DAMAGE IDENTIFICATION USING COMMITTEE OF NEURAL NETWORKS

This paper presents a committee of neural networks technique, which employs frequency response functions (FRFs), modal properties (natural frequencies and mode shapes), and wavelet transform (WT) data simultaneously to identify damage in structures. The experimental demonstration of the method is obtained by studying the sensitivities of the FRFs, modal properties, and WT data for four types of faults in a cylindrical shell. The experimental results show that different faults affect data in a different manner. The proposed approach is tested on simulated data from a three-degree-of-freedom mass-spring-damper system. The results from the simulated study show that the performance of the approach is not influenced by the noise in the data. Finally, the method is used to identify damage in a population of ten steel seam-welded cylindrical shells. The proposed method is able to identify damage cases better than the three approaches used individually. The committee approach gives results that generally have a lower mean square error (MSE) than the average MSE of the individual methods. It is found that the effectiveness of the method is enhanced when experimentally measured data are used, which is in contrast to many existing methods. This is because the committee approach assumes that the errors given by the three approaches are uncorrelated, a situation that becomes more apparent when using measured data rather than simulated data.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/07339399
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Marwala, T
• Publication Date: 2000-1

Language

• English

Media Info

• Features: Appendices; Figures; References; Tables;
• Pagination: p. 43-50
• Serial:
  • Journal of Engineering Mechanics
  • Volume: 126
  • Issue Number: 1
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-9399
  • EISSN: 1943-7889
  • Serial URL: http://ascelibrary.org/journal/jenmdt

Subject/Index Terms

• TRT Terms: Cylindrical bodies; Degrees of freedom; Dynamics; Experiments; Fault location; Frequency response; Integral transforms; Mechanics; Natural frequency; Neural networks; Sensitivity; Simulation; Steel structures; Structural deterioration and defects; Wavelets
• Uncontrolled Terms: Modal properties
• Subject Areas: Bridges and other structures; Design; Highways; I24: Design of Bridges and Retaining Walls;

Filing Info

• Accession Number: 00781820
• Record Type: Publication
• Contract Numbers: 59895410, CMS 9402196, CMS 95-03779
• Files: TRIS
• Created Date: Jan 27 2000 12:00AM