Improving Knowledge of Structural System Behavior through Multiple Models

A system identification and model updating methodology that accounts for factors influencing the reliability of identification is proposed. An important aspect of this methodology is the generation of a population of candidate models. This paper presents an analysis of error sources that are used to define model populations. A case study illustrates the need for such an approach even when a single conservative model has been appropriate for design. Data mining techniques such as principal component analysis and k-means clustering combined to interpret model predictions. These methods are useful for estimating the dependability of system identification.

• Record URL:
  • https://doi.org/10.1061/(ASCE)0733-9445(2008)134:4(553)
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/07339445
• Authors:
  • Smith, Ian F C
  • Saitta, Sandro
• Publication Date: 2008-4

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 553-561
• Serial:
  • Journal of Structural Engineering
  • Volume: 134
  • Issue Number: 4
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-9445
  • Serial URL: http://ascelibrary.org/loi/jsendh

Subject/Index Terms

• TRT Terms: Case studies; Data mining; Mathematical models; Measurement; Structural analysis
• Subject Areas: Bridges and other structures; Highways; I20: Design and Planning of Transport Infrastructure;

Filing Info

• Accession Number: 01099113
• Record Type: Publication
• Files: TRIS
• Created Date: May 21 2008 7:04AM