dc.contributor.advisor |
Ma, Quincy |
|
dc.contributor.author |
Roeslin, Samuel |
|
dc.date.accessioned |
2021-10-21T00:19:50Z |
|
dc.date.available |
2021-10-21T00:19:50Z |
|
dc.date.issued |
2021 |
en |
dc.identifier.uri |
https://hdl.handle.net/2292/57074 |
|
dc.description.abstract |
This thesis presents the application of data science techniques, especially machine
learning, for the development of seismic damage and loss prediction models for
residential buildings. Current post-earthquake building damage evaluation forms are
developed for a particular country in mind. The lack of consistency hinders the
comparison of building damage between different regions. A new paper form has been
developed to address the need for a global universal methodology for post-earthquake
building damage assessment. The form was successfully trialled in the street ‘La Morena’
in Mexico City following the 2017 Puebla earthquake.
Aside from developing a framework for better input data for performance based
earthquake engineering, this project also extended current techniques to derive
insights from post-earthquake observations. Machine learning (ML) was applied to
seismic damage data of residential buildings in Mexico City following the 2017
Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake
sequence (CES). The experience showcased that it is readily possible to develop
empirical data only driven models that can successfully identify key damage drivers and
hidden underlying correlations without prior engineering knowledge. With adequate
maintenance, such models have the potential to be rapidly and easily updated to allow
improved damage and loss prediction accuracy and greater ability for models to be
generalised.
For ML models developed for the key events of the CES, the model trained using
data from the 22 February 2011 event generalised the best for loss prediction. This is
thought to be because of the large number of instances available for this event and the
relatively limited class imbalance between the categories of the target attribute. For the
CES, ML highlighted the importance of peak ground acceleration (PGA), building age,
building size, liquefaction occurrence, and soil conditions as main factors which affected
the losses in residential buildings in Christchurch. ML also highlighted the influence of
liquefaction on the buildings losses related to the 22 February 2011 event.
Further to the ML model development, the application of post-hoc methodologies
was shown to be an effective way to derive insights for ML algorithms that are not
intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’
ML models. |
|
dc.publisher |
ResearchSpace@Auckland |
en |
dc.relation.ispartof |
PhD Thesis - University of Auckland |
en |
dc.relation.isreferencedby |
UoA |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ |
|
dc.title |
Predicting Seismic Damage and Loss for Residential Buildings using Data Science |
|
dc.type |
Thesis |
en |
thesis.degree.discipline |
Civil Engineering |
|
thesis.degree.grantor |
The University of Auckland |
en |
thesis.degree.level |
Doctoral |
en |
thesis.degree.name |
PhD |
en |
dc.date.updated |
2021-09-03T05:36:07Z |
|
dc.rights.holder |
Copyright: The author |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
dc.identifier.wikidata |
Q112956561 |
|