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Using
GIS technology, Cornell researchers developed the largest US database
ever assembled of spatially distributed transient and permanent ground
deformation in conjunction with earthquake damage to water supply
lifelines. This research has helped substantially to delineate local
geotechnical and seismological hazards in the Los Angeles region that
are shown by zones of concentrated pipeline damage after the Northridge
earthquake. This research has been performed through MCEER, and resulted
in regressions between repair rates for different types of trunk and
distribution pipelines and various seismic parameters. The regressions
are statistically reliable and have improved predictive capabilities
compared with the default relationships currently used in loss estimation
programs (O'Rourke and Jeon, 2000). They will be referenced in the
next version of HAZUS software that implements the National Loss Estimation
Methodology sponsored by FEMA. The regressions and statistical databases
are being incorporated in pre-standards for estimating water supply
losses developed by the American Lifeline Alliance through ASCE under
contract with FEMA. The research has led to the discovery of a relationship
for visualizing damage patterns by linking the two dimensional representation
of local damage and the grid size used in GIS to analyze the spatial
distribution of data (O'Rourke, et al., 2001).
GIS research on visualizing damage patterns in pipeline networks
has been extended to buildings. Algorithms developed for pipelines
have been modified and validated to select optimal GIS mesh dimensions
and contour intervals for visualizing post-earthquake damage patterns
in buildings. Robust and statistically significant regressions have
been developed between the fraction of existing timber frame buildings
at any damage state and magnitudes of various seismic parameters
(O'Rourke and Jeon, 2002). Such work improves loss estimation significantly
and also creates advanced technology to visualize post-earthquake
damage patterns in buildings for rapid decision support and deployment
of emergency services.
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