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Research papers, University of Canterbury Library

Abstract This study provides a simplified methodology for pre-event data collection to support a faster and more accurate seismic loss estimation. Existing pre-event data collection frameworks are reviewed. Data gathered after the Canterbury earthquake sequences are analysed to evaluate the relative importance of different sources of building damage. Conclusions drawns are used to explore new approaches to conduct pre-event building assessment.

Research papers, University of Canterbury Library

Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.

Research papers, University of Canterbury Library

Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.

Research papers, University of Canterbury Library

The 14 November 2016 Kaikōura earthquake had major impacts on New Zealand's transport system. Road, rail and port infrastructure was damaged, creating substantial disruption for transport operators, residents, tourists, and business owners in the Canterbury, Marlborough and Wellington regions, with knock-on consequences elsewhere. During both the response and recovery phases, a large amount of information and data relating to the transport system was generated, managed, analysed, and exchanged within and between organisations to assist decision making. To improve information and data exchanges and related decision making in the transport sector during future events and guide new resilience strategies, we present key findings from a recent post-earthquake assessment. The research involved 35 different stakeholder groups and was conducted for the Ministry of Transport. We consider what transport information was available, its usefulness, where it was sourced from, mechanisms for data transfer between organisations, and suggested approaches for continued monitoring.

Research papers, University of Canterbury Library

Liquefaction-induced lateral spreading during the 2011 Christchurch earthquake in New Zealand was severe and extensive, and data regarding the displacements associated with the lateral spreading provides an excellent opportunity to better understand the factors that influence these movements. Horizontal displacements measured from optical satellite imagery and subsurface data from the New Zealand Geotechnical Database (NZGD) were used to investigate four distinct lateral spread areas along the Avon River in Christchurch. These areas experienced displacements between 0.5 and 2 m, with the inland extent of displacement ranging from 100 m to over 600 m. Existing empirical and semi-empirical displacement models tend to under estimate displacements at some sites and over estimate at others. The integrated datasets indicate that the areas with more severe and spatially extensive displacements are associated with thicker and more laterally continuous deposits of liquefiable soil. In some areas, the inland extent of displacements is constrained by geologic boundaries and geomorphic features, as expressed by distinct topographic breaks. In other areas the extent of displacement is influenced by the continuity of liquefiable strata or by the presence of layers that may act as vertical seepage barriers. These observations demonstrate the need to integrate geologic/geomorphic analyses with geotechnical analyses when assessing the potential for lateral spreading movements.

Research papers, University of Canterbury Library

Motivation This poster aims to present fragility functions for pipelines buried in liquefaction-prone soils. Existing fragility models used to quantify losses can be based on old data or use complex metrics. Addressing these issues, the proposed functions are based on the Christchurch network and soil and utilizes the Canterbury earthquake sequence (CES) data, partially represented in Figure 1. Figure 1 (a) presents the pipe failure dataset, which describes the date, location and pipe on which failures occurred. Figure 1 (b) shows the simulated ground motion intensity median of the 22nd February 2011 earthquake. To develop the model, the network and soil characteristics have also been utilized.

Research papers, University of Canterbury Library

We present the initial findings from a study of adaptive resilience of lifelines organisations providing essential infrastructure services, in Christchurch, New Zealand following the earthquakes of 2010-2011. Qualitative empirical data was collected from 200 individuals in 11 organisations. Analysis using a grounded theory method identified four major factors that aid organisational response, recovery and renewal following major disruptive events. Our data suggest that quality of top and middle-level leadership, quality of external linkages, level of internal collaboration, ability to learn from experience, and staff well-being and engagement influence adaptive resilience. Our data also suggest that adaptive resilience is a process or capacity, not an outcome and that it is contextual. Post-disaster capacity/resources and post-disaster environment influence the nature of adaptive resilience.

Research papers, University of Canterbury Library

Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.

Articles, UC QuakeStudies

An example of the five year rebuild schedule map created as part of the prioritisation process detailing where and when construction would start. The data behind this map was updated every quarter.

Research papers, University of Canterbury Library

After the Christchurch earthquakes, the government declared about 8000 houses as Red Zoned, prohibiting further developments in these properties, and offering the owners to buy them out. The government provided two options for owners: the first was full payment for both land and dwelling at the 2007 property evaluation, the second was payment for land, and the rest to be paid by the owner’s insurance. Most people chose the second option. Using data from LINZ combined with data from StatNZ, this project empirically investigates what led people to choose this second option, and what were the implications of these choices for the owners’ wealth and income.

