INVESTIGATION ON THE ARTIFICIAL NEURAL NETWORKS PREDICTION CAPABILITIES APPLIED TO VIBRATING PLATES IN SIMILITUDE

The prediction capabilities of artificial neural networks in similitude field are investigated. They have been applied to plates in similitude with two objectives: prediction of natural frequencies and model identification. The results show that the method is able to give accurate predictions and that an experimental training set can be created if the models are well characterized

Support of Dynamic Measurements Through Similitude Formulations

Up to now, similitude methods have been used in order to overcome the typical drawbacks of experimental testing and numerical simulations by reconstructing the full-scale model behavior from that of the scaled model. The novelty of this work is the application of similitude theory not as a tool for predicting the prototype dynamic response, but for supporting, and eventually validating, experimental measurements polluted by noise. Two Aluminium Foam Sandwich (AFS) plates are analyzed with Digital Image Correlation (DIC) cameras. First, an algorithm for blind source separation problems is used to extract information about the excitation; then, SAMSARA (Similitude and Asymptotic Models for Structural-Acoustic Research Applications) similitude method is applied to both the force spectra and velocity responses of prototype and model. The reconstruction of force and velocity curves demonstrates that the similitude results are coherent with the quality of the experimental measurements: when the spatial pattern in resonance is recognizable, then the curves overlap. Instead, when the displacement field of just one model is not well identified, the reconstruction exhibits discrepancies. Therefore, similitude methods reveal to be an interesting tool for understanding if a set of measurements is reliable or not and their application should not be underestimated, especially in the light of the expanding range of approaches which can extract important information from noisy observations

Effect of rare earth dopants on structural and mechanical properties of nanoceria synthesized by combustion method

Structural characteristics of combustion synthesized, calcined and densified pure and doped nanoceria with tri-valent cations of Er, Y, Gd, Sm and Nd were analyzed by X-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM). The results showed that the as-synthesized and calcined nanopowders were mesoporous and calculated lattice parameters were close to theoretical ion-packing model. The effect of dopants on elastic modulus, microhardness and fracture toughness of sintered pure and doped ceria were investigated. It was observed that tri-valent cation dopants increased the hardness of the ceria, whereas the fracture toughness and elastic modulus were decreased

Structural and mechanical properties of La0.6Sr0.4M0.1Fe0.9O3-δ (M: Co, Ni and Cu) perovskites

La0.6Sr0.4M0.1Fe0.9O3-δ (M: Co, Ni and Cu) perovskite nanostructures were synthesized using low frequency ultrasound assisted synthesis technique and effect of substitution of Fe by Co, Ni and Cu on crystal structure and mechanical properties in La0.6Sr0.4FeO3-δ perovskite were studied. The HRTEM and Rietveld refinement analyses revealed the uniform equi-axial shape of the obtained nanostructures with the existence of La0.6Sr0.4M0.1Fe0.9O3−δ with mixed rhombohedral and orthorhombic structures. Substitution of Cu decreases the melting point of La0.6Sr0.4FeO3-δ. The results of mechanical characterizations show that La0.6Sr0.4Co0.1Fe0.9O3−δ and La0.6Sr0.4Ni0.1Fe0.9O3−δ have ferroelastic behavior and comparable elastic moduli, however, subtitution of Ni shows higher hardness and lower fracture toughness than Co in Bsite dopin

Phononic bandgap optimization in sandwich panels using cellular truss cores

The development of custom cellular materials has been driven by recent advances in additive manufacturing and structural topological optimization. These contemporary materials with complex topologies have better structural efficiency than traditional materials. Particularly, truss-like cellular structures exhibit considerable potential for application in lightweight structures owing to their excellent strength-to-mass ratio. Along with being light, these materials can exhibit unprecedented vibration properties, such as the phononic bandgap, which prohibits the propagation of mechanical waves over certain frequency ranges. Consequently, they have been extensively investigated over the last few years, being the cores for sandwich panels among the most important potential applications of lattice-based cellular structures. This study aims to develop a methodology for optimizing the topology of sandwich panels using cellular truss cores for bandgap maximization. In particular, a methodology is developed for designing lightweight composite panels with vibration absorption properties, which would bring significant benefits in applications such as satellites, spacecraft, aircraft, ships, automobiles, etc. The phononic bandgap of a periodic sandwich structure with a square core topology is maximized by varying the material and the geometrical properties of the core under different configurations. The proposed optimization methodology considers smooth approximations of the objective function to avoid non-differentiability problems and implements an optimization approach based on the globally convergent method of moving asymptotes. The results show that it is feasible to design a sandwich panel using a cellular core with large phononic bandgaps

