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Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms

  • Zhu, Yirong (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Huang, Lihua (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Zhang, Zhijun (Southwest China Architectural Design and Research Institute Corp. Ltd) ;
  • Bayrami, Behzad (Department of Civil Engineering, Moghadas Ardabili Institute of Higher Education)
  • Received : 2021.12.13
  • Accepted : 2022.08.01
  • Published : 2022.08.10
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Abstract

Recycling concrete construction waste is an encouraging step toward green and sustainable building. A lot of research has been done on recycled aggregate concretes (RACs), but not nearly as much has been done on concrete made with recycled aggregate. Recycled aggregate concrete, on the other hand, has been found to have a lower mechanical productivity compared to conventional one. Accurately estimating the mechanical behavior of the concrete samples is a most important scientific topic in civil, structural, and construction engineering. This may prevent the need for excess time and effort and lead to economic considerations because experimental studies are often time-consuming, costly, and troublous. This study presents a comprehensive data-mining-based model for predicting the splitting tensile strength of recycled aggregate concrete modified with glass fiber and silica fume. For this purpose, first, 168 splitting tensile strength tests under different conditions have been performed in the laboratory, then based on the different conditions of each experiment, some variables are considered as input parameters to predict the splitting tensile strength. Then, three hybrid models as GWO-RF, GWO-MLP, and GWO-SVR, were utilized for this purpose. The results showed that all developed GWO-based hybrid predicting models have good agreement with measured experimental results. Significantly, the GWO-RF model has the best accuracy based on the model performance assessment criteria for training and testing data.

Keywords

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