Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
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.
The suburb of New Brighton in Christchurch Aotearoa was once a booming retail sector until the end of its exclusivity to Saturday shopping in 1980 and the aftermath of the devastating 2011 Christchurch earthquake. The suburb of New Brighton was hit particularly hard and fell into economic collapse, partly brought on by the nature of its economic structure. This implosion created an urban crisis where people and businesses abandoned the suburb and its once-booming commercial economy. As a result, New Brighton has been left with the residue of abandoned infrastructure and commercial propaganda such as billboards, ATM machines, commercial facades, and shopping trolleys that as abandoned fragments, no longer contribute to culture, society and the economy. This design-led research investigation proposes to repurpose the broken objects that were left behind. By strategically selecting objects that are symbols of the root cause of the economic devastation, the repurposed and re-contextualised fragments will seek to allegorically expose the city’s destructive economic narrative, while providing a renewed sense of place identity for the people. This design-led thesis investigation argues that the seemingly innocuous icons of commercial industry, such as billboards, ATM machines, commercial facades, and shopping trolleys, are intended to act as lures to encourage people to spend money; ultimately, these urban and architectural lures can contribute to economic devastation. The aim of this investigation is to repurpose abandoned fragments of capitalist infrastructure in ways that can help to unveil new possibilities for a disrupted community and enhance their awareness of what led to the urban disruption. The thesis proposes to achieve this research aim by exploring three principal research objectives: 1) to assimilate and re-contextualise disconnected urban fragments into new architectural interventions; 2) to anthropomorphise these new interventions so that they are recognisable as architectural ‘inhabitants’, the storytellers of the urban context; and 3) to curate these new architectural interventions in ways that enable a community-scale allegorical and didactic experience to be recognised.
