ADVANCED DAMAGE DETECTION IN REINFORCED CONCRETE APPLYING ACOUSTIC EMISSION, WAVELET TRANSFORM AND SHANNON ENTROPY
DOI:
https://doi.org/10.55766/sujst-2024-05-e05716Keywords:
Concrete Structure, Health Monitoring, Acoustic Emission, Damage Detection, Wavelet Transform, Shannon EntropyAbstract
Concrete structures are extensively used in infrastructure but are susceptible to deterioration due to external loads, fatigue, and environmental factors. Early damage detection is vital for effective monitoring and failure prevention. Continuous sensor-based health monitoring can extend the design life of these structures. Acoustic Emission (AE) is a promising non-destructive testing technique for assessing damage in concrete, as it captures signals generated by microcrack formation and propagation. AE sensors detect these signals, which can be analyzed using wavelet transform (WT) in the time-frequency domain. Additionally, changes in the Shannon entropy of AE signals can indicate the presence and severity of damage in concrete. This study explores the effectiveness of combining WT and Shannon entropy for detecting damage in concrete structures using AE signals. Concrete specimens were subjected to simulated damage using pencil-lead break (PLB) tests, and the resulting AE signals were recorded. The WT was applied to decompose the AE signals into time-frequency components, facilitating the identification of damage-induced frequency bands. Shannon entropy was then calculated for each decomposed frequency band to measure the randomness or disorder within the signal. The results are expected to show that damage initiation and progression are reflected in changes in the frequency content and entropy of AE signals. An increase in entropy in specific WT frequency bands is anticipated to correlate with the presence and severity of damage in the concrete. This study aims to establish a different method for the localization of damage in concrete using a combination of WT and Shannon entropy analysis of AE signals.
References
Aggelis, D.G. (2011). Classification of cracking mode in concrete by acoustic emission parameters. Mechanics Research Communications, 38(3):153-157. https://doi.org/10.1016/j.mechrescom.2011.03.007
Breckenridge, F.R. (1990). Transient sources for acoustic emission work. In Progress in acoustic emission, 20-37.
Chen, J., Dou, Y., Li, Y., and Li, J. (2016). Application of Shannon wavelet entropy and Shannon wavelet packet entropy in analysis of power system transient signals. Entropy, 18(12):437. https://doi.org/10.3390/e18120437
Chui, C.K. (1992). An introduction to wavelets. Mathematics of Computation, 60(202):854 https://doi.org/10.2307/2153134
Das, S., Datta, A.K., Topdar, P., and Pal, A. (2024a). Innovative approaches to concrete health monitoring: Wavelet transform and artificial intelligence models. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-024-01178-7
Das, S., Datta, A.K., Topdar, P., and Sengupta, S. (2022) Application of S1 A1 modes of acoustic emission waves for health monitoring of reinforced concrete slab. In 2022 International Interdisciplinary Conference on Mathematics, Engineering and Science (MESIICON), 1-4. https://doi.org/10.1109/MESIICON55227.2022.10093654
Das, S., Datta, A.K., Topdar, P., and Sengupta, S. (2023). Damage localization in reinforced concrete slab using acoustic emission technique. Advances in Structural Mechanics and Applications, 26:197-208. https://doi.org/10.1007/978-3-031-05509-6_13
Das, S., Datta, A.K., Topdar, P., and Sengupta, S. (2024b). Damage detection in reinforced concrete slab using Shannon entropy applied to acoustic emission signals. Recent Developments in Structural Engineering, 52:501-515). https://doi.org/10.1007/978-981-99-9625-4_35
Ebrahimkhanlou, A. and Salamone, S. (2017). Acoustic emission source localization in thin metallic plates: A single-sensor approach based on multimodal edge reflections. Ultrasonics, 78:134-145. https://doi.org/10.1016/j.ultras.2017.03.006
Esmaielzadeh, S., Mahmoodi, M.J., and Abad, M.J.S. (2023). Application of signal processing techniques in structural health monitoring of concrete gravity dams. Asian Journal of Civil Engineering, 24:2,049-2,063. https://doi.org/10.1007/s42107-023-00624-2
Hsu, N. and Breckenridge, F.R. (1981). Characterization and calibration of acoustic emission sensors. Materials Evaluation, 39(1):60-68.
IS 10262: 2009. Concrete Mix Proportioning- Guidelines, Buraeu of Indian Standards, 1-14.
IS 456: 2000, Plain and Reinforced Concrete - Code of Practice, Bureau of Indian Standards. 1–114.
Jeong, H. and Jang, Y.-S. (2000). Wavelet analysis of plate wave propagation in composite laminates. Composite Structures, 49(4):443-450. https://doi.org/10.1016/S0263-8223(00)00079-9
Ji, Z. and Yan, S. (2017). Properties of an improved Gabor wavelet transform and its applications to seismic signal processing and interpretation. Applied Geophysics, 14:529-542. https://doi.org/10.1007/s11770-017-0642-9
Mirgal, P., Pal, J., and Banerjee, S. (2020). Online acoustic emission source localization in concrete structures using iterative and evolutionary algorithms. Ultrasonics, 108:106211. https://doi.org/10.1016/j.ultras.2020.106211
Naderpour, H., Ezzodin, A., Kheyroddin, A., and Ghodrati Amiri, G. (2017). Signal processing based damage detection of concrete bridge piers subjected to consequent excitations. Journal of Vibroengineering, 19(3):2,080-2,089. https://doi.org/10.21595/jve.2015.16474
Pal, A., Kundu, T., and Datta, A.K. (2024). Acoustic emission-based assessment of weld effects on the health monitoring of the rail section: An experimental study. Engineering Research Express. https://doi.org/10.1088/2631-8695/ad26e1
Piotrkowski, R., Castro, E., and Gallego, A. (2009). Wavelet power, entropy and bispectrum applied to AE signals for damage identification and evaluation of corroded galvanized steel. Mechanical Systems and Signal Processing, 23(2):432-445. https://doi.org/10.1016/j.ymssp.2008.05.006
Pomponi, E. and Vinogradov, A. (2013). A real-time approach to acoustic emission clustering. Mechanical Systems and Signal Processing, 40(2):791-804. https://doi.org/10.1016/j.ymssp.2013.03.017
Sengupta, S., Datta, A.K., and Topdar, P. (2015). Structural damage localization by acoustic emission technique: A state of the art review. Latin American Journal of Solids and Structures, 12(8):1,565-1,582. https://doi.org/10.1590/1679-78251722
Shen, L., and Bai, L. (2006). A review on Gabor wavelets for face recognition. Pattern Analysis and Applications, 9:273-292. https://doi.org/10.1007/s10044-006-0033-y
Suzuki, H., Kinjo, T., Hayashi, Y., Takemoto, M., Ono, K., and Hayashi, Y. (1996). Wavelet transform of acoustic emission signals. Journal of Acoustic Emission, 14:69-84.
Yang, L., Yang, T.T., Zhou, Y.C., Wei, Y.G., Wu, R.T., and Wang, N.G. (2016). Acoustic emission monitoring and damage mode discrimination of APS thermal barrier coatings under high temperature CMAS corrosion. Surface and Coatings Technology, 304:272-282. https://doi.org/10.1016/j.surfcoat.2016.06.080
Zhang, J., Kang, Z., Hou, D., Dong, B., and Ma, H. (2022). Wavelet power and Shannon entropy applied to acoustic emission signals for corrosion detection and evaluation of reinforced concrete. ES Materials and Manufacturing, 16:46-55. https://doi.org/10.30919/esmm5f554