Damage detection in steel–concrete composite structures by impact hammer modal testing and experimental validation

Steel–concrete composite systems are an efficient alternative to mid- and high-rise building structures because of their high strength-to-weight ratio when compared to traditional concrete or steel constructive systems. Nevertheless, composite structural systems are susceptible to damage due to, for example, deficient construction processes, errors in design and detailing, steel corrosion, and the drying shrinkage of concrete. As a consequence, the overall strength of the structure may be significantly decreased. In view of the relevance of this subject, the present paper addresses the damage detection problem in a steel–concrete composite structure with an impact-hammer-based modal testing procedure. The mathematical formulation adopted in this work allows for the identification of regions where stiffness varies with respect to an initial virgin state without the need for theoretical models of the undamaged structure (such as finite element models). Since mode shape curvatures change due to the loss of stiffness at the presence of cracks, a change in curvature was adopted as a criterion to quantify stiffness reduction. A stiffness variability index based on two-dimensional mode shape curvatures is generated for several points on the structure, resulting in a damage distribution pattern. Our numerical predictions were compared with experimentally measured data in a full-scale steel–concrete composite beam subjected to bending and were successfully validated. The present damage detection strategy provides further insight into the failure mechanisms of steel–concrete composite structures, and promotes the future development of safer and more reliable infrastructures

Experimental investigation into techniques to predict leak shapes in water distribution systems using vibration measurements

Water loss from leaking pipes represents a substantial loss of revenue as well as environmental and public health concerns. Leak location is normally identified by placing sensors either side of the leak and recording and analysing the leak noise. The leak noise contains information about the leak’s characteristics, including its shape. Whilst a tool which non-invasively provides information about a leak’s shape from the leak noise would be useful for water industry practitioners, no tool currently exists. This study evaluates the effect of various leak shapes on the vibration signal and presents a unique methodology for predicting the leak shape from the vibration signal. An innovative signal processing technique which utilises the machine learning method Random Forest classifiers is used in combination with a number of signal features in order to develop a leak shape prediction algorithm. The results demonstrate a robust methodology for predicting leak shape at several leak flow rates and backfill types, providing a useful tool for water companies to assess leak repair based on leak shape

Mechanical properties updating of a non-uniform natural fibre composite panel by means of a parallel genetic algorithm

This article presents an investigation on the mechanical properties of a composite panel made of unidirectional flax fibres embedded in a polyethylene matrix (flax-PE). An initial set of mechanical properties was identified by classical static tests. Then, an experimental modal analysis was performed in order to get information on natural frequencies and mode shapes, which are related to the mechanical properties. The experimental modal results were compared with numerical ones, obtained through finite element model using the initial set of mechanical properties. Finally, in order to get a good numerical-experimental correlation, the mechanical properties throughout the panel were updated using an inverse modelling method based on parallel genetic algorithms

Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies

The main problem in damage assessment is the determination of how to ascertain the presence, location, and severity of structural damage given the structure's dynamic characteristics. The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms. However, these algorithms run quite slowly, and the damage assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health monitoring applications. Artificial neural networks (ANN) have recently been introduced as an alternative to model updating methods. Once a neural network has been properly trained, it can potentially detect, locate, and quantify structural damage in a short period of time and can therefore be applied for real-time damage assessment. The primary contribution of this research is the development of a real-time damage assessment algorithm using ANN and antiresonant frequencies. Antiresonant frequencies can be identified more easily and more accurately than mode shapes, and they provide the same information. This research addresses the setup of the neural network parameters and provides guidelines for the selection of these parameters in similar damage assessment problems. Two experimental cases validate this approach: an 8-DOF mass-spring system and a beam with multiple damage scenarios