The development of techniques capable of evaluating deterioration of reinforced concrete (RC) is instrumental to the
advancement of the structural health monitoring (SHM) and service life estimate for constructed facilities. One of the
main causes leading to degradation of RC is the corrosion of the steel reinforcement. This process can be modeled
phenomenologically, while laboratory tests aimed at studying durability responses are typically accelerated in order to
provide useful results within a realistic period of time. Among nondestructive methods, acoustic emission (AE) is
emerging as a tool to detect the onset and progression of deterioration mechanisms. In this paper, the development of
accelerated corrosion and continuous AE monitoring test set-up for RC specimens are presented. Relevant information
are provided with regard to the characteristics of the corrosion circuit, continuous measurement and acquisition of
corrosion potential, selection of AE sensors and AE parameter setting. Results from small-scale pre-notched RC
specimens aim to isolate the frequency spectrum where the corrosion first takes place. Waveform analysis critical in the
definition of a prognosis model will extend the AE dataset for the onset of corrosion.
The Acoustic Emission (AE) technique plays a progressively significant role in the field of non-destructive testing
(NDT) especially in structural health monitoring (SHM). Acoustic emissions are commonly defined as transient elastic
waves in a material caused by the of localized stress release. In using AE for structural diagnostics, noise has always
been a potential barrier. AE can be produced from sources not related to material damage including traffic or friction.
The major challenge is the differentiation of signals relevant to the purpose of the monitoring - such as crack growth in
a member - from noise of various origins. This paper deals with noise discrimination and introduces a novel approach
for noise interpretation in AE data. AE activities recorded in field and lab environments for concrete and steel
specimens are investigated in this study. Approaches for clustering and separation of AE signals based on multiple
features extracted from experimental data are presented.
A system is being developed to monitor in-service deterioration of reinforced concrete (RC) in highway bridges. The
system includes the monitoring of acoustic emission (AE). To develop a preliminary understanding of AE source
mechanisms and their causes while also getting closer to the challenges of separating relevant AE from noise, a 6ft long
RC test article was monitored in the outdoors environment of a New Jersey summer. There were indications of daily
swings in the AE rate, coinciding with the daily swings in temperature. However this correlation was not consistent or
reproducible. As the monitoring was extended into the winter and the test site was buried in snow, the AE rate dropped
drastically. It was concluded that temperature changes were instrumental in stimulating AE from this damaged concrete.
Implications for the formulation of AE evaluation criteria are discussed. Also, the summer swings provoked
consideration of the underlying stress field, the fractal nature of the heterogeneous material and the stochastic AE
phenomenon. An analysis of calm time distributions yielded results similar to those found by Abe and Suzuki for
earthquake time distributions. Analysis of this kind may help to differentiate relevant AE from some kinds of noise.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.