Knowledge: about concret properties is of utmost importance in engineering material , and elastic modulus il> one of oncrete\'s moSt important properties that is used in the calculation of def rmati n of structures. For this reason. many researchen. have attemptecl to introduce various correlations between thL propert and the compressive .\',trength. In this paper. support vector committee (SVC) is u \'ed for prediction of elastic m dulus of normal srrength ( SCI and high-strength concretc (HSC). The SVC is based on learning theory. and deploys the technique by intm dueing accuracy insensitive loss funcLion. he comparison between concrete cIa. tic modulus PI\' dieted by the SVC method with the experimental data und those from other methods like support vector machinc (SVM), artificial neural network~ (A , fuzzy logic, and other conventional methods .show marked impr v ment in relation to the best of prediction methods with error indices constantly le~s than 1\'11-. It is theret\' re concluded that the SVC model i!> a greatly morc effecliv method of prediction for elastic modulus of all grades of concretc.

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