Faezehossadat Khademi*, Mahmoud Akbari**, Sayed Mohammadmehdi Jamal***

Periodicity:March - May'2015

DOI : https://doi.org/10.26634/jce.5.2.3350

* 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.

DOI : https://doi.org/10.26634/jce.5.2.3350

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.

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