Article Article
Concrete Damage Identification based on Acoustic Emission and Wavelet Neural Network

In order to improve the accuracy of damage source identification in concrete based on acoustic emission testing (AE) and neural networks, and locating and repairing the damage in a practical roller compacted concrete (RCC) dam, a multilevel AE processing platform based on wavelet energy spectrum analysis, principal component analysis (PCA), and a neural network is proposed. Two data sets of 15 basic AE parameters and 23 AE parameters added on the basis of the 15 basic AE parameters were selected as the input vectors of a basic parameter neural network and a wavelet neural network, respectively. Taking the measured tensile data of an RCC prism sample as an example, the results show that compared with the basic parameter neural network, the wavelet neural network achieves a higher accuracy and faster damage source identification, with an average recognition rate of 8.2% and training speed of about 33%.

DOI: https://doi.org/10.32548/2022.me-04232

References

Abouhussien, A.A., and A.A.A. Hassan, 2016, “Acoustic Emission Monitoring for Bond Integrity Evaluation of Reinforced Concrete under Pull-Out Tests,” Advances in Structural Engineering, Vol. 20, No. 9, pp. 1390–1405, https://doi.org/10.1177/2F1369433216678864

Cui, L.H., F. Ma, Q. Gu, and T.F. Cai, 2018, “Time-Frequency Analysis of Pressure Pulsation Signal in the Chamber of Self-Resonating Jet Nozzle,” International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, No. 11, https://doi.org/10.1142/S0218001418580065

Falamarzi, Y., N. Palizdan, Y.F. Huang, and T.S. Lee, 2014, “Estimating Evapotranspiration from Temperature and Wind Speed Data Using Artificial and Wavelet Neural Networks (WNNs),” Agricultural Water Management, Vol. 140, pp. 26–36, https://doi.org/10.1016/j.agwat.2014.03.014

Fan, X.Q., S.W. Hu, J. Lu, and C.J. Wei, 2016, “Acoustic Emission Properties of Concrete on Dynamic Tensile Test,” Construction and Building Materials, Vol. 114, pp. 66–75, https://doi.org/10.1016/j.conbuildmat.2016.03.065

Gutkin, R., C.J. Green, S. Vangrattanachai, S.T. Pinho, P. Robinson, and P.T. Curtis, 2011, “On Acoustic Emission for Failure Investigation in CFRP: Pattern Recognition and Peak Frequency Analyses,” Mechanical Systems and Signal Processing, Vol. 25, No. 4, pp. 1393–1407, https://doi.org/10.1016/j.ymssp.2010.11.014

Jafari, S.M., H. Mehdigholi, and M. Behzad, 2014, “Value Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network,” Shock and Vibration, Vol. 2014, https://doi.org/10.1155/2014/823514

Jolliffe, I.T., 2002, Principal Component Analysis, second edition, Springer Series in Statistics

Kumar, A., and R. Kumar, 2017, “Least Square Fitting for Adaptive Wavelet Generation and Automatic Prediction of Defect Size in the Bearing Using Levenberg–Marquardt Backpropagation,” Journal of Nondestructive Evaluation, Vol. 36, No. 1, pp. 1–16, https://doi.org/10.1007/s10921-016-0385-1

Liu, X.X., Z.Z. Liang, Y.B. Zhang, X.Z. Wu, and Z.Y. Liao, 2015, “Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network,” Shock and Vibration, Vol. 2015, pp. 1–14, https://doi.org/10.1155/2015/846308

Nguyen, D., and B. Widrow, 1990, “Improving the Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of the Adaptive Weights,” Proceedings of 1990 IJCNN International Joint Conference on Neural Networks, 17-21 June, San Diego, CA, https://doi.org/10.1109/IJCNN.1990.137819

Omondi, B., D.G. Aggelis, H. Sol, and C. Sitters, 2016, “Improved Crack Monitoring in Structural Concrete by Combined Acoustic Emission and Digital Image Correlation Techniques,” Structural Health Monitoring, Vol. 15, No. 3, pp. 359–378, https://doi.org/10.1177/1475921716636806

Perez, S., M. Karakus, and E. Sepulveda, 2015, “A Preliminary Study on the Role of Acoustic Emission on Inferring Cerchar Abrasivity Index of Rocks Using Artificial Neural Network,” Wear, Vols. 344–345, pp. 1–8, https://doi.org/10.1016/j.wear.2015.10.006

Ran, Z.H., J.T. Qu, and F. He, 2011, Pattern Identification Theory and Its Application of Bridge Structural Damage Diagnosis, Beijing: Science Press, pp. 243–249

Sok, T., S.J. Hong, Y.K. Kim, and S.W. Lee, 2018, “Evaluation of Load Transfer Characteristics in Roller-Compacted Concrete Pavement,” International Journal of Pavement Engineering, Vol. 21, No. 6, pp. 796–804, https://doi.org/10.1080/10298436.2018.1511782

Su, H.Z., B. Ou, J.J. Tong, J. Hu, and Z.P. Wen, 2012, “Pattern Recognition Method for the Source Location of Acoustic Emission Generated During the Damage of Hydraulic Concrete,” Strain, Vol. 48, No. 6, pp. 482–490, https://doi.org/10.1111/j.1475-1305.2012.00845.x

Su, Y.Z., S.F. Yuan, and B.L. Zhang, 2009, “Impact Location of Composite Materials Based on Acoustic Emission and Neural Network,” Sensors and Microsystems, Vol. 28, No. 9, pp. 63–68

Sudha, J., S. Sampathkumar, and R.K. Kumar, 2011, “Condition Monitoring of Delamination during Drilling of GFRP Composites Using the Acoustic Emission Technique – A Neural Network Model,” Insight, Vol. 53, No. 8, pp. 445–449, https://doi.org/10.1784/insi.2011.53.8.445

Wang, X., H.P. Zhang, and X. Yan, 2008, “Correlation Analysis of Acoustic Emission Signal Parameters for Tensile Failure of PE-UHMW/PE-LD Laminates,” China Plastics, Vol. 22, No. 7, pp. 92–96

Wang, Y., S.J. Chen, S.J. Liu, and H.X. Hu, 2016, “Best Wavelet Basis for Wavelet Transforms in Acoustic Emission Signals of Concrete Damage Process,” Russian Journal of Nondestructive Testing, Vol. 52, pp. 125–131, https://doi.org/10.1134/S1061830916030104

Zhang, J.W., 2018, “Investigation of Relation between Fracture Scale and Acoustic Emission Time-Frequency Parameters in Rocks,” Shock and Vibration, Vol. 2018, https://doi.org/10.1155/2018/3057628

Zhao, Y.J., Z.G. Wang, C.M. Liu, J.Y. Kong, and B.Q. Han, 2013, “Damage Analysis of Refractories Based on Wavelet Energy Spectrum Coefficient,” Journal of Wuhan University of Science and Technology, Vol. 36, No. 1, pp. 32–35

 

Metrics
Usage Shares
Total Views
29 Page Views
Total Shares
0 Tweets
29
0 PDF Downloads
0
0 Facebook Shares
Total Usage
29