Peter Majende, artist. Madras Street Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4068 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3987 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3959 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3993 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3956 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3969 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3953 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4062 From the collection of Christchurch City Libraries.
Natural 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.
A photograph of the earthquake damage to a block of shops on Manchester Street.
A photograph of waterproof sheets covering parts of the earthquake-damaged Cranmer Courts building.
A photograph of a power pole on Highfield Road that shifted during the earthquake.
Residents in a hercules being evacuated from Christchurch after the 22 February 2011 earthquake.
An Air Force craft bringing support to Christchurch after the 22 February 2011 earthquake.
A camera operator filming Prime Minister John Key's briefing following the 22 February earthquake.
An aerial view of Hagley Park a week after the 22 February 2011 earthquake.
An aerial photograph of Christchurch following the 4 September earthquake, taken from a helicopter.
A photograph of the earthquake damage to a building in the Christchurch central city.
A photograph of an earthquake-damaged brick building, with demolition equipment in the foreground.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.
An aerial photograph of Canterbury following the 4 September earthquake, taken from a helicopter.