Room to Breathe: Designing a framework for medium density housing in Ōtaut…

Research papers, Victoria University of Wellington

<b>Ōtautahi-Christchurch faces the future in an enviable position. Compared to other New Zealand cities Christchurch has lower housing costs, less congestion, and a brand-new central city emerging from the rubble of the 2011 earthquakes. ‘Room to Breathe: designing a framework for medium density housing (MDH) in Ōtautahi-Christchurch’ seeks to answer the timely question how can medium density housing assist Ōtautahi-Christchurch to respond to growth in a way that supports a well-functioning urban environment? Using research by design, the argument is made that MDH can be used to support a safe, accessible, and connected urban environment that fosters community, while retaining a level of privacy. This is achieved through designing a neighbourhood concept addressing 3 morphological scales- macro- the city; meso- the neighbourhood; and micro- the home and street. The scales are used to inform a design framework for MDH specific to Ōtautahi-Christchurch, presenting a typological concept that takes full advantage of the benefits higher density living has to offer.</b> Room to Breathe proposes repurposing underutilised areas surrounding existing mass transit infrastructure to provide a concentrated populous who do not solely rely on private vehicles for transport. By considering all morphological scales Room to Breathe provides one suggestion on how MDH could become accepted as part of a well-functioning urban environment.

A machine learning-supported rapid classification of building damage follo…

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