Prediction of Compressive Strength of Concrete by Data-Driven Models

Faezehossadat Khademi*, Mahmoud Akbari**, Sayed Mohammadmehdi Jamal***
* Graduate Student, Civil, Architectural, and Environmental Engineering Department, Illinois Institute of Technology, Chicago, USA.
** Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran.
*** Graduate Student, Civil Engineering Department, University of Hormozgan, Hormozgan, Iran.
Periodicity:March - May'2015


The aim of this study is prediction of 28-day compressive strength of concrete by data-driven models. Hence, by considering concrete constituents as input variables, two data-driven models namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are constructed for the purpose of predicting the 28-days compressive strength of different concrete mix designs. Comparing the two models illustrates that MLR model is not a suitable model for predicting the compressive strength; however, ANN can be used to efficiently predict the compressive strength of concrete.


Concrete, Compressive Strength, Multiple Linear Regression model (MLR), Artificial Neural Network (ANN).

How to Cite this Article?

Khademi,F., Akbari,M., and Jamal,S,M. (2015). Prediction of Compressive Strength of Concrete by Data-Driven Models. i-manager’s Journal on Civil Engineering, 5(2), 16-23.


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