Smart Structures and Systems

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Machine learning and RSM models for prediction of compressive strength of smart bio-concrete

  • Algaifi, Hassan Amer (Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia) ;
  • Bakar, Suhaimi Abu (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia) ;
  • Alyousef, Rayed (Department of Civil Engineering, College of Engineering, Prince Sattam bin Abdulaziz University) ;
  • Sam, Abdul Rahman Mohd. (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia) ;
  • Alqarni, Ali S. (Department of Civil Engineering, College of Engineering, King Saud University) ;
  • Ibrahim, M.H. Wan (Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia) ;
  • Shahidan, Shahiron (Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia) ;
  • Ibrahim, Mohammed (Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals) ;
  • Salami, Babatunde Abiodun (Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals)
  • Received : 2021.01.14
  • Accepted : 2021.06.01
  • Published : 2021.10.25
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Abstract

In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, R2, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.

Keywords

Acknowledgement

The authors acknowledge full gratitude to Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education through fundamental research grant scheme VOT. NO. FRGS /1/2018/TK01/UTHM/02/3. In addition, this research activity was also supported and funded by Malaysian Technical University Network (MTUN) Grant Vot K122 and Industry Grant PLUS BERHAD (M007). Indeed, the authors would like to thank their support and cooperation in this research. Moreover, the authors extend their thanks to Shimadzu Corporation, Alex Corporation (M) Sdn Bhd (KS Wong) and Volume Graphic Software for their assistance and collaboration during the analysis of the X-Ray CT imaging using Shimadzu Inspection System.

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