Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang, Qichen; Ahmad, Waqas; Ahmad, Ayaz; Aslam, Fahid; Mohamed, Abdullah; Vatin, Nikolai

doi:10.3390/polym14061074

Cited by 58 publications

(22 citation statements)

References 57 publications

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“…It was noted that 61% of the error data was below 0.5 MPa, 30% was from 0.5 to 1 MPa, and only 8% was higher than 1 MPa. Previous studies also reported the better performance of the random forest model in forecasting the various properties of different materials in terms of superior R 2 and lower error values [47,52,53]. This analysis revealed a higher accuracy of the random forest model with respect to the AdaBoost models.…”

Section: Random Forest Resultssupporting

confidence: 55%

“…It was noted that 60% of the error data was below 0.5 MPa, 27% ranged from 0.5 to 1 MPa, and only 13% was higher than 1 MPa. Wang, et al [47] reported that the AdaBoost machine learning approaches predicted the better compressive strength of geopolymer composites. Zhu, et al [54] used the machine learning to forecast the splitting-tensile strength (STS) of the concrete containing recycled aggregate (RA) and revealed that the precision level of the bagging model was better.…”

Section: Bagging Resultsmentioning

confidence: 99%

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Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

et al. 2022

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Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model’s performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Then, the model’s performance was validated using k-fold cross-validation. The random forest model, with an R2 of 0.96, outperformed the AdaBoost models. The random forest models with greater R2 and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.

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Section: Random Forest Resultssupporting

confidence: 55%

Section: Bagging Resultsmentioning

confidence: 99%

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

et al. 2022

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“…The error and R 2 values for the compressive strength of UHSC for XGBoost are more accurate than the bagging model. Wang, et al [ 33 ] reported that the AdaBoost machine learning approaches predicted the best compressive strength of geopolymer composites. Zhu, et al [ 40 ] used machine learning to forecast the splitting tensile strength (STS) of concrete containing recycled aggregate (RA) and revealed that the precision level of the bagging model was better.…”

Section: Resultsmentioning

confidence: 99%

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

et al. 2022

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In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

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“…Nine groups will be utilized for training the models and one will be used for validation. The lower error values (MAE and RMSE) and the higher R 2 values suggest the higher precision of a model [ 69 ]. Moreover, the process must be repeated 10 times to obtain a suitable decision.…”

Section: Model’s Validationmentioning

confidence: 99%

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

et al. 2022

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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, Na2SiO3/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 (R2). 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.

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Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Cited by 58 publications

References 57 publications

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

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