Soft computing approaches towards tensile strength estimation of GFRP rebars subjected to alkaline-concrete environment

The rising demand for coastal infrastructure has led to seawater and sea sand concrete which is highly alkaline in nature. Glass-fibre reinforced polymer (GFRP) bars are a durable alternative to conventional steel rebars; however, they are susceptible to degradation in a harsh alkaline concrete environment. This study aims to develop three soft computing techniques, namely minimax probability machine regression (MPMR), deep neural network (DNN), and integrated adaptive neuro-fuzzy inference system with genetic algorithm (ANFIS-GA), to estimate accurate tensile strength retention (TSR) of conditioned GFRP rebars in the aggressive alkaline concrete environment. The sensitivity of production parameters of GFRP rebars and environmental factors in estimating TSR is also discussed. Statistical analyses, e.g. root mean square error (RMSE) and determination coefficient (R2), are used to assess the proposed models' accuracy. The predictions from the developed models manifested a reliable agreement to the experimental results. In comparison, the developed DNN and hybrid ANFIS-GA algorithms outperformed MPMR. The RMSE and R2 are (8.16% and 78.22), (7.42% and 76.04), and (7.53% and 75.00) for the ANFIS-GA, DNN, and MPMR, respectively. The DNN and ANFIS-GA were also compared with the previous approaches developed in the literature. The superior performance of DNN and ANFIS-GA was observed with a maximum model error approach of 10.71%. Furthermore, the sensitivity of temperature and pH of the conditioning environment significantly affect the TSR of GFRP bars. The diameter of GFRP bars and volume fraction of fibres are essential attributes in the degradation of GFRP rebars and shall be considered in the design of concrete structures.