Fuzzy control system

Fibre-reinforced plastic

Algorithm

Residual

Engineering

Statistics

Materials science

Adaptive neuro fuzzy inference system

Ultimate tensile strength

Bar (unit)

Computer science

Durability

Geology

Oceanography

Volume fraction

Mathematics

Residual strength

Fuzzy logic

Weibull distribution

Artificial intelligence

Composite material

Structural engineering

Artificial neural network

Machine learning

Gene expression programming

Ocean Engineering

Based on a comprehensive literature study, the potential variables that can affect the durability of GFRP bars under a harsh alkaline environment especially seawater and sea sand concrete (SWSSC) environment, were investigated and reported in this paper. The study presents a new strategy for finding tensile strength retention (TSR) using empirical models based on the strong non-linear ability of artificial intelligence techniques, i.e., artificial neuro-networking (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS). The diameter of GFRP bars, the volume fraction of glass fibers, the pH value of solutions, the temperature, and the duration of conditioning were considered as input parameters to find TSR of aged GFRP bars. Statistical checks evaluated the trained models, and the results demonstrate that the models provide a reliable estimate of TSR. A simple mathematical prediction formula was developed using the GEP model that can quickly foresee the TSR for aged GFRP bar. In comparison with the GEP model, the ANN model and the ANFIS model provided slightly better results. The parametric study indicates that the large diameter of bars and the high volume fraction of fibers have positive effects on the TSR, while the high temperature and the long duration of conditioning have negative influences.

Computational AI prediction models for residual tensile strength of GFRP bars aged in the alkaline concrete environment