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[Display omitted] • CFRP-wrapped R/S RC columns were simulated using soft-computing methodology. • GPR, SVM, and M5P were inter-compared in prediction of K s for the first time. • GPR/SVM-based kernels and (un)pruned M5P were used for the first time for K s. • GPR-PUKF model outperformed than SVM and M5P models with lower deviations. • The total thickness of CFRP was the most effective parameter for predicting the K s. In this study, various soft-computing models (Gaussian process regression (GPR) and support vector machines (SVM) based on the polynomial kernel function (PKF), Pearson VII universal kernel function (PUKF), and radial basis kernel function (RBKF), as well as pruned/unpruned M5P tree models) were simultaneously applied for the first time in prediction of the lateral confinement coefficient (K s) of CFRP-wrapped rectangular/square (R/S) RC columns, and their corresponding predictive successes were appraised statistically. For this aim, short side of the column section (b), long side of the column section (h), total thickness of CFRP (t), compressive strength of the unconfined concrete (f' c 0), and the elastic modulus of CFRP (E CFRP) were used as independent input variables whereas the K s was the output variable. Results indicated that the performance of the Pearson VII kernel function-based Gaussian process regression (GPR-PUKF) model was superior to other models for the training and testing stages. A sensitivity investigation showed that the total thickness of CFRP (t) was the most effective parameter for predicting the K s using GPR-PUKF-based model. Findings of the present computational assessment obviously revealed that the employed soft-computing strategy had the capability of accurately estimating the K s of R/S RC columns wrapped with CFRP. [ABSTRACT FROM AUTHOR]