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Images, UC QuakeStudies

A photograph of a room inside a flat on Poplar Street taken during the Residential Access Project. The project gave residents temporary access within the red-zone cordon in order to retrieve items from their homes. The building's outer wall has crumbled leaving the room exposed.

Images, UC QuakeStudies

A photograph of a study area in a flat on Poplar Street taken during the Residential Access Project. The project gave residents temporary access within the red-zone cordon in order to retrieve items from their homes. Books and papers have been strewn across the ground.

Images, UC QuakeStudies

A photograph of a room inside a flat on Poplar Street taken during the Residential Access Project. The project gave residents temporary access within the red-zone cordon in order to retrieve items from their homes. The building's outer wall has crumbled leaving the room exposed.

Research papers, University of Canterbury Library

The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.