Guided Lamb waves for identification of damage in composite structures: A review

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

Particle swarm optimization algorithm: an overview

A review of recent research on mechanics of multifunctional composite materials and structures

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

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Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

A strain sensor has been fabricated from a polymer nanocomposite with multiwalled carbon nanotube (MWNT) fillers. The piezoresistivity
of this nanocomposite strain sensor has been investigated based on an improved three-dimensional (3D) statistical resistor network
model incorporating the tunneling effect between the neighboring carbon nanotubes (CNTs), and a fiber reorientation model. The
numerical results agree very well with the experimental measurements. As compared with traditional strain gauges, much higher sensitivity
can be obtained in the nanocomposite sensors when the volume fraction of CNT is close to the percolation threshold. For a small
CNT volume fraction, weak nonlinear piezoresistivity is observed both experimentally and from numerical simulation. The tunneling
effect is considered to be the principal mechanism of the sensor under small strains.

Tunneling effect in a polymer/carbon nanotube nanocompositestrain sensor

Identification of Damage Using Lamb Waves

Identification of Damage Using Lamb Waves : From Fundamentals to Applications

Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm

The interaction of Lamb wave modes at varying frequencies with a through-thickness crack of different lengths in aluminium plates was analysed in terms of finite element method and experimental study. For oblique-wave incidence, both numerical and experimental results showed that the wave scattering from a crack leads to complicated transmission, reflection and diffraction accompanied by possible wave-mode conversion. A dual-PZT actuation scheme was therefore applied to generate the fundamental symmetrical mode (S0) with enhanced energy to facilitate the identification of crack-scattered wave components. The relationship between crack length and the reflection/transmission coefficient obtained with the aid of the Hilbert transform was established, through which the crack length was quantitatively evaluated. The effects of wavelength of Lamb waves and wave diffraction on the properties of the reflection and transmission coefficients were analysed.

Zhongqing Su

Papers

Guided Lamb waves for identification of damage in composite structures: A review

Identification of Damage Using Lamb Waves

Identification of Damage Using Lamb Waves : From Fundamentals to Applications

Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm

Quantitative assessment of through-thickness crack size based on Lamb wave scattering in aluminium plates