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Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
| Content Provider | MDPI |
|---|---|
| Author | Amin, Muhammad Nasir Khan, Kaffayatullah Ahmad, Waqas Javed, Muhammad Faisal Qureshi, Hisham Jahangir Saleem, Muhammad Umair Qadir, Muhammad Ghulam Faraz, Muhammad Iftikhar |
| Copyright Year | 2022 |
| Description | The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, $Na_{2}SiO_{3}$/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient $(R^{2}$). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts. |
| Starting Page | 2128 |
| e-ISSN | 20734360 |
| DOI | 10.3390/polym14102128 |
| Journal | Polymers |
| Issue Number | 10 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-23 |
| Access Restriction | Open |
| Subject Keyword | Polymers Machine Learning Geopolymer Concrete Artificial Intelligence Prediction Models Compressive Strength |
| Content Type | Text |
| Resource Type | Article |
