Title: Determination of rheological characteristics of flow concrete by artificial neural networks method
Author: Ahmet Bilgil, Bekir S. Tezekici, M. Vehbi Gokce
Civil Engineering Department, University of Nigde, 51245 Nigde, Turkey.
Electrical-Electronical Engineering Department, University of Nigde, 51245 Nigde, Turkey.
Faculty of Engineering, Civil Engineering Department Nigde Vocational School for Higher Education, University of Nigde, 51245 Nigde, Turkey
Journal: Int. J. of Reasoning-based Intelligent Systems, 2010 Vol.2, No.3/4, pp.310 - 316
Abstract: Fresh concrete is usually considered as a non-Newtonian fluid since it is a mixture of aggregate, cement and water. The flow behaviour of fresh concrete plays a crucial role in the quality of high performance concretes. Therefore, knowing rheological characteristics of high performance fresh concrete has been an absolute must, to be able to control the behaviour of that type of concrete. But, determining rheological characteristics of fresh concrete experimentally is not an easy task. In this study, considering the mixtures of materials that compose fresh concrete, rheological characteristics of fresh concrete such as slump, yield stress and viscosity values were determined theoretically at a high degree of accuracy by artificial neural networks (ANN) method. Consequently, it was concluded that using ANN in determining flow characteristics of fresh concrete would be a convenient and easy to use tool.
Keywords: concrete viscosity; fresh concrete rheology; artificial neural networks; ANNs; concrete flow; slump; yield stress; viscosity.