Improvements on Mapping Soil Liquefaction at a Regional Scale
Zhu, Jing.
2017
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Abstract: Earthquake
induced soil liquefaction is an important secondary hazard during earthquakes and can
lead to significant damage to infrastructure. Mapping liquefaction hazard is important
in both planning for earthquake events and guiding relief efforts by positioning
resources once the events have occurred. This dissertation addresses two aspects of
liquefaction hazard mapping at a ... read moreregional scale including 1) predictive liquefaction
hazard mapping and 2) post-liquefaction cataloging. First, current predictive hazard
liquefaction mapping relies on detailed geologic maps and geotechnical data, which are
not always available in at-risk regions. This dissertation improves the predictive
liquefaction hazard mapping by the development and validation of geospatial liquefaction
models (Chapter 2 and 3) that predict liquefaction extent and are appropriate for global
application. The geospatial liquefaction models are developed using logistic regression
from a liquefaction database consisting of the data from 27 earthquake events from six
countries. The model that performs best over the entire dataset includes peak ground
velocity (PGV), VS30, distance to river, distance to coast, and precipitation. The model
that performs best over the noncoastal dataset includes PGV, VS30, water table depth,
distance to water body, and precipitation. Second, post-earthquake liquefaction
cataloging historically relies on field investigation that is often limited by time and
expense, and therefore results in limited and incomplete liquefaction inventories. This
dissertation improves the post-earthquake cataloging by the development and validation
of a remote sensing-based method that can be quickly applied over a broad region after
an earthquake and provide a detailed map of liquefaction surface effects (Chapter 4).
Our method uses the optical satellite images before and after an earthquake event from
the WorldView-2 satellite with 2 m spatial resolution and eight spectral bands. Our
method uses the changes of spectral variables that are sensitive to surface moisture and
soil characteristics paired with a supervised
classification.
Thesis (Ph.D.)--Tufts University, 2017.
Submitted to the Dept. of Civil Engineering.
Advisor: Laurie Baise.
Committee: Elena Naumova, Magaly Koch, Richard Vogel, and Eric Thompson.
Keywords: Civil engineering, and Remote sensing.read less - ID:
- 76537d01j
- Component ID:
- tufts:20685
- To Cite:
- TARC Citation Guide EndNote
