A Novel Defect Evaluation Method for Concrete Structures in Infrared Based on ANN and PSO Algorithm

Abstract:

Article Preview

Abstract: The defect of concrete structures on material level can be expressed by defect depth and defect range. Infrared thermal imaging technology of concrete material defect detection is actually an inverse problem of heat transfer. On the basis of the current research achievements, infrared thermal imaging methods used in concrete structure defects was deduced to a multi-objective function optimization problem. Considering the traditional optimization algorithm slow convergence speed and local minima faults, this paper introduce particle swarm optimization algorithm (PSO) and the BP neural network to detect concrete material defect depth and range.PSO algorithm was used to optimize neural networks connection weights between layers and the network topology. The simulation test results are in good agreement with the experiment results and verify the validity of this method.

Info:

Periodical:

Key Engineering Materials (Volumes 439-440)

Edited by:

Yanwen Wu

Pages:

552-557

DOI:

10.4028/www.scientific.net/KEM.439-440.552

Citation:

B. L. Liang and Y. Tian, "A Novel Defect Evaluation Method for Concrete Structures in Infrared Based on ANN and PSO Algorithm", Key Engineering Materials, Vols. 439-440, pp. 552-557, 2010

Online since:

June 2010

Export:

Price:

$35.00

[1] Buyukozturk, O. (1998). Imaging of concrete structures., NDT&E International 31(4): 233-243.

[2] Lawrence A. Bliss Selecting Artificial Neural Network Inputs Using Particle Swarm Optimization, thesis for doctor degree, June (2003).

[3] Eberhart, R. and Kennedy, J. A New Optimizer Using Particles Swarm Theory, Proc. Sixth International Symposium on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ, pp.39-43. C, (1995).

DOI: 10.1109/mhs.1995.494215

[4] Eberhart, R. and Shi, Y. Comparison between Genetic Algorithms and Particle Swarm Optimization, Proceedings of the 7th International Conference on Evolutionary Programming VII, pp.611-616, (1998).

DOI: 10.1007/bfb0040812

[5] Mei Lin, W. L., Wang Yuwen (2002). A Novel Defect Evaluation Method in Infrared Based on Genetic Algorithm., ACTA OPTICA SINICA 22(12): 1452-1456. (in chinese).

[6] Favro L D, H. X., Kuo P K et al. (1995). Imaging the early time behavior reflected thermal-wave pulses., Proc. SPIE: 162-166.

[7] Favro L D, H. X., Kuo P K et al. (1996). Measuring defect depths by thermal-wave imaging., Proc. SIPE: 236-239.

[8] Vavilov V , G. E., Bison P G et al. (1996). Surface transient temperature inversion for hidden characterisation Theory and applications., Int .J. Heat Mass Transfer 39(2): 355-371.

DOI: 10.1016/0017-9310(95)00126-t

[9] X, M. (1993). Nondestructive evaluation of Materials by Infrared Thermography., London: Springer-Verlag: 1-173.

[10] Saeed Nojavan, F. -G. Y. (2006). Damage imaging of reinforced concrete structure using electromagnetic migration algorithm., International Journal of Solids and Structures 43(8): 5886-5908.

DOI: 10.1016/j.ijsolstr.2005.08.017

[11] Po-Liang Yeh, P. -L. L. (2009). Imaging of internal cracks in concrete structures using the surface rendering technique., NDT&E International 42(9): 181-187.

DOI: 10.1016/j.ndteint.2008.09.003

[12] Vavilov V , G. E., Bison P G et al. (1996). Surface transient temperature inversion for hidden characterisation Theory and applications., Int .J. Heat Mass Transfer 39(2): 355-371.

DOI: 10.1016/0017-9310(95)00126-t

[13] Chiang K-W, Noureldin A., and El-Sheimy N: A New Weights Updating Method for Neural Networks Based INS/GPS Integration Architectures;, Journal of Measurement Science and Technology, London, UK, V 15 (10), pp.2053-2061, October (2004).

DOI: 10.1088/0957-0233/15/10/015

[14] M.M. Reda Taha, A. N., N. El-Sheimy, N.G. Shrive (2003). Artificial neural networks for predicting creep with an example application to structural masonry., Canadian Journal of Civil Engineering 30(3).

DOI: 10.1139/l03-003

[15] Eberhart, R. a. K., J (1995). A New Optimizer Using Particles Swarm Theory., Proc. Sixth International Symposium on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ: 39-43.

[16] Shi Y, Eberhart R, A modified particle swarm optimizer [C], IEEE WorldCongress on Computational Intelligence, pp.69-73, (1998).

[17] Wang Suihua, Feng Aiqin, Li Aiguo , A BP Networks Learning Algorithm Based on PSO, Computer Engineering and Application, 13, 2003(in chinese).

[18] Chiang K-W, Noureldin A., and El-Sheimy N: A New Weights Updating Method for Neural Networks Based INS/GPS Integration Architectures;, Journal of Measurement Science and Technology, London, UK, V 15 (10), pp.2053-2061, October (2004).

DOI: 10.1088/0957-0233/15/10/015

In order to see related information, you need to Login.