The behavior of the composite multi-layer cylindrical shells subjected to blast load

Today, with the increasing damages of explosion, it is of great significance to assess the performance of structures against such damages that established by explosive load. Accidental explosions exert great and intense dynamic forces to surrounding structures. Recently, composite shells have been used in structures to protect them against explosion which is due to its high resistance to volume ratio, flexibility and resistance to shock forces. Thus, it is essential to assess how structures protected by such materials behave against these forces. In this paper, we have used Abaqus software to analyze data pertaining the behavior of composite shells against explosive loads. We assessed the various parameters affecting the behavior of CRFP and E-Glass Epoxy and how they were affected by explosive load. Some of the parameters assessed include loading, curving rate, number of layers and size of interior angle. In practice, it is necessary to include openings in the composite shell, thus it is important to evaluate the effect of these openings on the behavior of the composite shell. The survey showed that use of the opening has fallen down shift. The reason for this phenomenon is reduction of area that effected by explosive load in composite shel

Efficiency of Neural Networks for Estimating the Patch Load Resistance of Plate Girders with a Focus on Uncertainties in Material and Geometrical Properties

In this paper, a sensitivity analysis of artificial neural networks (NNs) is presented and employed for estimating the patch load resistance of plate girders subjected to patch loading. To evaluate the accuracy of the proposed NN model, the results are compared with the previously proposed empirical models, so that we can estimate the resistance of plate girders subjected to patch loading. The empirical models are calibrated, for improving the formulae, with experimental data set which was collected from the corresponding literature. NNs models are later trained and validated through using the existing experimental data. In this process several NNs architectures are taken into account. A set of good NNs models are selected and then analyzed regarding their robustness when confronted with the test data set and regarding their ability to reproduce the effect of uncertainty on the data. A sensitivity analysis is conducted herein in order to investigate the effect of variability in material and geometrical properties of plate girders. Thereafter, several estimates measuring the efficiency and the quality of the NN model and the calibrated models are obtained and discussed

Safety Analysis of the Patch Load Resistance of Plate Girders: Influence of Model Error and Variability

This study aims to undertake a statistical study to evaluate the accuracy of nine models that have been previously proposed for estimating the ultimate resistance of plate girders subjected to patch loading. For each model, mean errors and standard errors, as well as the probability of underestimating or overestimating patch load resistance, are estimated and the resultant values are compared one to another. Prior to that, the models are initially calibrated in order to improve interaction formulae using an experimental data set collected from the literature. The models are then analyzed by computing design factors associated with a target risk level (probability of exceedance). These models are compared one to another considering uncertainties existed in material and geometrical properties. The Monte Carlo simulation method is used to generate random variables. The statistical parameters of the calibrated models are calculated for various coefficients of variations regardless of their correlation with the random resistance variables. These probabilistic results are very useful for evaluating the stochastic sensitivity of the calibrated models