Development of a Seismic Damage Prediction Model Using Machine Learning

Simple item page
dc.contributor.authorRoeslin, Samuel
dc.contributor.authorMa, Quincy
dc.contributor.authorElwood, Ken
dc.contributor.authorWicker, Jorg
dc.date.accessioned2018-09-11T23:06:47Z
dc.date.available2018-09-11T23:06:47Z
dc.date.issued2018en
dc.description.abstractNatural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.en
dc.identifier.urihttp://hdl.handle.net/10092/15977
dc.language.isoen
dc.rightsCC-BY 4.0 Internationalen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcode
dc.titleDevelopment of a Seismic Damage Prediction Model Using Machine Learningen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
POSTER - Roeslin_Development of a Seismic Damage Prediction Model Using Machine Learing.pdf
Size:
2.31 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
QuakeCORE: Posters