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Images for Christchurch earthquake damage; more images...

Images, eqnz.chch.2010

Removal of the earthquake damaged footbridge over the Avon at Medway Street. Tuesday 12 February 2013. File reference: CCL-2013 -02-12-untitled4.bmp From the collection of Christchurch City Libraries.

Images, UC QuakeStudies

A photograph of the photocopy template for the Christchurch City Council's yellow sticker. The sticker was used by the Civil Defence after the 2010 and 2011 earthquakes to indicate that a building had been inspected and that structural damage or other safety hazards had been found. The sticker states that there should be no entry to the building, 'except on essential business'. It also states that 'earthquake aftershocks present danger' and that people who enter must do so at their own risk.

Research papers, University of Canterbury Library

As a result of the Canterbury earthquakes, over 60% of the concrete buildings in the Christchurch Central Business District have been demolished. This experience has highlighted the need to provide guidance on the residual capacity and repairability of earthquake-damaged concrete buildings. Experience from 2010 Chile indicates that it is possible to repair severely damaged concrete elements (see photo at right), although limited testing has been performed on such repaired components. The first phase of this project is focused on the performance of two lightly-reinforced concrete walls that are being repaired and re-tested after damage sustained during previous testing.

Images, UC QuakeStudies

A photograph of the platform for the Townsend Telescope amongst the rubble of the Observatory tower at the Christchurch Arts Centre. The tower collapsed during the 22 February 2011 earthquake, severely damaging the telescope.

Images, UC QuakeStudies

A photograph of ceramic pots decorated with a mosaic. The mosaic was made out of broken pieces from an earthquake-damaged ceramic handbag ornament.Crack'd for Christchurch comments, "Mosaic pots made by Fifi Colston from Wellington. The handbag is gone but the pots live on."

Research papers, The University of Auckland Library

The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.

Images, UC QuakeStudies

A photographs of members of a China Urban Search and Rescue team on Worcester Street near the Christchurch Art Gallery. The art gallery served as the temporary headquarters for Civil Defence after the 22 February 2011 earthquake. In the background, the earthquake damage to the dome of the Regent Theatre can be seen.

Images, UC QuakeStudies

A photograph of the earthquake damage to a building in central Christchurch. The basement of the building has collapsed and the concrete blocks have broken away from each other. The left corner of the building has also suffered damage, with many of the concrete blocks shaking loose.

Research papers, The University of Auckland Library

The sequence of earthquakes that has affected Christchurch and Canterbury since September 2010 has caused damage to a great number of buildings of all construction types. Following post-event damage surveys performed between April 2011 and June 2011, the damage suffered by unreinforced stone masonry buildings is reported and different types of observed failures are described. A detailed technical description of the most prevalently observed failure mechanisms is provided, with reference to recognised failure modes for unreinforced masonry structures. The observed performance of existing seismic retrofit interventions is also provided, as an understanding of the seismic response of these interventions is of fundamental importance for assessing the vulnerability of similar strengthening techniques when applied to unreinforced stone masonry structures.

Images, eqnz.chch.2010

The obligatory earthquake damage shot. Taken on Ilford Pan F+ with a Yashica-Mat 124G, developed in ID-11 for 8.5 minutes, printed on Ilford Multigrade IV RC, print developed in Ilford Universal PQ.

Images, UC QuakeStudies

A photograph of the earthquake damage to the Observatory tower at the Christchurch Arts Centre. The top two storeys of the tower collapsed during the 22 February 2011 earthquake and the rubble spilled into the courtyard in front. A digger was used to clear the rubble away from the building. A tarpaulin has been draped over the top of the tower.

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.

Research papers, The University of Auckland Library

High demolition rates were observed in New Zealand after the 2010-2011 Canterbury Earthquake Sequence despite the success of modern seismic design standards to achieve required performance objectives such as life safety and collapse prevention. Approximately 60% of the multi-storey reinforced concrete (RC) buildings in the Christchurch Central Business District were demolished after these earthquakes, even when only minor structural damage was present. Several factors influenced the decision of demolition instead of repair, one of them being the uncertainty of the seismic capacity of a damaged structure. To provide more insight into this topic, the investigation conducted in this thesis evaluated the residual capacity of moderately damaged RC walls and the effectiveness of repair techniques to restore the seismic performance of heavily damaged RC walls. The research outcome provided insights for developing guidelines for post-earthquake assessment of earthquake-damaged RC structures. The methodology used to conduct the investigation was through an experimental program divided into two phases. During the first phase, two walls were subjected to different types of pre-cyclic loading to represent the damaged condition from a prior earthquake, and a third wall represented a repair scenario with the damaged wall being repaired using epoxy injection and repair mortar after the pre-cyclic loading. Comparisons of these test walls to a control undamaged wall identified significant reductions in the stiffness of the damaged walls and a partial recovery in the wall stiffness achieved following epoxy injection. Visual damage that included distributed horizontal and diagonal cracks and spalling of the cover concrete did not affect the residual strength or displacement capacity of the walls. However, evidence of buckling of the longitudinal reinforcement during the pre-cyclic loading resulted in a slight reduction in strength recovery and a significant reduction in the displacement capacity of the damaged walls. Additional experimental programs from the literature were used to provide recommendations for modelling the response of moderately damaged RC walls and to identify a threshold that represented a potential reduction in the residual strength and displacement capacity of damaged RC walls in future earthquakes. The second phase of the experimental program conducted in this thesis addressed the replacement of concrete and reinforcing steel as repair techniques for heavily damaged RC walls. Two walls were repaired by replacing the damaged concrete and using welded connections to connect new reinforcing bars with existing bars. Different locations of the welded connections were investigated in the repaired walls to study the impact of these discontinuities at the critical section. No significant changes were observed in the stiffness, strength, and displacement capacity of the repaired walls compared to the benchmark undamaged wall. Differences in the local behaviour at the critical section were observed in one of the walls but did not impact the global response. The results of these two repaired walls were combined with other experimental programs found in the literature to assemble a database of repaired RC walls. Qualitative and quantitative analyses identified trends across various parameters, including wall types, damage before repair, and repair techniques implemented. The primary outcome of the database analysis was recommendations for concrete and reinforcing steel replacement to restore the strength and displacement capacity of heavily damaged RC walls.