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Audio, Radio New Zealand

Artist and landscape architect Bridget Allen wouldn't have known how appropriate the name of her gardening business was to be when she set it up, out of Ilam art school and working at the Christchurch Botanic Gardens.  The name Regenerative Gardening Maintenance was prophetic given her city and its landscape was about to start regenerating.  The 2010-2011 Canterbury earthquakes saw not only buildings turned to rubble, large tracts of land, including an area around Ōtākaro Avon River the size of two New York Central Parks, started to turn from suburbia back to nature. The red zone has been turning green ever since.  In the wake of tragedy artists and gardeners came together to innovate and create new public spaces, with an eye on sustainability and community connection. Allen cofounded New Brighton sewing charity Stitch-o-Mat and retrained as a landscape architect.  Since 2023 she has been the director of The Green Lab, which began after the quakes as Greening the Rubble, creating urban green spaces and events for connection, while also working with residents to make their own backyards more sustainable.     Ever busy with working and planting bees, workshops to build habitats for plants and nature, and consultations to help people make their backyards more sustainable, on August 16 Bridget is running with The Green Lab Birds of Brighton printmaking workshops. It's at the Make Station in New Brighton Mall at 11am and 1pm. No experience is needed.  She joined Culture 101's Mark Amery.

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