Application of Artificial Immune System in Structural Health Monitoring

A large number of methods have been proposed in the area of structural health monitoring (SHM). However, many of them rely on the prior knowledge of structural-parameter-values or the assumption that the structural-parameter-values do not change without damage. This dependence on specific parameter values limits these methods’ applicability. This paper proposes an artificial immune system- (AIS-) based approach for the civil structural health monitoring, which does not require specific parameter values to work. A linear three-floor structure model and a number of single-damage scenarios were used to evaluate the proposed method’s performance. The high success rate showed this approach’s great potential for the SHM tasks. This approach has merits of less dependence on the structural-parameter-values and low demand on the training conditions.

[1]  K.Lee Lerner,et al.  The Gale encyclopedia of science , 2004 .

[2]  P E Seiden,et al.  A model for simulating cognate recognition and response in the immune system. , 1992, Journal of theoretical biology.

[3]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[4]  Bo Chen,et al.  Artificial immune pattern recognition for structure damage classification , 2009 .

[5]  Vahid Johari Majd,et al.  Model updating of multistory shear buildings for simultaneous identification of mass, stiffness and damping matrices using two different soft-computing methods , 2011, Expert Syst. Appl..

[6]  Fernando Niño,et al.  Recent Advances in Artificial Immune Systems: Models and Applications , 2011, Appl. Soft Comput..

[7]  W. K Chiu,et al.  Structural Health Monitoring: Research and Applications , 2013 .

[8]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[9]  Zhuo Wang,et al.  Three-step method for stiffness identification of inter-story shearing structures under ambient excitation , 2009 .

[10]  Charles R. Farrar,et al.  The fundamental axioms of structural health monitoring , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[12]  Bo Chen,et al.  A hybrid immune model for unsupervised structural damage pattern recognition , 2011, Expert Syst. Appl..

[13]  B. R Ellis,et al.  Dynamic testing and stiffness evaluation of a six-storey timber framed building during construction , 2001 .

[14]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .