The use of inverse methods for response estimation of long-span suspension bridges with uncertain wind loading conditions: Practical implementation and results for the Hardanger Bridge

Øyvind Wiig Petersen, Ole Oiseth, Eliz-Mari Lourens

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
10 Downloads (Pure)

Abstract

Structural health monitoring (SHM) seeks to assess the condition or behaviour of the structure from measurement data, which for long-span bridges typically are wind velocities and/or structural vibrations. However, in the assessment of the wind-induced response effects, models for the actual loads must be adopted, which introduces uncertainties. An alternative is to apply model-based inverse methods that consider the input forces unknown, and estimate these forces jointly together with the system states using limited vibration data. This article presents a case study of implementing Kalman-type inverse methods to a long-span suspension bridge in complex terrain, with the objective of estimating the full-field response. Previous studies have shown the local wind field is complicated, leading to uncertain load effects. We discuss the key challenges faced in the use of the methodology for the long-span bridges and present the results for a six hour storm event. The analysis show that the dynamic response contribution from the 14 lowermost bridge modes (up to 3 rad/s or 0.5 Hz) can be reconstructed with decent accuracy. The estimated response magnitude differs from the predicted response from design specifications, pointing to initial load model uncertainties that can be reduced to give greater confidence in the assessment of wind-induced fatigue, wind-resistant performance or other response effects.
Original languageEnglish
Pages (from-to)21-36
Number of pages16
JournalJournal of Civil Structural Health Monitoring
Volume9
Issue number1
DOIs
Publication statusE-pub ahead of print - 2018

Keywords

  • Suspension bridge
  • Structural monitoring
  • Inverse methods
  • Response estimation

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