Determination of rheological characteristics of flow concrete by artificial neural networks method. (15th November 2010)

• Record Type: Journal Article
• Authors: Bilgil, Ahmet; Tezekici, Bekir S.; Gokce, M. Vehbi
• Is Part Of: International journal of reasoning-based intelligent systems. Volume 2:Number 3/4(2010)
• Page Start: 310
• Page End: 316
• Publication Date: 2010-11-15
• Subjects: concrete viscosity -- fresh concrete rheology -- artificial neural networks -- ANNs -- concrete flow -- slump -- yield stress -- viscosity; Artificial intelligence -- Periodicals; Reasoning -- Periodicals; 006.3
• Languages: English
• ISSNs: 1755-0556

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.