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Images for Damage; more images...

Images, eqnz.chch.2010

The September Canterbury earthquake. These pictures were taken of Colombo Street in Sydenham. A lot of masonry in this area has been damaged/fallen down. The New Zealand Army, along with Police, were minding the cordons. Note: these photos were taken on a cellphone; mind the quality.

Images, eqnz.chch.2010

The September Canterbury earthquake. These pictures were taken of Colombo Street in Sydenham. A lot of masonry in this area has been damaged/fallen down. A second hand dealer store. This is next to Penny Lane which had to relocate down the street. Note: these photos were taken on a cellphone; mind the quality.

Images, eqnz.chch.2010

The September Canterbury earthquake. These pictures were taken of Colombo Street in Sydenham. A lot of masonry in this area has been damaged/fallen down. The New Zealand Army, along with Police, were minding the cordons. Note: these photos were taken on a cellphone; mind the quality.

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

Damage to a house in Richmond. The brick wall is badly cracked and twisted, and some bricks have fallen, exposing the lining paper and framing below. The driveway is cracked and covered in liquefaction. The photographer comments, "These photos show our old house in River Rd and recovery work around Richmond and St Albans. More shaking damage on the east wall of the living room at our house".

Images, UC QuakeStudies

Damage to a house in Richmond. The brick wall is badly cracked and twisted, and some bricks have fallen, exposing the lining paper and framing below. The driveway is cracked and covered in liquefaction. The photographer comments, "These photos show our old house in River Rd and recovery work around Richmond and St Albans. More shaking damage on the east wall of the living room at our house".

Images, eqnz.chch.2010

The September Canterbury earthquake. These pictures were taken of Colombo Street in Sydenham. A lot of masonry in this area has been damaged/fallen down. Angus Donaldson copy service. This has moved down the road on Colombo Street. It is now sharing the premises with Penny Lane Records. Note: these photos were taken on a cellphone; mind the qu...

Research papers, University of Canterbury Library

The purpose of this research is to investigate men’s experiences of the 2016 7.8 magnitude Kaikōura earthquake and Tsunami. While, research into the impacts of the earthquake has been conducted, few studies have examined how gender shaped people’s experiences of this natural hazard event. Analysing disasters through a gender lens has significantly contributed to disaster scholarship in identifying the resilience and vulnerabilities of individuals and communities pre- and post-disaster (Fordham, 2012; Bradshaw, 2013). This research employs understandings of masculinities (Connell, 2005), to examine men’s strengths and challenges in responding, recovering, and coping following the earthquake. Qualitative inquiry was carried out in Northern Canterbury and Marlborough involving 18 face-to-face interviews with men who were impacted by the Kaikōura earthquake and its aftermath. Interview material is being analysed using thematic and narrative analysis. Some of the preliminary findings have shown that men took on voluntary roles in addition to their fulltime paid work resulting in long hours, poor sleep and little time spent with family. Some men assisted wives and children to high ground then drove into the tsunami zone to check on relatives or to help evacuate people. Although analysis of the findings is currently ongoing, preliminary findings have identified that the men who participated in the study have been negatively impacted by the 2016 Kaikōura earthquake. A theme identified amongst participants was an avoidance to seek support with the challenges they were experiencing due to the earthquake. The research findings align with key characteristics of masculinity, including demonstrating risky behaviours and neglecting self or professional care. This study suggests that these behaviours affect men’s overall resilience, and thus the resilience of the wider community.