Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites
Abstract
:1. Introduction
2. Methods
2.1. Description of Data
2.2. Machine Learning Algorithms Employed
2.2.1. Decision Tree
2.2.2. AdaBoost
2.2.3. Random Forest
3. Models Results
3.1. Decision Tree Model
3.2. AdaBoost Model
3.3. Random Forest Model
4. Validation of Models
5. Sensitivity Analysis
6. Discussions
6.1. Comparison of Machine Learning Models
6.2. Comparison of Experimental and Predicted Results
7. Conclusions
- Ensemble ML approaches (AdaBoost and RF) performed better than the individual ML technique (DT) at predicting the CS of GPCs, with the AdaBoost and RF models performing with a similar degree of precision. The correlation coefficients (R2) for the AdaBoost, RF and DT models were 0.90, 0.90, and 0.83, respectively.
- Statistical checks and k-fold analysis verified the model’s performance. Furthermore, these checks also confirmed the comparable accuracy of the AdaBoost and RF models. The lower deviation (MAE, MAPE, and RMSE) of the predicted results and higher R2 values of the ensembled models validated their higher precision.
- The comparison of the experimental and predicted results further validated the higher accuracy of AdaBoost and RF models due to less deviation of the predicted results than the experimental results. On the other hand, the deviation of the DT model’s results was higher than the AdaBoost and RF models and is less recommended for estimating the CS of GPCs.
- Sensitivity analysis revealed that fly ash, ground granulated blast furnace slag, and NaOH molarity have a greater influence on the model’s outcome and account for 26.37%, 14.74%, and 13.12% of the contribution, respectively. However, NaOH, water/solids ratio, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, and Na2SiO3 contributed 11.60%, 9.52%, 7.53%, 6.48%, 5.84%, and 4.80%, respectively, to the prediction of the outcome.
- This type of research will aid the construction sector by enabling the development of quick and cost-effective methods for predicting material strength. Additionally, by promoting eco-friendly construction using these strategies, the acceptance and use of GPC in construction will be expedited.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Water/Solids Ratio | NaOH Molarity | Gravel 4/10 mm (kg/m3) | Gravel 10/20 mm (kg/m3) | NaOH (kg/m3) | Na2SiO3 (kg/m3) | Fly Ash (kg/m3) | GGBS (kg/m3) | Fine Aggregate (kg/m3) |
---|---|---|---|---|---|---|---|---|---|
Minimum | 0 | 1 | 0 | 0 | 3.5 | 18 | 0 | 0 | 459 |
Maximum | 0.63 | 20 | 1293.4 | 1298 | 147 | 342 | 523 | 450 | 1360 |
Range | 0.63 | 19 | 1293.4 | 1298 | 143.5 | 324 | 523 | 450 | 901 |
Median | 0.34 | 9.2 | 208 | 789 | 56 | 108 | 120 | 300 | 728 |
Mode | 0.53 | 10 | 0 | 0 | 64 | 108 | 0 | 0 | 651 |
Mean | 0.34 | 8.14 | 288.39 | 737.37 | 53.74 | 111.66 | 174.34 | 225.15 | 729.88 |
Standard Error | 0.01 | 0.24 | 19.54 | 18.82 | 1.67 | 2.53 | 8.82 | 8.52 | 6.87 |
Standard Deviation | 0.11 | 4.56 | 372.31 | 358.55 | 31.91 | 48.16 | 167.95 | 162.27 | 130.97 |
Sum | 124.8 | 2955.1 | 104,684.3 | 267,664.9 | 19,508.8 | 40,532.7 | 63,286.0 | 81,728.1 | 264,947.8 |
Model | MAE (MPa) | MAPE (%) | RMSE (MPa) |
---|---|---|---|
Decision tree | 7.016 | 16.020 | 10.432 |
AdaBoost | 5.199 | 12.302 | 7.467 |
Random forest | 5.325 | 12.420 | 7.602 |
K-Fold | Decision Tree | AdaBoost | Random Forest | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MAE | MAPE | RMSE | R2 | MAE | MAPE | RMSE | R2 | MAE | MAPE | RMSE | R2 | |
1 | 16.03 | 18.70 | 21.92 | 0.60 | 8.70 | 13.25 | 13.04 | 0.43 | 10.70 | 12.97 | 13.43 | 0.54 |
2 | 7.02 | 16.93 | 11.57 | 0.76 | 5.65 | 12.30 | 8.01 | 0.49 | 5.33 | 13.76 | 8.09 | 0.72 |
3 | 9.15 | 16.03 | 10.94 | 0.20 | 6.56 | 14.03 | 8.16 | 0.79 | 5.54 | 14.88 | 8.37 | 0.64 |
4 | 11.76 | 17.21 | 10.43 | 0.70 | 8.18 | 12.55 | 8.43 | 0.67 | 8.06 | 13.66 | 11.40 | 0.52 |
5 | 7.31 | 16.02 | 12.41 | 0.59 | 6.11 | 12.98 | 7.47 | 0.90 | 5.34 | 12.90 | 7.85 | 0.77 |
6 | 12.96 | 16.55 | 17.07 | 0.37 | 12.94 | 14.45 | 14.34 | 0.57 | 9.85 | 13.77 | 13.82 | 0.53 |
7 | 7.72 | 18.67 | 19.58 | 0.72 | 9.50 | 13.66 | 12.06 | 0.60 | 9.43 | 12.42 | 15.56 | 0.74 |
8 | 10.92 | 16.03 | 15.26 | 0.41 | 9.33 | 13.08 | 14.33 | 0.86 | 11.12 | 14.02 | 13.93 | 0.34 |
9 | 8.15 | 17.22 | 16.50 | 0.72 | 5.20 | 12.95 | 7.68 | 0.74 | 5.80 | 13.79 | 7.60 | 0.79 |
10 | 19.78 | 17.02 | 23.23 | 0.83 | 14.68 | 12.35 | 18.28 | 0.61 | 18.47 | 12.50 | 19.31 | 0.90 |
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Wang, Q.; Ahmad, W.; Ahmad, A.; Aslam, F.; Mohamed, A.; Vatin, N.I. Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites. Polymers 2022, 14, 1074. https://doi.org/10.3390/polym14061074
Wang Q, Ahmad W, Ahmad A, Aslam F, Mohamed A, Vatin NI. Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites. Polymers. 2022; 14(6):1074. https://doi.org/10.3390/polym14061074
Chicago/Turabian StyleWang, Qichen, Waqas Ahmad, Ayaz Ahmad, Fahid Aslam, Abdullah Mohamed, and Nikolai Ivanovich Vatin. 2022. "Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites" Polymers 14, no. 6: 1074. https://doi.org/10.3390/polym14061074