; Artificial Intelligence Model for Early-Age Autogenous Shrinkage of Concrete
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Artificial Intelligence Model for Early-Age Autogenous Shrinkage of Concrete

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In this paper, an artificial neural network (ANN) model for the early-age autogenous shrinkage of concrete is proposed. The model inputs include the cement content, water-cement ratio (w/c), type and percentage of supplementary cementitious materials, total aggregate volume, curing temperature, and hydration age. The autogenous shrinkage of concrete is the model output. The autogenous shrinkage database assembled and used in the training of the proposed ANN is considered a contribution to the state of the art of knowledge; it includes testing results on modern concretes under various environmental conditions. The developed ANN model exhibited excellent capability in capturing complex effects and interactions among model inputs on the development of autogenous shrinkage. Subsequent to model validation, a parametric study was carried out to identify the effects of input variables on the evolution of autogenous shrinkage. The trained ANN model demonstrated the ability of predicting the autogenous shrinkage behavior, not only of traditional concrete mixtures, but also that of modern concrete mixtures such as reactive powder concrete, which are characterized by very low w/c. [PUBLICATION ABSTRACT]

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									 ACI MATERIALS JOURNAL                                                                           TECHNICAL PAPER
Title no. 109-M33

Artificial Intelligence Model for Early-Age Autogenous
Shrinkage of Concrete
by Moncef L. Nehdi and Ahmed M. Soliman

In this paper, an artificial neural network (ANN) model for the           supplementary cementitious materials.6 Such parameters are
early-age autogenous shrinkage of concrete is proposed. The               highly interdependent and exhibit complex, combined roles
model inputs include the cement content, water-cement ratio               on the development of autogenous shrinkage.
(w/c), type 
								
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