The city of Christchurch has experienced over 10,000 aftershocks since the 4th of September 2010 earthquake of which approximately 50 have been greater than magnitude 5. The damage caused to URM buildings in Christchurch over this sequence of earthquakes has been well documented. Due to the similarity in age and construction of URM buildings in Adelaide, South Australia and Christchurch (they are sister cities, of similar age and heritage), an investigation was conducted to learn lessons for Adelaide based on the Christchurch experience. To this end, the number of URM buildings in the central business districts of both cities, the extent of seismic strengthening that exists in both cities, and the relative earthquake hazards for both cities were considered. This paper will report on these findings and recommend strategies that the city of Adelaide could consider to significantly reduce the seismic risk posed by URM buildings in future earthquake.
This paper analyses the city of Christchurch, New Zealand, which has been through dramatic changes since it was struck by a series of earthquakes of different intensities between 2010 and 2011. The objective is to develop a deeper understanding of resilience by looking at changes in green and grey infrastructures. The study can be helpful to reveal a way of doing comparative analysis using resilience as a theoretical framework. In this way, it might be possible to assess the blueprint of future master plans by considering how important the interplay between green and grey infrastructure is for the resilience capacity of cities.
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.
