This article presents an experimental study on verification of the applicability of Gaussian mixture modelling (GMM) algorithm of acoustic emissions for damage identification in reinforced concrete (RC) structures. Eight RC-flanged beam specimens with different properties were tested subjected to flexural loading. An incremental cyclic load was applied on RC-flanged beam specimens till failure, and simultaneously, the released acoustic emissions (AE) were recorded. It may be required to study crack classification in RC structures, because crack classification studies are useful to predict the structural performance and subsequently to implement the appropriate structural rehabilitation methods. AE belonging to tensile cracking and shear cracking can be studied by a probabilistic approach. It was observed that the line separating the AE clusters belonging to tensile and shear cracks was shifting towards a higher rise angle as the specimen is reaching collapse stage. This observation indicates dominance of shear cracks near the collapse stage. At the loading cycle where yielding occurred in the test specimen obtained by using GMM algorithm for AE, the load cycle entered into heavy damage zone is almost same as per NDIS-2421 damage assessment chart. The results obtained by both GMM algorithms for AE and NDIS-2421 criterion to evaluate the damage in the RC-flanged beams were compared and discussed.
Journal of Civil Structural Health Monitoring – Springer Journals
Published: Jun 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera