Statistical assessment and prediction of damage from post-earthquake damage surveys

Theme: Natural & Biological Hazards

Primary Supervisor:

Ioannis Kosmidis

Statistical Science, UCL

Ioannis Kosmidis's Profile Picture

Secondary Supervisor:

Tiziana Rossetto

Civil, Environmental and Geomatic Engineering, UCL

Tiziana Rossetto's Profile Picture
Additional Supervisor(s):

Project Description:

Data from past earthquakes can be used to help design infrastructure that is resilient to earthquake damage. The data usually consist of post-event damage data from various surveys, excitation measures and structural characteristics.

These data can then be used to empirically assess the fragility of the buildings. However, current methods for empirical fragility assessment typically ignore measurement errors and do not make the best use of the event characteristics.

The core task of this project is the development of statistical models that can relate post-earthquake damage data both to structural characteristics of buildings and to incompletely observed spatial and functional characteristics of the event, and can account for measurement error.

The aim is to use such models for the objective assessment and prediction of seismic damage, and the quantification of the uncertainty in the predictions. The work is expected to also feedback on guidelines for post-event data collection.

Policy Impact of Research:

This project aims to develop a statistical modelling framework and software solutions for empirical fragility assessment from post-earthquake data.

This work is expected to directly impact the re-insurance industry, assist reconnaissance organisations, and contribute to earthquake engineering.

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