 | 43.3 | 41 | 65 | 13 | 78 |
Techniques | MAE (MPa) | RMSE (MPa) | R2 |
---|---|---|---|
SVM-AdaBoost | 7.7 | 9.5 | 0.94 |
SVM-Bagging | 7.4 | 9.3 | 0.95 |
DT-Gradient Boosting | 4.7 | 5.9 | 0.98 |
DT-XG Boost | 7.7 | 10.5 | 0.94 |
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Amin, M.N.; Ahmad, W.; Khan, K.; Ahmad, A.; Nazar, S.; Alabdullah, A.A. Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions. Materials 2022, 15, 5207. https://doi.org/10.3390/ma15155207
Amin MN, Ahmad W, Khan K, Ahmad A, Nazar S, Alabdullah AA. Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions. Materials. 2022; 15(15):5207. https://doi.org/10.3390/ma15155207
Chicago/Turabian StyleAmin, Muhammad Nasir, Waqas Ahmad, Kaffayatullah Khan, Ayaz Ahmad, Sohaib Nazar, and Anas Abdulalim Alabdullah. 2022. "Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions" Materials 15, no. 15: 5207. https://doi.org/10.3390/ma15155207