A Review of the Research Progress of Structural Damage Identification Method Based on Computational Intelligence Techniques

This paper presents a comprehensive review of computational Intelligence (CI) technology applied in structural damage identification, clarifies the basic principles of computational intelligence techniques, as well as the applicable difficulties that exist in the field of structural damage identification (SDI) from 6 aspects: fuzzy theory, evidence theory, rough set theory, artificial neural networks, support vector machines and evolutionary computation, and then discussed the applicable prospects of computational Intelligence in SDI. It points out that the reasonable cross-fusion of a variety of CI method to specific research object is a necessary means for SDI research. For economy and practicality considerations, CI is suitable for highly integrated complex structural damage identification.

[1]  Shih-Lin Hung,et al.  Detection of structural damage via free vibration responses generated by approximating artificial neural networks , 2003 .

[2]  Leonard Ziemiański,et al.  Neural networks in mechanics of structures and materials – new results and prospects of applications , 2001 .

[3]  R. A. Saeed,et al.  3D fluid–structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS , 2013 .

[4]  Bo-Suk Yang,et al.  Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors , 2007, Expert Syst. Appl..

[5]  Hui Li,et al.  Structural damage identification based on integration of information fusion and shannon entropy , 2008 .

[6]  Yuan Shen-fang,et al.  Identification of impact damage based on FBG and SVM technology , 2010 .

[7]  Yongsheng Zhu,et al.  Bearing performance degradation assessment based on the rough support vector data description , 2013 .

[8]  Ali Nazari,et al.  Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS , 2012 .

[9]  Ranjan Ganguli,et al.  Genetic fuzzy system for damage detection in beams and helicopter rotor blades , 2003 .

[10]  Qiang Liu,et al.  Fuzzy pattern recognition of AE signals for grinding burn , 2005 .

[11]  S. M. Seyedpoor.  A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization , 2012 .

[12]  Yansong Diao,et al.  Structural damage identification based on information fusion technology , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.

[13]  Qinghua Hu,et al.  Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine , 2012 .

[14]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..

[15]  Jian Hua Zhao,et al.  Structural Damage Localization Using D-S Evidence Theory , 2011 .

[16]  Z. Lachiri,et al.  Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM , 2013 .

[17]  Manoj Kumar Tiwari,et al.  Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM , 2012, Expert Syst. Appl..

[18]  Amparo Alonso-Betanzos,et al.  Automatic bearing fault diagnosis based on one-class ν-SVM , 2013, Comput. Ind. Eng..

[19]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[20]  M. M. Reda Taha,et al.  Damage identification for structural health monitoring using fuzzy pattern recognition , 2005 .

[21]  Zhengliang Li,et al.  A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm , 2009 .

[22]  Yeong Min Kim,et al.  Rough set algorithm for crack category determination of reinforced concrete structures , 2009, Adv. Eng. Softw..

[23]  Lili Wang,et al.  A new method for studying the dynamic response and damage evolution of polymers at high strain rates , 2006 .

[24]  Chun-Hsien Chen,et al.  Concrete bridge deterioration diagnosis using fuzzy inference system , 2001 .

[25]  Michael J. Brennan,et al.  Structural damage detection by fuzzy clustering , 2008 .

[26]  Yanchun Liang,et al.  Multi-BP expert system for fault diagnosis of powersystem , 2013, Eng. Appl. Artif. Intell..

[27]  Karim Salahshoor,et al.  Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers , 2010 .

[28]  Yan Yun-ju.  DAMAGE PATTERNS RECOGNITION STUDY BASED ON ADAPTIVE PROBABILISTIC NEURAL NETWORK , 2008 .

[29]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[30]  Xiaohong Yuan,et al.  Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory , 2007, Inf. Fusion.

[31]  Matthew Zeidenberg,et al.  Neural networks in artificial intelligence , 1990, Ellis Horwood series in artificial intelligence.

[32]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[33]  Petros-Fotios Alvanitopoulos,et al.  Neuro-fuzzy techniques for the classification of earthquake damages in buildings , 2010 .

[35]  Chen Ning Cai,et al.  A GA Approach for Damage Localization Using Scattered Lamb Wave , 2013 .

[36]  Mathieu Feuilloy,et al.  Acoustic emission pattern recognition approach based on Hilbert–Huang transform for structural health monitoring in polymer-composite materials , 2013 .

[37]  Junjie Li,et al.  Damage detection based on improved particle swarm optimization using vibration data , 2012, Appl. Soft Comput..

[38]  Li Qiao.  The Static Damage Identification Method Based on the Support Vector Machine , 2010 .