Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods

Sustainable concrete is gaining in popularity as a result of research into waste materials, such as recycled aggregate (RA). This strategy not only protects the environment, but also meets the demand for concrete materials. Using advanced artificial intelligence (AI) approaches, this study anticipat...

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Bibliographic Details
Published in:Materials 2022-06, Vol.15 (12), p.4296
Main Authors: Amin, Muhammad Nasir, Ahmad, Ayaz, Khan, Kaffayatullah, Ahmad, Waqas, Nazar, Sohaib, Faraz, Muhammad Iftikhar, Alabdullah, Anas Abdulalim
Format: Article
Language:English
Subjects:
Accuracy
Aggregates
Algorithms
Artificial intelligence
Artificial neural networks
Cement
Concrete
Concrete aggregates
Construction industry
Decision trees
Environmental protection
Machine learning
Monte Carlo simulation
Neural networks
Neurons
Parameter sensitivity
Recycled materials
Recycling
Root-mean-square errors
Sensitivity analysis
Statistical methods
Statistical tests
Support vector machines
Tensile strength
Waste materials
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Summary:Sustainable concrete is gaining in popularity as a result of research into waste materials, such as recycled aggregate (RA). This strategy not only protects the environment, but also meets the demand for concrete materials. Using advanced artificial intelligence (AI) approaches, this study anticipates the split tensile strength (STS) of concrete samples incorporating RA. Three machine-learning techniques, artificial neural network (ANN), decision tree (DT), and random forest (RF), were examined for the specified database. The results suggest that the RF model shows high precision compared with the DT and ANN models at predicting the STS of RA-based concrete. The high value of the coefficient of determination and the low error values of the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) provided significant evidence for the accuracy and precision of the RF model. Furthermore, statistical tests and the k-fold cross-validation technique were used to validate the models. The importance of the input parameters and their contribution levels was also investigated using sensitivity analysis and SHAP analysis.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma15124296