Fire analysis of steel frames with the use of artificial neural networks

The paper presents an alternative approach to the modelling of the mechanical behaviour of steel frame material when exposed to the high temperatures expected in fires. Based on a series of stress-strain curves obtained experimentally for various temperature levels, an artificial neural network (ANN) is employed in the material modelling of steel. Geometrically and materially, a non-linear analysis of plane frame structures subjected to fire is performed by FEM. The numerical results of a simply supported beam are compared with our measurements, and show a good agreement, although the temperature-displacement curves exhibit rather irregular shapes. It can be concluded that ANN is an efficient tool for modelling the material properties of steel frames in fire engineering design studies. (c) 2007 Elsevier Ltd. All rights reserved

Influence of moisture on fire resistance of side-plated RC beams

The objective of the paper is to present the effect of moisture in concrete on the fire resistance of side-plated reinforced concrete (RC) beams. For this purpose an adequate new finite-element (FE) procedure is presented. The paper concludes with a parametric study, examining the impacts of the vapor-tight side plates, high initial moisture content and low initial permeability of concrete of the selected RC beam on the rate of the risk of concrete spalling

Influence of moisture on fire resistance of side-plated RC beams

The objective of the paper is to present the effect of moisture in concrete on the fire resistance of side-plated reinforced concrete (RC) beams. For this purpose an adequate new finite-element (FE) procedure is presented. The paper concludes with a parametric study, examining the impacts of the vapor-tight side plates, high initial moisture content and low initial permeability of concrete of the selected RC beam on the rate of the risk of concrete spalling