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Research papers, The University of Auckland Library

Utility managers are always looking for appropriate tools to estimate seismic damage in wastewater networks located in earthquake prone areas. Fragility curves, as an appropriate tool, are recommended for seismic vulnerability analysis of buried pipelines, including pressurised and unpressurised networks. Fragility curves are developed in pressurised networks mainly for water networks. Fragility curves are also recommended for seismic analysis in unpressurised networks. Applying fragility curves in unpressurised networks affects accuracy of seismic damage estimation. This study shows limitations of these curves in unpressurised networks. Multiple case study analysis was applied to demonstrate the limitations of the application of fragility curves in unpressurised networks in New Zealand. Four wastewater networks within New Zealand were selected as case studies and various fragility curves used for seismic damage estimation. Observed damage in unpressurised networks after the 2007 earthquake in Gisborne and the 2010 earthquake in Christchurch demonstrate the appropriateness of the applied fragility curves to New Zealand wastewater networks. This study shows that the application of fragility curves, which are developed from pressurised networks, cannot be accurately used for seismic damage assessment in unpressurised wastewater networks. This study demonstrated the effects of different parameters on seismic damage vulnerability of unpressurised networks.

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.