Structural Damage Detection and Classification Algorithm Based on Artificial Immune Pattern Recognition

Dong Wei Zhang; Yong Ming Mao; Feng Long Kan; Nan Chen

doi:10.4028/www.scientific.net/AMR.945-949.1265

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 945-949Structural Damage Detection and Classification...

Structural Damage Detection and Classification Algorithm Based on Artificial Immune Pattern Recognition

Abstract:

This paper studies the structural damage detection and classification problems by using the artificial immune system which has the extremely powerful capabilities of adaptive and the bionic principle between learning and memory. We proposed an artificial immune pattern recognition and structural detection classification algorithm through imitating the immune recognition and learning mechanism. With the structure of benchmark, the damage detection and classification are tested. The simulation results show the classification rate is very well. The algorithm based on the immune learning and evolution can produce the high quality memory cells which effectively identify all kinds of structural damage model.

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Periodical:

Advanced Materials Research (Volumes 945-949)

Pages:

1265-1269

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.945-949.1265

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Online since:

June 2014

Authors:

Dong Wei Zhang*, Yong Ming Mao, Feng Long Kan, Nan Chen

Keywords:

Clone, Immune System, Structural Health Monitoring (SHM)

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References

[1] Sohn, H., Farrar, C.R.: Damage diagnosis using time series analysis of vibration signals. Smart Materials and Structures 10, 46–51 (2001).

DOI: 10.1088/0964-1726/10/3/304

Google Scholar

[2] Lee, J.J., Lee, J.W., Yi, J.H.: Neural networks-based damage detection for bridges considering errors in baseline finite element models. Journal of Sound and Vibration 280, 555–578 (2005).

DOI: 10.1016/j.jsv.2004.01.003

Google Scholar

[3] Bo, C., Chuanzhi, Z.: The Grid: Smart Sensor Phenomena, Technology, Networks, and Systems II, San Diego, California (2009).

Google Scholar

[4] Bo, C., Chuanzhi, Z.: Artificial immune pattern recognition for structure damage classification. Computers & Structures 87(21–22), 1394–1407 (2009).

DOI: 10.1016/j.compstruc.2009.08.012

Google Scholar

[5] Johnson, E.A.: A benchmark problem for structural health monitoring and damage detection. In: Proceedings of 14th Engineering Mechanics Conference, Austin, Texas (2000).

Google Scholar