Prediction of compressive strength of rice husk ash concrete through different machine learning processes
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.
Details
Original language | English |
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Article number | 352 |
Journal | Crystals |
Volume | 11 |
Issue number | 4 |
Publication status | Published - 2021 |
Peer-reviewed | Yes |
Keywords
ASJC Scopus subject areas
Keywords
- Artificial intelligence, Artificial neural networks, Data science, Eco-friendly concrete, Green concrete, Machine learning, Multiple linear regression, Rice husk ash, Sustainable concrete, Sustainable development