Search

found 5904 results

Images for earthquakes; more images...

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.

Articles, UC QuakeStudies

Canterbury Earthquakes Symposium - Social Recovery 101 – Waimakariri District Council's social recovery framework and lessons learnt from the Greater Christchurch earthquakes This panel discussion was presented by Sandra James, Director (Connecting People) The Canterbury Earthquakes Symposium, jointly hosted by the Department of the Prime Minister and Cabinet and the Christchurch City Council, was held on 29-30 November 2018 at the University of Canterbury in Christchurch. The purpose of the event was to share lessons from the Canterbury earthquakes so that New Zealand as a whole can be better prepared in future for any similar natural disasters. Speakers and presenters included Greater Christchurch Regeneration Minister, Hon Dr Megan Woods, Christchurch Mayor, Lianne Dalziel, Ngāi Tahu chief executive, Arihia Bennett, head of the public inquiry into EQC, Dame Sylvia Cartwright, urban planner specialising in disaster recovery and castrophe risk management, Dr Laurie Johnson; Christchurch NZ chief executive and former Press editor, Joanna Norris; academic researcher and designer, Barnaby Bennett; and filmmaker, Gerard Smyth. About 300 local and national participants from the public, private, voluntary sectors and academia attended the Symposium. They represented those involved in the Canterbury recovery effort, and also leaders of organisations that may be impacted by future disasters or involved in recovery efforts. The focus of the Symposium was on ensuring that we learn from the Canterbury experience and that we can apply those learnings.

Images, eqnz.chch.2010

Canterbury Brewery, St Asaph Street, Christchurch. File reference: CCL-2012-02-20-CanterburyBrewery-February-2012 DSC_144.JPG From the collection of Christchurch City Libraries.