Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3957 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4032 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4047 From the collection of Christchurch City Libraries.
Peter Majende, artist. Madras Street Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4063 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22- IMG_4011 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3986 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3952 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_3955 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4087 From the collection of Christchurch City Libraries.
Peter Majende, artist. Madras Street Friday 22 February 2013. File reference: CCL-2013 -02-22- IMG_4064 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4060 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4075 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4028 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4049 From the collection of Christchurch City Libraries.
Friday 22 February 2013. File reference: CCL-2013 -02-22-IMG_4079 From the collection of Christchurch City Libraries.
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 first page of a copy of a Level 1 Rapid Assessment Form. The form was used by the Civil Defence to document the earthquake damage to buildings in central Christchurch after the 22 February 2011 earthquake.
A photograph of the second page of a copy of a Level 2 Rapid Assessment Form. The form was used by the Civil Defence to document the earthquake damage to buildings in central Christchurch after the 22 February 2011 earthquake.
A photograph of the third page of a copy of a Level 2 Rapid Assessment Form. The form was used by the Civil Defence to document the earthquake damage to buildings in central Christchurch after the 22 February 2011 earthquake.
A photograph of the first page of a copy of a Level 2 Rapid Assessment Form. The form was used by the Civil Defence to document the earthquake damage to buildings in central Christchurch after the 22 February 2011 earthquake.
Part 1 of a video contributed by Henry Allison, a participant in the Understanding Place research project. The video has the description "Henry Allison talks about his experiences at the brewery on St Asaph Street during the earthquakes, and about the architecture that was lost in the central city".
A story submitted by Lynette Evans to the QuakeStories website.