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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

Research papers, University of Canterbury Library

The 2010-2011 Christchurch earthquakes generated damage in several Reinforced Concrete (RC) buildings, which had RC walls as the principal resistant element against earthquake demand. Despite the agreement between structural engineers and researchers in an overall successfully performance there was a lack of knowledge about the behaviour of the damaged structures, and even deeper about a repaired structure, which triggers arguments between different parties that remains up to these days. Then, it is necessary to understand the capacity of the buildings after the earthquake and see how simple repairs techniques improve the building performance. This study will assess the residual capacity of ductile slender RC walls according to current standards in New Zealand, NZS 3101.1 2006 A3. First, a Repaired RC walls Database is created trying to gather previous studies and to evaluate them with existing international guidelines. Then, an archetype building is designed, and the wall is extracted and scaled. Four half-scale walls were designed and will be constructed and tested at the Structures Testing Laboratory at The University of Auckland. The overall dimensions are 3 [m] height, 2 [m] length and 0.175 [m] thick. All four walls will be identical, with differences in the loading protocol and the presence or absence of a repair technique. Results are going to be useful to assess the residual capacity of a damaged wall compare to the original behaviour and also the repaired capacity of walls with simpler repair techniques. The expected behaviour is focussed on big changes in stiffness, more evident than in previously tested RC beams found in the literature.