EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 103 (2017) MATEC Web Conf., 103 (2017) 02027 References
Open Access
Issue
MATEC Web Conf.
Volume 103, 2017
International Symposium on Civil and Environmental Engineering 2016 (ISCEE 2016)
Article Number 02027
Number of page(s) 6
Section Structure, Solid Mechanics and Computational Engineering
DOI https://doi.org/10.1051/matecconf/201710302027
Published online 05 April 2017
  1. K. Ono, Acoustic emission in materials research – A review, J. Acoustic Emission, 29, 284–308, (2011) [Google Scholar]
  2. S. Shahidan, S.S. Mohd Zuki, M.A. Rahim, and N. Ali, Amplitude distribution of emission wave for cracking process, MATEC Web of Conf., 2–8, (2016) [Google Scholar]
  3. T.H. Loutas and V. Kostopoulos, Health monitoring of carbon / carbon, woven reinforced composites. Damage assessment by using advanced signal processing techniques, Part I: Acoustic emission monitoring and damage mechanisms evolution, 69(2), 265–272, (2009) [Google Scholar]
  4. N. Godin, S. Huguet, R. Gaertner and L. Salmon, Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers, NDT E Int., 37, 253–264, (2004) [CrossRef] [Google Scholar]
  5. L. Li, S.V. Lomov, X. Yan, and V. Carvelli, Cluster analysis of acoustic emission signals for 2D and 3D woven glass / epoxy composites, Composite Structures, 116, 286–299, (2014) [CrossRef] [Google Scholar]
  6. A.V. Patil, B. Kumar and R.B. Barjibhe, An extensive review on the use of acoustic emission technique for continuous monitoring, Int. Res. J. Eng. and Technology, 3, 2395–56, (2016) [Google Scholar]
  7. M. Assarar, D. Scida, W. Zouari, E.H. Saidane, and R. Ayad, Acoustic emission characterization of damage in short hemp-fiber reinforced polypropylene composites, Polymer Composites, 37(4), 1101–1112, (2014) [CrossRef] [Google Scholar]
  8. M.A.A. Aldahdooh, N.M. Bunnori and M.A. Megat Johari, Damage evaluation of reinforced concrete beams with varying thickness using the acoustic emission technique, Construction and Building Materials, 44, 812–21, (2012) [CrossRef] [Google Scholar]
  9. S. Shahidan, S.S.M. Zuki, and N. Jamaluddin, Damage grading system for severity assessment on concrete structure, Case Study Construction Materials, 5, 79–86, (2016) [Google Scholar]
  10. N. Md Nor, N.M. Bunnori, A. Ibrahim, S. Shahidan, and S.N.M. Saliah, An investigation on acoustic wave velocity of reinforced concrete beam in-plane source, Proc. IEEE 7th International Colloquium on Signal Processing and Its Applications, 19–22, (2011) [Google Scholar]
  11. S. Shahidan, N. M. Bunnori, N. Md Nor, and S.R. Basri, Damage severity evaluation on reinforced concrete beam by means of acoustic emission signal and intensity analysis, 2011 IEEE Symposium on Industrial Electronics and Applications, 337–341, (2011) [CrossRef] [Google Scholar]
  12. D.D. Doan, E. Ramasso, V. Placet, S. Zhang, L. Boubakar, and N. Zerhouni, An unsupervised pattern recognition approach for AE data originating from fatigue tests on polymer-composite materials, Mechanical System and Signal Processing, 64–65, 465–478, (2015). [CrossRef] [Google Scholar]
  13. L. Calabrese, G. Campanella and E. Proverbio, Use of cluster analysis of acoustic emission signals in evaluating damage severity in concrete structures, J. Acoustic Emission, 28, 129–141, (2010) [Google Scholar]
  14. E. Pomponi and A. Vinogradov, A real-time approach to acoustic emission clustering, Mechanical System and Signal Processing, 40(2), 791–804, (2013) [CrossRef] [Google Scholar]
  15. J. Yadav and M. Sharma, A Review of K-mean Algorithm, Int. J. of Engineering Trends and Technology, 4, 2972–2976, (2013) [Google Scholar]
  16. S. Shahidan, R. Pullin, K.M. Holford, and N. Nor, Quantitative Evaluation of the Relationship between Tensile Crack and Shear Movement in Concrete beams, Adv. Mater. Res., 626, 355–359, (2013) [Google Scholar]
  17. S.A. Kudus, N.M. Bunnori, S.R. Basri, S. Shahidan, M.N.M. Jamil, and N.M. Noor, An overview current application of artificial neural network in concrete, Adv. Mater. Res., 626, 372–375, (2012) [Google Scholar]
  18. S. Shahidan, S.S. Mohd Zuki, M.A. Rahim, and N. Ali, amplitude distribution of emission wave for cracking process, MATEC Web of Conf., 2–8, (2016) [Google Scholar]
  19. L. Yang, H.S. Kang, Y.C. Zhou, W. Zhu, C.Y. Cai, and C. Lu, Surface and coatings technology frequency as a key parameter in discriminating the failure types of thermal barrier coatings cluster analysis of acoustic emission signals, Surface and Coating Technology, 264, 97–104, (2015) [CrossRef] [Google Scholar]
  20. N. Fallahi, G. Nardoni, H. Heidary, R. Palazzetti, X.T. Yan, and A. Zucchelli, Supervised and non-supervised AE data classification of nanomodified CFRP during DCB tests, FME Transaction., 44(4), 415–21, (2016) [CrossRef] [Google Scholar]
  21. N. Md Nor, N.M. Bunnori, A. Ibrahim, S. Shahidan, and S.N. Mat Saliah, Relationship between acoustic emission signal strength and damage evaluation of reinforced concrete structure: Case studies, IEEE Symposium on Industrial Electronics and Applications, 308–313, (2011) [Google Scholar]
  22. L. Farbaniec, H. Couque, and G. Dirras, Prediction of ductile fracture through smallsize notched tensile specimens, Proc. of the 2015 Annual Conference on Experimental and Applied Mechanics, 67–72, (2016) [Google Scholar]
  23. N. Md Nor, N.M. Bunnori, A. Ibrahim, S. Shahidan, and S.N.M. Saliah, An observation of noise intervention into acoustic emission signal on concrete structure, Proc. IEEE 7th Int. Colloq. Signal Process. Its Appl. CSPA, 7–10, (2011) [Google Scholar]
  24. S. Shahidan, N.M. Bunnori, N. Nor, M.A. Rahim, R. Abdullah, and S.S.M. Zuki, Analysis improvements of acoustic emission data in evaluation of concrete beam, ARPN J. Eng. Appl. Sci., 11(20), 11804–11807, (2016) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.