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Prediction of compressive strength of cement mortar using chemical composition of raw materials

•   Mr. Mahdi Ahmadi. Jalayer ,  Master of science , Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran ,   
•   Ms. Sahar Mahdinia ,  Ferdowsi Univeristy of Mashhad ,   
•   Dr. Mohammadreza Tavakkolizadeh ,  Assistant Professor, Department of Civil and Environmental Engineering, Engineering Faculty, Ferdowsi University of Mashhad ,   

Major Topic: Concrete Structures|سازه های بتنی

Abstract

In recent years, the use of artificial intelligence methods to predict the properties of cement-based mortar and concrete has become very attractive with the aim of preventing expensive laboratory testing. Since most properties of mortar depend on the cement used for their preparation and also the properties of the cement are driven by the materials used for their production, properties of mineral raw materials have a significant impact on the characteristics of cement produced. This study considered different percentages of raw materials entering the kiln (Sio2, Al2O3, Fe2o3, CaO, MgO, SO3, K2O, and Na2O) to produce Portland cement, and used a neural network algorithm to estimate the 2-day compressive strength of cement mortar. Results showed that the prediction of compressive strength is accurate enough, so it is possible to use this very inexpensive method to replace, reduce or complement existing costly and continuous laboratory testing in cement factories.

Keywords

Compressive strength of cement mortar samples; neural network algorithm; cement raw materials; MATLAB software

• High accuracy of prediction method
• Save time using prediction methods

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