Prediction of compressive strength of rice husk ash concrete through different machine learning processes

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Ammar Iqtidar - , COMSATS University Islamabad (Author)
  • Niaz Bahadur Khan - , National University of Sciences and Technology Pakistan (Author)
  • Sardar Kashif-ur-Rehman - , COMSATS University Islamabad (Author)
  • Muhmmad Faisal Javed - , COMSATS University Islamabad (Author)
  • Fahid Aslam - , Prince Sattam Bin Abdulaziz University (Author)
  • Rayed Alyousef - , Prince Sattam Bin Abdulaziz University (Author)
  • Hisham Alabduljabbar - , Prince Sattam Bin Abdulaziz University (Author)
  • Amir Mosavi - , TUD Dresden University of Technology, Óbuda University (Author)

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 languageEnglish
Article number352
JournalCrystals
Volume11
Issue number4
Publication statusPublished - 2021
Peer-reviewedYes

Keywords

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