Research papers, University of Canterbury Library

Semi-empirical models based on in-situ geotechnical tests have become the standard of practice for predicting soil liquefaction. Since the inception of the “simplified” cyclic-stress model in 1971, variants based on various in-situ tests have been developed, including the Cone Penetration Test (CPT). More recently, prediction models based soley on remotely-sensed data were developed. Similar to systems that provide automated content on earthquake impacts, these “geospatial” models aim to predict liquefaction for rapid response and loss estimation using readily-available data. This data includes (i) common ground-motion intensity measures (e.g., PGA), which can either be provided in near-real-time following an earthquake, or predicted for a future event; and (ii) geospatial parameters derived from digital elevation models, which are used to infer characteristics of the subsurface relevent to liquefaction. However, the predictive capabilities of geospatial and geotechnical models have not been directly compared, which could elucidate techniques for improving the geospatial models, and which would provide a baseline for measuring improvements. Accordingly, this study assesses the realtive efficacy of liquefaction models based on geospatial vs. CPT data using 9,908 case-studies from the 2010-2016 Canterbury earthquakes. While the top-performing models are CPT-based, the geospatial models perform relatively well given their simplicity and low cost. Although further research is needed (e.g., to improve upon the performance of current models), the findings of this study suggest that geospatial models have the potential to provide valuable first-order predictions of liquefaction occurence and consequence. Towards this end, performance assessments of geospatial vs. geotechnical models are ongoing for more than 20 additional global earthquakes.

Research papers, University of Canterbury Library

The University of Canterbury is known internationally for the Origins of New Zealand English (ONZE) corpus (see Gordon et al 2004). ONZE is a large collection of recordings from people born between 1851 and 1984, and it has been widely utilised for linguistic and sociolinguistic research on New Zealand English. The ONZE data is varied. The recordings from the Mobile Unit (MU) are interviews and were collected by members of the NZ Broadcasting service shortly after the Second World War, with the aim of recording stories from New Zealanders outside the main city centres. These were supplemented by interview recordings carried out mainly in the 1990s and now contained in the Intermediate Archive (IA). The final ONZE collection, the Canterbury Corpus, is a set of interviews and word-list recordings carried out by students at the University of Canterbury. Across the ONZE corpora, there are different interviewers, different interview styles and a myriad of different topics discussed. In this paper, we introduce a new corpus – the QuakeBox – where these contexts are much more consistent and comparable across speakers. The QuakeBox is a corpus which consists largely of audio and video recordings of monologues about the 2010-2011 Canterbury earthquakes. As such, it represents Canterbury speakers’ very recent ‘danger of death’ experiences (see Labov 2013). In this paper, we outline the creation and structure of the corpus, including the practical issues involved in storing the data and gaining speakers’ informed consent for their audio and video data to be included.

Research papers, University of Canterbury Library

Nowadays the telecommunication systems’ performance has a substantial impact on our lifestyle. Their operationality becomes even more substantial in a post-disaster scenario when these services are used in civil protection and emergency plans, as well as for the restoration of all the other critical infrastructure. Despite the relevance of loss of functionality of telecommunication networks on seismic resilience, studies on their performance assessment are few in the literature. The telecommunication system is a distributed network made up of several components (i.e. ducts, utility holes, cabinets, major and local exchanges). Given that these networks cover a large geographical area, they can be easily subjected to the effects of a seismic event, either the ground shaking itself, or co-seismic events such as liquefaction and landslides. In this paper, an analysis of the data collected after the 2010-2011 Canterbury Earthquake Sequence (CES) and the 2016 Kaikoura Earthquake in New Zealand is conducted. Analysing these data, information gaps are critically identified regarding physical and functional failures of the telecommunication components, the timeline of repair/reconstruction activities and service recovery, geotechnical tests and land planning maps. Indeed, if these missing data were presented, they could aid the assessment of the seismic resilience. Thus, practical improvements in the post-disaster collection from both a network and organisational viewpoints are proposed through consultation of national and international researchers and highly experienced asset managers from Chorus. Finally, an outline of future studies which could guide towards a more resilient seismic performance of the telecommunication network is presented.