An automatic recognition algorithm for GPR images of RC structure voids

Journal of Applied Geophysics

Volumn 99, Issue , 2013, Pages 125-134

  Xie, Xiongyao ^a   Qin, Hui ^a   Yu, Chao ^b   Liu, Lanbo ^c  

^a TONGJI UNIVERSITY   (China)

^b Shanghai Construction Engineering Administration Co Ltd   (China)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Automatic recognition; Forward simulation; Ground penetrating radar (GPR); Reinforced concrete (RC) structure; Support vector machine (SVM); Void

Indexed keywords

AUTOMATIC IDENTIFICATION; AUTOMATIC RECOGNITION; EXTRACTING FEATURES; FORWARD SIMULATION; GROUND PENETRATING RADAR (GPR); PREDICTIVE DECONVOLUTION; SUPPORT VECTOR MACHINE ALGORITHM; VOID;

AUTOMATION; BARS (METAL); CONCRETES; FINITE DIFFERENCE TIME DOMAIN METHOD; GEOLOGICAL SURVEYS; GROUND PENETRATING RADAR SYSTEMS; REINFORCED CONCRETE; TOOLS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTIFICIAL INTELLIGENCE; CONCRETE STRUCTURE; FORWARD MODELING; GROUND PENETRATING RADAR; REINFORCED CONCRETE; VOID;

EID: 84888027009     PISSN: 09269851     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jappgeo.2013.02.016     Document Type: Article

References (23)

1. Cheriet M., Ayat N.E., Suen C.Y. Automatic model selection for the optimization of SVM kernels. Pattern Recognition 2005, 38:1733-1745.
2. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20:273-297.
3. Cui Y.A., Wang L., Xiao J.P. Automatic feature recognition for GPR image processing. World Academy of Science, Engineering and Technology 2010, 61:176-179.
4. Diamanti N., Redman D. Field observations and numerical models of GPR response from vertical pavement cracks. Journal of Applied Geophysics 2012, 81:106-116.
5. Ehret B. Pattern recognition of geophysical data. Geoderma 2010, 160:111-125.
6. Giannopoulos A. Modelling ground penetrating radar by GPRMAX. Construction and Building Materials 2005, 19:755-762.
7. Kadioglu S., Daniels J.J. 3D visualization of integrated ground penetrating radar data and EM-61 data to determine buried objects and their characteristics. Journal of Geophysics and Engineering 2008, 5:448-456.
8. Kadioglu S., Ulugergerli E.U. Imaging karstic cavities in transparent 3D volume of the GPR data set in Akkopru dam, Mugla, Turkey. Nondestructive Testing and Evaluation 2012, 27:263-271.
9. Kim J.H., Cho S.J., Yi M.J. Removal of ringing noise in GPR data by signal processing. Geosciences Journal 2007, 11:75-81.
10. Krzyzak A. Nonlinear function learning and classification using RBF networks with optimal kernels. Nonlinear Estimation and Classification 2003, 171:393-404.
11. Li C.H., Lin C.T., Kuo B.C. An automatic method for selecting the parameter of the RBF kernel function to support vector machines. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, July 25-30, 2010, Honolulu, USA 2010, 836-839. K. Germain, P. Smits (Eds.).
12. Moreno-Torres J.G., Saez J.A., Herrera F. Study on the impact of partition-induced dataset shift on k-fold cross-validation. IEEE Transactions on Neural Networks and Learning Systems 2012, 23:1304-1312.
13. Pasolli E., Melgani F., Donelli M. Automatic analysis of GPR images: a pattern-recognition approach. Geoscience and Remote Sensing 2009, 47:2206-2217.
14. Porsani J.L., Sauck W.A. Ground-penetrating radar profiles over multiple steel tanks: artifact removal through effective data processing. Geophysics 2007, 72:J77-J83.
15. Shao W., Bouzerdoum A., Phung S.L. Automatic classification of GPR signals. Proceedings of the 13th International Conference on Ground Penetrating Radar, June 21-25 2010, Leece, Italy 2010, 738-743. L. Crocco, L. Orlando, R. Persico, M. Pieraccini (Eds.).
16. Shihab S., Al-Nuaimy W., Huang Y. Neural network target identifier based on statistical features of GPR signals. Proceedings of the 9th International Conference on Ground Penetrating Radar, April 29-May 2, Santa Barbara, California USA 2002, 135-138. S. Koppenjan (Ed.).
17. Solla M., Lorenzo H., Rial F.I. Ground-penetrating radar for the structural evaluation of masonry bridges: results and interpretational tools. Construction and Building Materials 2012, 29:458-465.
18. Wang T., Ye X.F., Wang L. Grid search optimized SVM method for dish-like underwater robot attitude prediction. Proceedings of 5th International Joint Conference on Computational Sciences and Optimization, June 23-26, 2012, Harbin, Heilongjiang, China 2012, 839-843. L. Yu, Y. Zhao, Y. Hao, S. Wang (Eds.).
19. Wu J.B., Tian M., Zhou H.L. Feature extraction and recognition based on SVM. Proceedings of 4th International Conference on Wireless Communications, Networking and Mobile Computing, October 12-1, 2008, Dalian, China 2008, 1-4. H. Zhou (Ed.).
20. Wu J.B., Tian M., Ling Y.T. Feature extraction and recognition of landmine. 5th International Conference on Wireless Communications, Networking and Mobile Computing, Sept. 24-26, 2009, Beijing, China 2009, 1-4. Y. Zhang (Ed.).
21. Xie X.Y., Liu Y.J., Huang H.W., Du J., Zhang F.S., Liu L.B. Evaluation of grout behind the lining of shield tunnels using ground-penetrating radar in the Shanghai Metro Line, China. Journal of Geophysics and Engineering 2007, 4:253-261.
22. Zhang J.H. Optimization of kernel function parameters SVM Based on the GA. Materials Science and Information Technology 2012, 433:4124-4128.
23. Zhang F.S., Xie X.Y., Huang H.W. Application of ground penetrating radar in grouting evaluation for shield tunnel construction. Tunnelling and Underground Space Technology 2010, 25:99-107.