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Title: Artificial neural networks in prediction of mechanical behavior of concrete at high temperature
Keywords: Compressive Strength
Fission Reactor Materials
High Temperature Effects
Stress-Strain Relations
Issue Date: 1997
Publisher: Elsevier
Citation: Nuclear Engineering and Design 178(1), 1-11
Abstract: The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress–strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress–strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging.
URI: 10.1016/S0029-5493(97)00152-0
ISSN: 0029-5493
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