As part of the 'Project Masonry' Recovery Project funded by the New Zealand Natural Hazards Research Platform, commencing in March 2011, an international team of researchers was deployed to document and interpret the observed earthquake damage to masonry buildings and to churches as a result of the 22nd February 2011 Christchurch earthquake. The study focused on investigating commonly encountered failure patterns and collapse mechanisms. A brief summary of activities undertaken is presented, detailing the observations that were made on the performance of and the deficiencies that contributed to the damage to approximately 650 inspected unreinforced clay brick masonry (URM) buildings, to 90 unreinforced stone masonry buildings, to 342 reinforced concrete masonry (RCM) buildings, to 112 churches in the Canterbury region, and to just under 1100 residential dwellings having external masonry veneer cladding. In addition, details are provided of retrofit techniques that were implemented within relevant Christchurch URM buildings prior to the 22nd February earthquake and brief suggestions are provided regarding appropriate seismic retrofit and remediation techniques for stone masonry buildings. http://www.nzsee.org.nz/publications/nzsee-quarterly-bulletin/
The author followed five primary (elementary) schools over three years as they responded to and began to recover from the 2010–2011 earthquakes in and around the city of Christchurch in the Canterbury region of New Zealand. The purpose was to capture the stories for the schools themselves, their communities, and for New Zealand’s historical records. From the wider study, data from the qualitative interviews highlighted themes such as children’s responses or the changing roles of principals and teachers. The theme discussed in this article, however, is the role that schools played in the provision of facilities and services to meet (a) physical needs (food, water, shelter, and safety); and (b) emotional, social, and psychological needs (communication, emotional support, psychological counseling, and social cohesion)—both for themselves and their wider communities. The role schools played is examined across the immediate, short-, medium-, and long-term response periods before being discussed through a social bonding theoretical lens. The article concludes by recommending stronger engagement with schools when considering disaster policy, planning, and preparation http://www.schoolcommunitynetwork.org/SCJ.aspx
The objective of the study presented herein is to assess three commonly used CPT-based liquefaction evaluation procedures and three liquefaction severity index frameworks using data from the 2010–2011 Canterbury earthquake sequence. Specifically, post-event field observations, ground motion recordings, and results from a recently completed extensive geotechnical site investigation programme at selected strong motion stations (SMSs) in the city of Christchurch and surrounding towns are used herein. Unlike similar studies that used data from free-field sites, accelerogram characteristics at the SMS locations can be used to assess the performance of liquefaction evaluation procedures prior to their use in the computation of surficial manifestation severity indices. Results from this study indicate that for cases with evidence of liquefaction triggering in the accelerograms, the majority of liquefaction evaluation procedures yielded correct predictions, regardless of whether surficial manifestation of liquefaction was evident or not. For cases with no evidence of liquefaction in the accelerograms (and no observed surficial evidence of liquefaction triggering), the majority of liquefaction evaluation procedures predicted liquefaction was triggered. When all cases are used to assess the performance of liquefaction severity index frameworks, a poor correlation is shown between the observed severity of liquefaction surface manifestation and the calculated severity indices. However, only using those cases where the liquefaction evaluation procedures yielded correct predictions, there is an improvement in the correlation, with the Liquefaction Severity Number (LSN) being the best performing of the frameworks investigated herein. However scatter in the relationship between the observed and calculated surficial manifestation still remains for all liquefaction severity index frameworks.
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.
