Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine

This article examines the capability of Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM) for determination of compressive strength (f (c) ) of self compacting concrete. The input variables of LSSVM and RVM are Cement (kg/m(3))(C), Fly ash (kg/m(3))(F), Water/powder (w/p), Superplasticizer dosage (%)(SP) Sand (kg/m(3))(S) and Coarse Aggregate (kg/m(3))(CA). The output of LSSVM and RVM is f (c) . The developed LSSVM and RVM give equations for prediction of f (c) . A comparative study has been done between the developed LSSVM, RVM and ANN models. Experiments have been conducted to verify the developed RVM and LSSVM. The developed RVM gives variance of the predicted f (c) . The results confirm that the developed RVM is a robust model for prediction of f (c) of self compacting concrete.