4.6 Article

EEFIT mission to Haiti following the 12th January 2010 earthquake

Journal

BULLETIN OF EARTHQUAKE ENGINEERING
Volume 11, Issue 1, Pages 35-68

Publisher

SPRINGER
DOI: 10.1007/s10518-012-9361-z

Keywords

Aerial imaging; Damage assessment; Earthquake; Field visit; Liquefaction; Satellite imagery

Funding

  1. AIR Worldwide
  2. Arup
  3. Aon Benfield
  4. British Geological Survey
  5. CREA Consultants
  6. Malishev Wilson Engineers
  7. Risk Management Solutions
  8. Sellafield Ltd
  9. EPSRC [EP/I007334/1] Funding Source: UKRI
  10. Engineering and Physical Sciences Research Council [EP/I007334/1] Funding Source: researchfish

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The M (w) = 7.2 Haiti earthquake of 12th January 2010 caused extensive damage to buildings and other infrastructure in the epicentral region in and around Port-au-Prince. The Earthquake Engineering Field Investigation Team (EEFIT), which is based in the United Kingdom, organised a field mission to Haiti with the authors as the team members. The paper presents the geotechnical findings of the team including those relating to soil liquefaction and lateral spreading and discusses the performance of buildings, including historic buildings, and bridges. Unprecedented use was made of damage assessments made from remote images (i.e. images taken from satellites and aircraft) when planning the post-earthquake relief effort in Haiti and a principal objective of the team was to evaluate the accuracy of such assessments. Accordingly, 142 buildings in Port-au-Prince were inspected in the field by the EEFIT team; damage assessments had previously been made using remote images for all these buildings. On the basis of this survey, the tendency of remote assessments to underestimate damage was confirmed; it was found that the underestimate applied to assessments based on oblique images using the relatively new technique of Pictometry, as well as those based on vertical images, although to a lesser degree. The paper also discusses the distribution of damage in Port-au-Prince, which was found to be strongly clustered in ways that appear not to have been completely explained.

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