Piezoelectric wafer active sensors (PWAS) have been used as actuators in structural health monitoring system of beams,
plates, and truss elements. This paper presents a shear lag solution for the transfer of stress and strain between a
structurally attached piezoelectric wafer active sensor and the support structure. We will derive a shear lag solution not
limited to the low frequency approximation, i.e., a generic solution. Both the low frequency approximation and the
generic solution will be applied to the computation of PWAS-structure tuning curves.
Guided Lamb waves can be excited in composite materials through piezoelectric wafer active sensors (PWAS) to detect
damage. PWAS are small, light-weight, inexpensive, and can be attached or embedded in composite structures. The
proposed paper will present a parallel effort on two analytical approaches for predicting Lamb wave propagation in
composite structures with surface attached PWAS. The first approach implements a layerwise mechanics theory and
finite element for laminated composite beams with transducers and delaminations. The second approach uses a transfer
matrix methodology (TM) and normal mode expansion (NME) to predict PWAS-plate interaction.
Wave propagation predictions are performed using 2-D layerwise beam theory approximating the in-plane
displacement, the through-thickness displacements and the electrical field as a continuous assembly of linear layerwise
fields through the thickness. The effect of delamination cracks can be predicted by the introduction of additional
degrees of freedom. Prediction of symmetric, antisymmetric and shear horizontal Lamb wave dispersion curves is done
for composite material structures using TM methodology developed by Nayfeh. NME technique is applied to predict the
PWAS tuning curves on composite plates; theoretical and experimental results are compared. Prediction of sensor
signals and local displacement curves through the thickness will be presented for composite structure.
Piezoelectric wafer active sensors (PWAS) are small, inexpensive, unobtrusive devices capable of generating and detecting Lamb waves in thin-wall structures. PWAS are directly attached to the surface of a metallic structure or inserted between the layers of a composite structure. PWAS interact with the structure through surface shear stresses that couple the in-plane motion of the PWAS with the in-plane motion of the structure undergoing Lamb wave motion. The paper will present a simulation of the Lamb wave interaction between PWAS and host structure using analytical solutions in axisymmetric formulation. The Bessel function solutions are used to model the Lamb waves emanating from the PWAS. The time domain Fourier transform is used to process the excitation signal into its frequency components. The frequency domain excitation is used to modulate the Fourier transform of the Bessel function solution in the frequency domain. Inverse Fourier transform is used to return from the frequency domain in to the time domain. Simulations will be presented for symmetric and antisymmetric Lamb-wave modes at various frequencies and mode numbers. The influence of the mode number and frequencies upon the efficiency of the Lamb wave interaction between PWAS and host structure is studied and exemplified with numerical solutions and visualizations. Experiments on different kind PWAS and plate material, thickness, and dimension, will be illustrated and compared with the simulations. The aim of the paper will be then to evidence that the illustrated method is able to predict the Lamb-wave tuning with PWAS transducers in different structures.
In the last decades many forest areas are suffering from conventional and new types of damage, with a consequent loss of valuable ecological and economic resources. The monitoring of these damage has therefore become a primary application of satellite remotely sensed data, and particularly of Landsat TM imagery. Unfortunately, conventional mapping methods based on uni or multivariate regressions between ground measurement and remotely sensed spectral information have often led to unsatisfactory results, especially in complex environments where several disturbing factors can affect the forest spectral signatures. It is here proposed that a new, more flexible estimation method based on fuzzy classification of remotely sensed data can offer several advantages when used for this purpose. After a brief description of its basis, the method is applied together with conventional multivariate regression procedures in two case studies in Tuscany (Central Italy) representative of different forest types affected by damages of different origins. The results show that the new method produces higher accuracies in the estimation of forest damage, particularly in areas with complex environmental situations.
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