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Research papers, The University of Auckland Library

Seismic retrofitting of unreinforced masonry buildings using posttensioning has been the topic of many recent experimental research projects. However, the performance of such retrofit designs in actual design level earthquakes has previously been poorly documented. In 1984 two stone masonry buildings within The Arts Centre of Christchurch received posttensioned seismic retrofits, which were subsequently subjected to design level seismic loads during the 2010/2011 Canterbury earthquake sequence. These 26 year old retrofits were part of a global scheme to strengthen and secure the historic building complex and were subject to considerable budgetary constraints. Given the limited resources available at the time of construction and the current degraded state of the steel posttension tendons, the posttensioned retrofits performed well in preventing major damage to the overall structure of the two buildings in the Canterbury earthquakes. When compared to other similar unretrofitted structures within The Arts Centre, it is demonstrated that the posttensioning significantly improved the in-plane and out-of-plane wall strength and the ability to limit residual wall displacements. The history of The Arts Centre buildings and the details of the Canterbury earthquakes is discussed, followed by examination of the performance of the posttension retrofits and the suitability of this technique for future retrofitting of other historic unreinforced masonry buildings http://www.aees.org.au/downloads/conference-papers/

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