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Research papers, Lincoln University

We examined the stratigraphy of alluvial fans formed at the steep range front of the Southern Alps at Te Taho, on the north bank of the Whataroa River in central West Coast, South Island, New Zealand. The range front coincides with the Alpine Fault, an Australian-Pacific plate boundary fault, which produces regular earthquakes. Our study of range front fans revealed aggradation at 100- to 300-year intervals. Radiocarbon ages and soil residence times (SRTs) estimated by a quantitative profile development index allowed us to elucidate the characteristics of four episodes of aggradation since 1000 CE. We postulate a repeating mode of fan behaviour (fan response cycle [FRC]) linked to earthquake cycles via earthquake-triggered landslides. FRCs are characterised by short response time (aggradation followed by incision) and a long phase when channels are entrenched and fan surfaces are stable (persistence time). Currently, the Te Taho and Whataroa River fans are in the latter phase. The four episodes of fan building we determined from an OxCal sequence model correlate to Alpine Fault earthquakes (or other subsidiary events) and support prior landscape evolution studies indicating ≥M7.5 earthquakes as the main driver of episodic sedimentation. Our findings are consistent with other historic non-earthquake events on the West Coast but indicate faster responses than other earthquake sites in New Zealand and elsewhere where rainfall and stream gradients (the basis for stream power) are lower. Judging from the thickness of fan deposits and the short response times, we conclude that pastoral farming (current land-use) on the fans and probably across much of the Whataroa River fan would be impossible for several decades after a major earthquake. The sustainability of regional tourism and agriculture is at risk, more so because of the vulnerability of the single through road in the region (State Highway 6).

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.