Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Ahmad, Ayaz; Ahmad, Waqas; Chaiyasarn, Krisada; Ostrowski, Krzysztof; Aslam, Fahid; Zajdel, Paulina; Joyklad, Panuwat

doi:10.3390/polym13193389

Cited by 89 publications

(38 citation statements)

References 77 publications

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“…On the other hand, for ensemble ML techniques, submodels were generated to leverage the weak learner that could be optimized and trained with data for achieving a higher value of R 2 . Other researchers have also observed that AdaBoost, Bagging, and RF models are more accurate in predicting outcomes than individual machine learning techniques [45,50,[59][60][61]. Feng, et al [45] observed that an AdaBoost model outperformed individual models, including an artificial neural network (ANN) and a support vector machine (SVM), in terms of R 2 and error values.…”

Section: Comparison Of Machine Learning Modelsmentioning

confidence: 99%

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

et al. 2022

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Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction. Various blends of concrete having 40 mixes were made as partial substitutes for waste marble dust. The ultrasonic pulse velocity of waste marble dust concrete (WMDC) was compared to a control mix without marble dust. Additionally, this research used standalone (multiple-layer perceptron neural network) and supervised machine learning methods (Bagging, AdaBoost, and Random Forest) to predict the ultrasonic pulse velocity of waste marble dust concrete. The models’ performances were assessed using R2, RMSE, and MAE. Then, the models’ performances were validated using k-fold cross-validation. Furthermore, the effect of raw ingredients and their interactions using SHAP analysis was evaluated. The Random Forest model, with an R2 of 0.98, outperforms the MLPNN, Bagging, and AdaBoost models. Compared to all the other models (individual and ensemble), the Random Forest model with greater R2 and lower error (RMSE, MAE) has a superior performance. SHAP analysis revealed that marble dust content has a positive and direct influence on and relationship to the ultrasonic pulse velocity of concrete. Using machine learning to forecast concrete properties saves time, resources, and effort for scholars in the engineering sector.

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Section: Comparison Of Machine Learning Modelsmentioning

confidence: 99%

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

et al. 2022

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“…The accuracy level between the real and anticipated output was observed from the coefficient correlation (R 2 ) value, and a higher value gives the impressive performance of the employed model. The AdaBoost technique was employed for optimization via producing 20 sub-models to obtain a higher R 2 value [26]. The application of these ML algorithms is to compare the predictive evaluation of each approach.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Shang

Ahmad

et al. 2022

Materials

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Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

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“…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. Ahmad, et al [ 41 ] studied the boosting and AdaBoost ML approaches to predict the compressive strength of a high-calcium fly-ash-based geopolymer. Bagging indicated the best results.…”

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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Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

Cited by 89 publications

References 77 publications

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

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

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