Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

Abstract It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.

[1]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  Emre Çomak,et al.  A decision support system based on support vector machines for diagnosis of the heart valve diseases , 2007, Comput. Biol. Medicine.

[5]  B. S. Wong,et al.  Ultrasonic evaluation of cement adhesion in wall tiles , 1996 .

[6]  S. K. Tso,et al.  Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis , 2006 .

[7]  Ruxu Du,et al.  Fault diagnosis using support vector machine with an application in sheet metal stamping operations , 2004 .

[8]  Michael Yit Lin Chew,et al.  Factors affecting ceramic tile adhesion for external cladding , 1999 .

[9]  Mel Siegel,et al.  Correlation of accelerometer and microphone data in the "coin tap test" , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[10]  Tong Feng,et al.  Application of evolutionary neural network in impact acoustics based nondestructive inspection of tile-wall , 2005, Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005..

[11]  S. K. Tso,et al.  Tile-wall bonding integrity inspection based on time-domain features of impact acoustics , 2006 .

[12]  Thomas G. Dietterich,et al.  Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs , 1991, AAAI.

[13]  Shiu Kit Tso,et al.  Wall inspection system for safety maintenance of high-rise buildings , 2007 .

[14]  A. Enis Çetin,et al.  Feasibility of impact-acoustic emissions for detection of damaged wheat kernels , 2007, Digit. Signal Process..

[15]  Shuo Yang,et al.  Nondestructive detection of weak joints in adhesively bonded composite structures , 2001 .

[16]  Masayasu Ohtsu,et al.  Quantitative Nondestructive evaluation of Defects in Concrete Surface Leyer by Impact Acoustics Methods Toshiro KAMADA Masanori ASANO, Minoru KUNIEDA and Keitetsu ROKUGO Discussion by Masayasu OHTSU , 2003 .

[17]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[18]  T. Uomoto,et al.  Nondestructive testing method of concrete using impact acoustics , 1997 .

[19]  Chi-Man Vong,et al.  Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference , 2006, Eng. Appl. Artif. Intell..