Selection of input vectors to neural networks for structural damage identification

Yiqing Ni, B. S. Wang, J. M. Ko

Research output: Journal article publicationConference articleAcademic researchpeer-review

16 Citations (Scopus)


This paper addresses constructing appropriate input vectors (input patterns) to neural networks for hierarchical identification of damage location and extent from measured modal properties. Hierarchical use of neural networks is feasible for damage detection of large-scale civil structures such as cable-supported bridges and tall buildings. The neural network is first trained using one-level damage samples to locate the position of damage. After the damage location is determined, the network is re-trained by an incremental weight update method using additional samples corresponding to different damage degrees but only at the identified location. The re-trained network offers an accurate evaluation of the damage extent. The input vectors selected for this purpose fulfil the conditions: (a) most parameters of the input vectors are arguably independent of damage extent and only depend on damage location; (b) all parameters of the input vectors can be computed from several natural frequencies and a few incomplete modal vectors. The damage detection capacity of such constructed networks is experimentally verified on a steel frame with extent-unknown damage inflicted at its connections.
Original languageEnglish
Pages (from-to)270-280
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1 Jan 1999
EventProceedings of the 1999 Smart Structures and Materials - Smart Systems for Bridges, Structures, and Highways - Newport Beach, CA, United States
Duration: 1 Mar 19992 Mar 1999

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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