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Compressive strength estimation of eco-friendly geopolymer concrete: Application of hybrid machine learning techniques

  • Xiang, Yang (School of Civil Engineering, Chongqing Vocational Institute of Engineering) ;
  • Jiang, Daibo (Logistics Base, Chongqing Technology and Business Institute) ;
  • Hateo, Gou (Building Department of Shandong University)
  • Received : 2022.10.19
  • Accepted : 2022.12.12
  • Published : 2022.12.25
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Abstract

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO2 emissions in the construction industry. The compressive strength (fc) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the fc of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

Keywords

Acknowledgement

Fund project : Chongqing Municipal Education Commission Project (KJQN202004004, KJQN201904004).

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