During the 2010/2011 Canterbury earthquakes, Reinforced Concrete Frame with Masonry Infill (RCFMI) buildings were subjected to significant lateral loads. A survey conducted by Christchurch City Council (CCC) and the Canterbury Earthquake Recovery Authority (CERA) documented 10,777 damaged buildings, which included building characteristics (building address, the number of storeys, the year of construction, and building use) and post-earthquake damage observations (building safety information, observed damage, level of damage, and current state of the buildings). This data was merged into the Canterbury Earthquake Building Assessment (CEBA) database and was utilised to generate empirical fragility curves using the lognormal distribution method. The proposed fragility curves were expected to provide a reliable estimation of the mean vulnerability for commercial RCFMI buildings in the region. http://www.13thcms.com/wp-content/uploads/2017/05/Symposium-Info-and-Presentation-Schedule.pdf VoR - Version of Record
The Evaluating Maternity Units (EMU) study is a mixed method project involving a prospective cohort study, surveys (two postnatal questionnaires) and focus groups. It is an Australasian project funded by the Australian Health and Medical Research Council. Its primary aim was to compare the birth outcomes of two groups of well women – one group who planned to give birth at a primary maternity unit, and a second group who planned to give birth at a tertiary hospital. The secondary aim was to learn about women’s views and experiences regarding their birthplace decision-making, transfer, maternity care and experiences, and any other issues they raised. The New Zealand arm of the study was carried out in Christchurch, and was seriously affected by the earthquakes, halting recruitment at 702 participants. Comprehensive details were collected from both midwives and women regarding antenatal and early labour changes of birthplace plans and perinatal transfers from the primary units to the tertiary hospital. Women were asked about how they felt about plan changes and transfers in the first survey, and they were discussed in some focus groups. The transfer findings are still being analysed and will be presented. This study is set within the local maternity context, is recent, relevant and robust. It provides midwives with contemporary information about transfers from New Zealand primary maternity units and women’s views and experiences. It may help inform the conversations midwives have with each other, and with women and their families/whānau, regarding the choices of birthplace for well childbearing women.
The susceptibility of precast hollow-core floors to sustain critical damage during an earthquake is now well-recognized throughout the structural engineering community in New Zealand. The lack of shear reinforcement in these floor units is one of the primary reasons causing issues with the seismic performance of these floors. Recent research has revealed that the unreinforced webs of these floor units can crack at drift demands as low as 0.6%. Such observation indicates that potentially many of the existing building stock incorporating hollow-core flooring systems in cities of relatively high seismic activity (e.g. Wellington and Christchurch) that probably have already experienced a level of shaking higher than 0.6% drift in previous earthquakes might already have their floor units cracked. However, there is little information available to reliably quantify the residual gravity load-carrying capacity of cracked hollow-core floor units, highlighting the need to understand the post-cracking behavior of hollow-core floor units to better quantify the extent of the risk that cracked hollow-core floor units pose.
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.
