Estimation of confined compressive strength of LRS‐FRP concrete specimens with computational intelligence

Abstract Reinforced concrete structures deteriorate due to changes in temperature, corrosion, and attacks of sulfate and chloride contents. Retrofitting techniques like fiber‐reinforced polymer (FRP) jacketing, known for their strength and corrosion resistance, are increasingly used to strengthen and retrofit deteriorated structural elements. Large rupture strain (LRS)‐FRP composite, composed of polyethylene terephthalate and polyethylene naphthalate, both of which have high tensile strength and high strain at rupture have been used in the studies of many researchers. This research aims to develop a reliable and accurate machine learning (ML) model to estimate the compressive strength of LRS‐FRP confined specimens. A total of 303 LRS‐FRP confined specimens were gathered after a thorough literature review to develop ML models, utilizing the linear regression, support vector regression, regression tree, and artificial neural network (ANN) algorithms. Additionally, 44 analytical models (AMs) were used to compare the performance of the developed ML models. The results revealed that the performance of the developed ANN model was higher among all the ML and AMs. The R ‐value and the mean absolute percentage error (MAPE) value of the developed ANN model were 0.9822 and 6.17%, respectively. The sensitivity analysis results show that the height of the specimens had the highest impact followed by the diameter of the specimen, the number of FRP layers and thickness, and then the tensile strength of LRS‐FRP. The ANN‐based mathematical expression is simple and easy to use to predict the compressive strength of the LRS‐FRP strengthened specimens.