In the context of Eurocode 4 Design of Composite Steel and Concrete Structures, Part 1-2: Structural fire design, this paper proposes a new simplified design method to determine the fire resistance of columns with concrete-filled steel hollow sections. This method was introduced into the French national annex of the Eurocode in October 2007 in place of the informative Annex H. After mentioning several theoretical shortcomings of Annex H and its lack of accuracy leading to unsafe design of columns with usual slenderness, an advanced finite-element model already developed by the current authors is briefly presented. A comparison with fire tests carried out in France, Germany and Canada illustrates the good accuracy of the model. The new design method is then explained progressively.

Previous investigations proved the unsafety of the current design guidelines in Annex H of EN 1994-1-2 for the calculation of the fire resistance of slender concrete-filled steel tubular (CFST) columns. In this paper, a new simplified design method based on the general rules in Clause 4.3.5.1 of EN 1994-1-2 is presented for correcting this inaccuracy. For the development of the method, an extensive parametric study consisting of about 20.500 analysis cases was carried out by using numerical models, which in turn were validated against a wide range of experimental results. The proposed method provides a significant extension over the current EN 1994-1-2 applicability limits, reaching high member slenderness, large eccentricities and being valid for all the commercially available geometries, including elliptical hollow sections. The design proposal is divided into two parts: thermal, where a simplified cross-sectional temperature field can be obtained based on equivalent temperatures for the composite section constituents, and mechanical, where a full method for evaluating the ultimate buckling load in the fire situation is given. The proposed method is valid for axially and eccentrically loaded columns, accounting for eccentricities on both minor and major axis and reaching large eccentricities of e/D = 1. Finally, it is proved that the proposed method provides safe predictions as compared to experimental results and meets the CEN/TC250/SC4 accuracy criteria.

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Unbiased global illumination methods based on stochastical techniques provide photorealistic images. However, they are prone to noise that can only be reduced by increasing the number of processed samples. The problem of finding the number of samples that are required in order to ensure that most observers cannot perceive any noise is still an open issue. In this article, we address this problem focusing on visual perception of noise. However, rather than using known perceptual models, we investigate the use of learning approaches classically used in the field of Artificial Intelligence. Hence, we propose to use such approaches to create a model which is able to learn which image highlights perceptual noise. The learning is performed through the use of a database of examples based on experimentations of noise perception with human users. This model can then be used in any progressive stochastic global illumination method in order to find the visual convergence threshold of different parts of an input image.

The aim of realistic image synthesis is to produce high fidelity images that authentically represent real scenes. As these images are produced for human observers, we may exploit the fact that not everything is perceived when viewing scene with our eyes. Thus, it is clear that taking advantage of the limited capacity of the human visual system (HVS), can significantly contribute to optimize rendering software.Global illumination methods are used to simulate realistic lighting in 3D scenes. They generally provide a progressive convergence to high-quality solution. One of the problem of such algorithms is to determine a stopping condition, for deciding if calculations reached a satisfactory convergence allowing the process to terminate.In this paper, we propose and we discuss different solutions to this important problem. We show different techniques based on the Visual Difference Predictor (VDP) proposed by Daly [Daly 1993] to define a perceptual stopping condition for rendering computations. We use the VDP to measure the perceived differences between rendered images and to guide the Path Tracing rendering to satisfy a perceptual quality. Also, in a controlled experimental setting with real subjects, we validate our results.