In the use of parametric and nonparametric algorithms for the nondestructive evaluation of concrete structures

Research in Nondestructive Evaluation

Volumn 20, Issue 2, 2009, Pages 71-93

  Travassos, L ^a   Vieira, D A G ^c   Ida, N ^d   Nicolas, A ^b  

^a SENAI CIMATEC   (Brazil)

^b ECOLE CENTRALE DE LYON   (France)

^c FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^d UNIVERSITY OF AKRON   (United States)

Author keywords

Inverse problems; Neural networks; Nondestructive testing; Numerical modeling; Optimization; Particle; Swarm

Indexed keywords

BURIED CYLINDERS; COMPUTING TIME; CONCRETE STRUCTURES; DIELECTRIC CHARACTERISTICS; IMAGING METHOD; NON DESTRUCTIVE EVALUATION; NON DESTRUCTIVE TESTING; NON-PARAMETRIC ALGORITHM; NUMERICAL MODELING; OBJECT CHARACTERISTICS; PARTICLE; PARTICLE SWARM; PERFORMANCE PARAMETERS;

DIFFERENTIAL EQUATIONS; ELASTIC CONSTANTS; INVERSE PROBLEMS; NONDESTRUCTIVE EXAMINATION; OPTIMIZATION;

NEURAL NETWORKS;

EID: 70749161302     PISSN: 09349847     EISSN: 14322110     Source Type: Journal    
DOI: 10.1080/09349840802513242     Document Type: Article

References (19)

1. J. Gazdag and P. Sguazzero. Proc. IEEE 72:1302-1315 (1984).
2. A. J. Berkhout. Geophysics 46:1638-1656 (1981).
3. A. J. Berkhout. Seismic Migration-Imaging of Acoustic Energy by Wave Field Extrapolation-A. Theorectical Aspects. Elsevier, New York (1982).
4. C. J. Leuschen and R. G. Plumb. IEEE Trans. Geoscience and Remote Sensing 3:295-326 (2001).
5. D. J. Daniels. Ground Penetrating Radar. The institute of Electrical Engineers, London (2004).
6. GSSI Handbook For Radar Testing of Concrete. Published by Geophysical Survey Systems, Inc., North Salem, NH 13 (2001).
7. H. Jia, T. Takenaka, and T. Tanaka. Time-Domain Inverse Scattering Method for Cross-Borehole Radar Imaging. IEEE Trans on Geoscience and Remote Sensing 40(7):1640-1647 (2002).
8. R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. Proceedings of the Sixth International symposium on Micro Machine and Human Science pp. 39-43 (1995).
9. J. Kennedy and R. C. Eberhart. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks 4 1942-1948 (1995).
10. R. Demining and A. J. Devaney. Diffraction tomography for multimonostatic ground penetrating radar imaging. Inv. Probl. 13:29-45 (1997).
11. S. Caorsi and G. Cevini. Neural Networks Trained by Scattered Electromagnetic Data for GPR Applications. 2d International Workshop on Advanced GPR, 14-16 (2003).
12. S. Caorsi and G. Cevini. An Electromagnetic Approach Based on Neural Networks for the GPR Investigation of Buried Cylinders. IEEE Geoscience and Remote Sensing Letters 2:3-7 (2005).
13. W. M. Caminhas, D. A. G. Vieira, and J. A. Vasconcelos. Parallel Layer Perceptron. Neurocomputing 55:771-778 (2003).
14. J. S. R. Jang. ANFIS: Adaptative-Network-Based Fuzzy Inference Systems. IEEE Transactions on Systems, Man and Cybernetics 23:665-685 (1993).
15. Simon Haykin Adaptive filter theory (3rd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA (1996).
16. H. Ceylan, E. Tutumluer, and E. J. Barenberg. Artificial Neural Networks for Analyzing Concrete Airfield Pavements Serving the Boeing B-777 Aircraft. Journal of the Transportation Research Board 1684 110-117 (2000).
17. R. W. Meier Backcalculation of Flexible Pavement Moduli from Falling Weight Deflectometer Data Using Artificial Neural Networks. Ph.D. Dissertation, Georgia Institute of Technology, School of Civil and Environmental Engineering, Atlanta, March (1995).
18. T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning, Springer, Standford, CA. (2001).
19. V. N. Vapnik The Nature of Statistical Learning Theory (Statistics for Engineering and Information Science). Springer, Standford, CA. (2001).