A laboratory analysis of concrete samples requires significant experimental time and cost. In addition, advancement in data mining provide valuable tool for researchers to extract information regarding relations among experiment and physical properties in a more elaborate way to improve prediction models performance and guide concrete mix design. A 90 samples data set is developed and used in this research. The experiment is designed to study the effect of natural silica addition at different levels on physical properties of concrete mainly compressive strength. Compressive strength is measured after 3 and 28 days for different levels of milling time. Support vector regression and neural network models are developed for predicting the compressive strength of concrete using five input variables including silica additive fraction. The SVR model metrics are compared with ANN model and showed good correlation coefficient of 0.929 but less than ANN. The advantage of SVR over ANN is shown in the developed regression model which can be interpreted physically. The silica fraction variable ranked third after curing time and cement ratio variable which indicates its importance.

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