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Images for Post Earthquake; more images...

Research papers, University of Canterbury Library

The purpose of this study is to analyse the felt earthquake impacts, resilience and recovery of organizations in Canterbury by comparing three business sectors (accommodation/food services, Education/Training and Manufacturing). A survey of the three sectors in 2013 of Canterbury organizations impacted by the earthquakes revealed significant differences between the three sectors on felt earthquake impacts and resilience. On recovery and mitigation factors, the accommodation/food services sector is not significantly different from the other two sectors. Overall, the survey results presented here indicate that the Accommodation/Food Services sector was the least impacted by the earthquakes in comparison to the Education/Training and Manufacturing sectors. Implications for post-disaster management and recovery of the accommodation sector are suggested.

Research papers, University of Canterbury Library

After a high-intensity seismic event, inspections of structural damages need to be carried out as soon as possible in order to optimize the emergency management, as well as improving the recovery time. In the current practice, damage inspections are performed by an experienced engineer, who physically inspect the structures. This way of doing not only requires a significant amount of time and high skilled human resources, but also raises the concern about the inspector’s safety. A promising alternative is represented using new technologies, such as drones and artificial intelligence, which can perform part of the damage classification task. In fact, drones can safely access high hazard components of the structures: for instance, bridge piers or abutments, and perform the reconnaissance by using highresolution cameras. Furthermore, images can be automatically processed by machine learning algorithms, and damages detected. In this paper, the possibility of applying such technologies for inspecting New Zealand bridges is explored. Firstly, a machine-learning model for damage detection by performing image analysis is presented. Specifically, the algorithm was trained to recognize cracks in concrete members. A sensitivity analysis was carried out to evaluate the algorithm accuracy by using database images. Depending on the confidence level desired,i.e. by allowing a manual classification where the alghortim confidence is below a specific tolerance, the accuracy was found reaching up to 84.7%. In the second part, the model is applied to detect the damage observed on the Anzac Bridge (GPS coordinates -43.500865, 172.701138) in Christchurch by performing a drone reconnaissance. Reults show that the accuracy of the damage detection was equal to 88% and 63% for cracking and spalling, respectively.

Images, UC QuakeStudies

A photograph of an advertising sign reading, "No chimney for Santa? Don't worry he will use the door! Merry Christmas". The photograph is captioned by Paul Corliss, "Causeway hoarding, post earthquake".