AbstractNowadays, retrofitting and rehabilitation of deteriorated reinforced concrete structures are becoming a growing need of the construction industry instead of demolishing aged structures. The application of fabric‐reinforced cementitious matrix (FRCM) on the existing concrete structures is one of the sustainable solutions to retrofit the concrete structures. This study used machine learning (ML) models such as linear regression (LR), support vector machines (SVM), and adaptive neuro‐fuzzy inference systems (ANFIS) to estimate the compressive strength (CS) of columns wrapped with FRCM. The experimental dataset of 301 column specimens was collected including input parameters such as cross‐sectional properties, mechanical properties of concrete and steel, and characteristics of FRCM material. Apart from ML models, seven analytical models were also used to compare the accuracy and precision of ML models. The results illustrate that the ANFIS model outperformed other ML models and established itself as a dependable and precise model. The R‐value of the ANFIS model was 0.9816, whereas R‐values of 0.9269 and 0.9572 were achieved by LR and SVM models, respectively. In addition, the MAPE value acquired by the ANFIS model was 1.52% which was lower than those of the LR model by 73.24%, and the SVM model by 60.60%, respectively. As the precision of the ANFIS model was higher as compared with SVM and LR models, so, the developed ANFIS‐based mathematical model can be easily used to predict the CS of FRCM‐strengthened concrete columns. The developed model is accurate, economical, and fast; and can be utilized by FRCM applicators and structural designers.
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