Hierarchical development of training database for artificial neural network-based damage identification

Lin Ye, Zhongqing Su, Chunhui Yang, Zhihao He, Xiaoming Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


Though serving as an effective means for damage identification, the capability of an artificial neural network (ANN) for quantitative prediction is substantially dependent on the amount of training data. In virtue of a concept of "Digital Damage Fingerprints" (DDF), a hierarchical approach for the development of training databases was proposed for ANN-based damage identification. With the object of exploiting the capability of ANN to address the key questions: "Is there damage?" and "Where is the damage?", the amount of training data (damage cases) was increased progressively. Mutuality was established between the quantity of training data and the accuracy of answers to the two questions of interest, and was experimentally validated by identifying the position of actual damage in carbon fibre-reinforced composite laminates. The results demonstrate that such a hierarchical approach is capable of offering prediction as to the presence and location of damage individually, with substantially reduced computational cost and effort in the development of the ANN training database.
Original languageEnglish
Pages (from-to)224-233
Number of pages10
JournalComposite Structures
Issue number3
Publication statusPublished - 1 Nov 2006
Externally publishedYes


  • Artificial neural network
  • Composite structure
  • Damage identification
  • Training database

ASJC Scopus subject areas

  • Ceramics and Composites
  • Civil and Structural Engineering


Dive into the research topics of 'Hierarchical development of training database for artificial neural network-based damage identification'. Together they form a unique fingerprint.

Cite this