Heritage buildings are an important element of our urban environments, representing the hope and aspirations of a generation gone, reminding us of our achievements and our identity. When heritage buildings suffer damage, or fall into disrepair they are either met by one of two extremes; a bulldozer or painstaking repair. If the decision to conserve defeats the bulldozer, current heritage practice favours restoration into a mausoleum-type monument to yesteryear. But what if, rather than becoming a museum, these heritage buildings could live on and become a palimpsest of history? What if the damage was embraced and embodied in the repair? The Cathedral of the Blessed Sacrament on Barbadoes Street, Christchurch is the case study building for this thesis. Suffering damage in the Canterbury earthquakes of 2010 and 2011, the Cathedral sits in ruin waiting for decisions to be made around how it can be retained for future generations. This thesis will propose a reconstruction for the Cathedral through the analysis of precedent examples of reconstructing damaged heritage buildings and guided by a heritage framework proposed in this thesis. The employed process will be documented as an alternative method for reconstructing other damaged heritage buildings.
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.
