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Research papers, The University of Auckland Library

Two days after the 22 February 2011 M6.3 earthquake in Christchurch, New Zealand, three of the authors conducted a transect of the central city, with the goal of deriving an estimate of building damage levels. Although smaller in magnitude than the M7.1 4 September 2010 Darfield earthquake, the ground accelerations, ground deformation and damage levels in Christchurch central city were more severe in February 2011, and the central city was closed down to the general public. Written and photographic notes of 295 buildings were taken, including construction type, damage level, and whether the building would likely need to be demolished. The results of the transect compared favourably to Civil Defence rapid assessments made over the following month. Now, more than one year and two major aftershocks after the February 2011 earthquake these initial estimates are compared to the current demolition status to provide an updated understanding of the state of central Christchurch.

Images, UC QuakeStudies

A Civil Defence staff member completing a Level 1 Rapid Assessment inspection form for a damaged house. Some of the brickwork has collapsed from the outer wall and the awnings over the windows have collapsed.

Images, UC QuakeStudies

A Civil Defence staff member completing a Level 1 Rapid Assessment inspection form for a damaged house. Some of the brickwork has collapsed from the outer wall of the house and the awnings over the windows have collapsed.

Research papers, The University of Auckland Library

Ingham and Biggs were in Christchurch during the M6.3, 22 February 2011 earthquake and Moon arrived the next day. They were enlisted by officials to provide rapid assessment of buildings within the Central Business District (CBD). In addition, they were asked to: 1) provide a rapid assessment of the numbers and types of buildings that had been damaged, and 2) identify indicator buildings that represent classes of structures that can be used to monitor changing conditions for each class following continuing aftershocks and subsequent damage. This paper explains how transect methodology was incorporated into the rapid damage assessment that was performed 48 hours after the earthquake. Approximately 300 buildings were assessed using exterior Level 1 reporting techniques. That data was used to draw conclusions on the condition of the entire CBD of approximately 4400 buildings. In the context of a disaster investigation, a transect involves traveling a selected path assessing the condition of the buildings and documenting the class of each building, and using the results in conjunction with prior knowledge relating to the overall population of buildings affected in the area of the study. Read More: http://ascelibrary.org/doi/abs/10.1061/9780784412640.033

Research papers, The University of Auckland Library

The paper proposes a simple method for quick post-earthquake assessment of damage and condition of a stock of bridges in a transportation network using seismic data recorded by a strong motion array. The first part of the paper is concerned with using existing free field strong motion recorders to predict peak ground acceleration (PGA) at an arbitrary bridge site. Two methods are developed using artificial neural networks (a single network and a committee of neural networks) considering influential parameters, such as seismic magnitude, hypocentral depth and epicentral distance. The efficiency of the proposed method is explored using actual strong motion records from the devastating 2010 Darfield and 2011 Christchurch earthquakes in New Zealand. In the second part, two simple ideas are outlined how to infer the likely damage to a bridge using either the predicted PGA and seismic design spectrum, or a broader set of seismic metrics, structural parameters and damage indices.

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.