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Summary A new approach to sensitize detecting structural damage using empirical mode decomposition (EMD) of the spatial signals is proposed. The difference of undamaged and damaged spatial strain and strain energy data for the beam and bridge modes is processed through the EMD, which yields the respective IMF1, which shows that damage location can be identified distinctively for as low as 0.01% reduction in elemental stiffness of beam discretized with 30 elements. An analysis was also carried out for a two‐dimensional bridge model (I‐40). It can detect damage sensitivity up to 0.5% loss in stiffness of the bridge. EMD as a spatial analysis tool seems to be the most sensitive among all methods currently available. Copyright © 2014 John Wiley & Sons, Ltd.

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The development of armour systems with higher ballistic resistance and light weight has gained considerable attention as an increasing number of countries are recognising the need to build up advanced self-defence system to deter potential military conflicts and threats. Graphene is a two dimensional one-atom thick nanomaterial which possesses excellent tensile strength (130 GPa) and specific penetration energy (10 times higher than steel). It is also lightweight, tough and stiff and is expected to replace the current aramid fibre-based polymer composites. Currently, insights derived from the study of the nacre (natural armour system) are finding applications on the development of artificial nacre structures using graphene-based materials that can achieve high toughness and energy dissipation. The aim of this review is to discuss the potential of graphene-based nanomaterials with regard to the penetration energy, toughness and ballistic limit for personal body armour applications. This review addresses the cutting-edge research in the ballistic performance of graphene-based materials through theoretical, experimentation as well as simulations. The influence of fabrication techniques and interfacial interactions of graphene-based bioinspired polymer composites for ballistic application are also discussed. This review also covers the artificial nacre which is shown to exhibit superior mechanical and toughness behaviours.

In laminated composite structures, delamination is one of the most common defects. The delamination affects the vibration characteristics of laminates, and thus these indicators can be used to detect the potentially catastrophic failures and measures the health characteristics of laminates. In this study, particle swarm optimization (PSO) and artificial neural network (ANN) are used to optimize and predict the influences of location and size of delamination on the dynamic behavior of composite plates. The classical laminated plate theory

Cylindrical storage tanks are widely used for various types of liquids, including hazardous contents, thus requiring suitable and careful design for seismic actions. The study herein presented deals with the dynamic analysis of a ground-based horizontal cylindrical tank containing butane and with its safety verification. The analyses are based on a detailed finite element (FE) model; a simplified one-degree-of-freedom idealization is also set up and used for verification of the FE results. Particular attention is paid to sloshing and asynchronous seismic input effects. Sloshing effects are investigated according to the current literature state of the art. An efficient methodology based on an "impulsive-convective" decomposition of the container-fluid motion is adopted for the calculation of the seismic force. The effects of asynchronous ground motion are studied by suitable pseudo-static analyses. Comparison between seismic action effects, obtained with and without consideration of sloshing and asynchronous seismic input, shows a rather important influence of these conditions on the final results.

This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.