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Computers and Concrete
  Volume 27, Number 4, April 2021 , pages 319-332
DOI: https://doi.org/10.12989/cac.2021.27.4.319
 


Predictive modeling of the compressive strength of bacteria-incorporated geopolymer concrete using a gene expression programming approach
Iman Mansouri, Mobin Ostovari, Paul O. Awoyera and Jong Wan Hu

 
Abstract
    The performance of gene expression programming (GEP) in predicting the compressive strength of bacteriaincorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28oC) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.
 
Key Words
    green concrete; geopolymer concrete; soft computing; gene expression programming; analytical modeling
 
Address
Iman Mansouri: Department of Civil Engineering, Birjand University of Technology, Birjand, Iran
Mobin Ostovari: Department of Civil Engineering, Birjand University of Technology, Birjand, Iran
Paul O. Awoyera: Department of Civil Engineering, Covenant University, Nigeria
Jong Wan Hu: Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, South Korea;Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, South Korea
 

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