Reza Jamalpour - Assistant Professor, Department of Civil Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Maryam Jamalpour - MS.c Student, Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Amirhosein Jamalpour - Teaching Assistant, Department of Civil Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

Concrete is an artificial stone that is made from a combination of cement, aggregate, water and additives. Today, this naturalstone has been used a lot in civil projects. One of the important characteristics of concrete is having a suitable efficiency for itsuse in different purposes and structures. The strength of concrete is highly dependent on its components and the amount andpercentage of their composition. Cement, water, granulation, lubricants, etc. are among the determining parameters that thesmallest change in their amount changes the strength of concrete. Predicting the strength of concrete is very difficult, but today,using machine learning techniques and having datasets, it is possible to predict the strength of concrete with a goodapproximation. In this paper, a data set of various concrete tests was analyzed using machine learning techniques and theresults were compared. In this review, the Linear Regression and Support Vector Regression with linear kernel algorithms areshown better results and less error than other algorithms

Concrete, Machine learning, Concrete Component, Concrete Test, Compressive Strength