No abstract

There is a need to better understand the effect of temperature changes on the response of ultrasonic guided-wave pitch-catch systems used for structural health monitoring. A model is proposed to account for all relevant temperature-dependent parameters of a pitch-catch system on an isotropic plate, including the actuator-plate and plate-sensor interactions through shear-lag behavior, the piezoelectric and dielectric permittivity properties of the transducers, and the Lamb wave dispersion properties of the substrate plate. The model is used to predict the S(0) and A(0) response spectra in aluminum plates for the temperature range of -40-+60 degrees C, which accounts for normal aircraft operations. The transducers examined are monolithic PZT-5A [PZT denotes Pb(Zr-Ti)O3] patches and flexible macrofiber composite type P1 patches. The study shows substantial changes in Lamb wave amplitude response caused solely by temperature excursions. It is also shown that, for the transducers considered, the response amplitude changes follow two opposite trends below and above ambient temperature (20 degrees C), respectively. These results can provide a basis for the compensation of temperature effects in guided-wave damage detection systems.

No abstract

Reinforced concrete is subjected to deterioration due to aging, increased load, and natural hazards. To minimize the maintenance costs and to increase the operation lifetime, researchers and practitioners are increasingly interested in improving current nondestructive evaluation technologies or building advanced structural health monitoring strategies. Acoustic emission methods offer an attractive solution for nondestructive evaluation/structural health monitoring of reinforced concrete structures. In particular, monitoring the development of cracks is of large interest because their properties reflect not only the condition of concrete as material but also the condition of the entire system at structural level. This article presents a new probabilistic approach based on Gaussian mixture modeling of acoustic emission to classify crack modes in reinforced concrete structures. Experimental results obtained in a full-scale reinforced concrete shear wall subjected to reversed cyclic loading are used to demonstrate and validate the proposed approach.

This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu-Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.