A simple and rapid approach to post-earthquake assessment of bridge condition and damage

Ramhormozian Shahab, Piotr Omenzetter, Rolando Orense

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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The paper proposes a simple method for quick post-earthquake assessment of damage and condition of a stock of bridges in a transportation network using seismic data recorded by a strong motion array. The first part of the paper is concerned with using existing free field strong motion recorders to predict peak ground acceleration (PGA) at an arbitrary bridge site. Two methods are developed using artificial neural networks (a single network and a committee of neural networks) considering influential parameters, such as seismic magnitude,
hypocentral depth and epicentral distance. The efficiency of the proposed method is explored using actual strong motion records from the devastating 2010 Darfield and 2011 Christchurch earthquakes in New Zealand. In the second part, two simple ideas are outlined how to infer the likely damage to a bridge using either the predicted PGA and seismic design spectrum, or a broader set of seismic metrics, structural parameters and damage indices.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Integrity, Reliability and Failure
Number of pages9
Publication statusPublished - 23 Jun 2013
Event4th International Conference on Integrity, Reliability & Failure - Funchal, Portugal
Duration: 23 Jun 201327 Jun 2013


Conference4th International Conference on Integrity, Reliability & Failure

Bibliographical note

The authors would like to express their gratitude to their supporters. Research work at the University of Auckland was supported by the National Hazards Platform grant UAOM11/15- 4.3. Piotr Omenzetter’s work within The LRF Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by The Lloyd's Register Foundation (The LRF). The LRF supports the advancement of engineering-related education, and funds research and development that enhances safety of life at sea, on land and in the air.


  • bridges
  • structural health monitoring
  • condition assessment
  • damage assessment
  • peak ground acceleration
  • artificial neural networks


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