A model based on an artificial neural network (ANN) for predicting the loss of strength of concrete under high temperatures (75–1200°C) is presented. The ANN is a recent development and is particularly useful for evaluating non-linear systems with several variables the relationships between which cannot be defined explicitly by mathematical equations. It has been reported in the literature that the fire resistance of concrete is related to several material and environmental factors, but this relationship is difficult to quantify mathematically. The advantage of the ANN is that it can be trained, using experimental data assembled from published research, to recognize the relationship between these influencing factors and the fire resistance of concrete measured by the loss of strength. After training, the ANN is applied to predict the loss of strength of concrete with new material and environmental factors. In the tests conducted the prediction errors between the network outputs and the actual experimental results were less than 15%.

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A simple un-tapered wing box model was considered to illustrate an aeroelastic tailoring of varying ribs orientation with respect to a range of sweep angles. This approach allows the bending-torsional modes characteristic to be altered hence offering possibility for improvement in aeroelastic performance without having to compromise its overall weight. Two cases of ribs orientation were considered for a range of sweep angles. The first case was by allowing one individual ribs to be orientated at a time and the second case considering all possible combination of ribs orientation. The finding shows that the torsional modes are greatly influenced by the rib orientation while the bending modes are not significantly affected. Therefore, this enable the frequency gap between the flutter modes to be altered; hence resulting into significant impact to aeroelastic performances. It has been found that for all the considered sweep angles, the varying ribs orientation can lead into an improvement of nearly 80-90% when compared to its corresponding baseline configuration. Therefore, this provides a leverage for an advancement in aeroelastic performance without having to penalize the total weight of the wing structure.