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.
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The development of Digital City technologies to manage and visualise spatial information has increasingly become a focus of the research community, and application by city authorities. Traditionally, the Geographic Information Systems (GIS) and Building Information Models (BIM) underlying Digital Cities have been used independently. However, integrating GIS and BIM into a single platform provides benefits for project and asset management, and is applicable to a range of issues. One of these benefits is the means to access and analyse large datasets describing the built environment, in order to characterise urban risk from and resilience to natural hazards. The aim of this thesis is to further explore methodologies of integration in two distinct areas. The first, integration through connectivity of heterogeneous datasets where GIS spatial infrastructure data is merged with 3D BIM building data to create a digital twin. Secondly, integration through analysis whereby data from the digital twin are extracted and integrated with computational models. To achieve this, a workflow was developed to identify the required datasets of a digital twin, and develop a process of integrating those datasets through a combination of; semi-autonomous conversion, translation and extension of data; and semantic web and services-based processes. Through use of a designed schema, the data were streamed in a homogenous format in a web-based platform. To demonstrate the value of this workflow with respect to urban risk and resilience, the process was applied to the Taiora: Queen Elizabeth II recreation and sports centre in eastern Christchurch, New Zealand. After integration of as-built GIS and BIM datasets, targeted data extraction was implemented, with outputs tailored for analysis in an infrastructure serviceability loss model, which assessed potable water network performance in the 22nd February 2011 Christchurch Earthquake. Using the same earthquake conditions as the serviceability loss model, performance of infrastructure assets in service at the time of the 22nd February 2011 Christchurch Earthquake was compared to new assets rebuilt at the site, post-earthquake. Due to improved potable water infrastructure resilience resulting from installation of ductile piles, a decrease of 35.5% in the probability of service loss was estimated in the serviceability loss model. To complete the workflow, the results from the external analysis were uploaded to the web-based platform. One of the more significant outcomes from the workflow was the identification of a lack of mandated metadata standards for fittings/valves connecting a building to private laterals. Whilst visually the GIS and BIM data show the building and pipes as connected, the semantic data does not include this connectivity relationship. This has no material impact on the current serviceability loss model as it is not one of the defined parameters. However, a proposed modification to the model would utilise the metadata to further assess the physical connection robustness, and increase the number of variables for estimating probability of service loss. This thesis has made a methodological contribution to urban resilience analysis by demonstrating how readily available up-to-date building and infrastructure data can be integrated, and with tailored extraction from a Digital City platform, be used for disaster impact analysis in an external computational engine, with results in turn imported and visualised in the Digital City platform. The workflow demonstrated that translation and integration of data would be more successful if a regional/national mandate was implemented for the submission of consent documentation in a specified standard BIM format. The results of this thesis have identified that the key to ensuring the success of an integrated tool lies in the initial workflow required to safeguard that all data can be either captured or translated in an interoperable format.
The development of Digital City technologies to manage and visualise spatial information has increasingly become a focus of the research community, and application by city authorities. Traditionally, the Geographic Information Systems (GIS) and Building Information Models (BIM) underlying Digital Cities have been used independently. However, integrating GIS and BIM into a single platform provides benefits for project and asset management, and is applicable to a range of issues. One of these benefits is the means to access and analyse large datasets describing the built environment, in order to characterise urban risk from and resilience to natural hazards. The aim of this thesis is to further explore methodologies of integration in two distinct areas. The first, integration through connectivity of heterogeneous datasets where GIS spatial infrastructure data is merged with 3D BIM building data to create a digital twin. Secondly, integration through analysis whereby data from the digital twin are extracted and integrated with computational models. To achieve this, a workflow was developed to identify the required datasets of a digital twin, and develop a process of integrating those datasets through a combination of; semi-autonomous conversion, translation and extension of data; and semantic web and services-based processes. Through use of a designed schema, the data were streamed in a homogenous format in a web-based platform. To demonstrate the value of this workflow with respect to urban risk and resilience, the process was applied to the Taiora: Queen Elizabeth II recreation and sports centre in eastern Christchurch, New Zealand. After integration of as-built GIS and BIM datasets, targeted data extraction was implemented, with outputs tailored for analysis in an infrastructure serviceability loss model, which assessed potable water network performance in the 22nd February 2011 Christchurch Earthquake. Using the same earthquake conditions as the serviceability loss model, performance of infrastructure assets in service at the time of the 22nd February 2011 Christchurch Earthquake was compared to new assets rebuilt at the site, post-earthquake. Due to improved potable water infrastructure resilience resulting from installation of ductile piles, a decrease of 35.5% in the probability of service loss was estimated in the serviceability loss model. To complete the workflow, the results from the external analysis were uploaded to the web-based platform. One of the more significant outcomes from the workflow was the identification of a lack of mandated metadata standards for fittings/valves connecting a building to private laterals. Whilst visually the GIS and BIM data show the building and pipes as connected, the semantic data does not include this connectivity relationship. This has no material impact on the current serviceability loss model as it is not one of the defined parameters. However, a proposed modification to the model would utilise the metadata to further assess the physical connection robustness, and increase the number of variables for estimating probability of service loss. This thesis has made a methodological contribution to urban resilience analysis by demonstrating how readily available up-to-date building and infrastructure data can be integrated, and with tailored extraction from a Digital City platform, be used for disaster impact analysis in an external computational engine, with results in turn imported and visualised in the Digital City platform. The workflow demonstrated that translation and integration of data would be more successful if a regional/national mandate was implemented for the submission of consent documentation in a specified standard BIM format. The results of this thesis have identified that the key to ensuring the success of an integrated tool lies in the initial workflow required to safeguard that all data can be either captured or translated in an interoperable format.
Asset management in power systems is exercised to improve network reliability to provide confidence and security for customers and asset owners. While there are well-established reliability metrics that are used to measure and manage business-as-usual disruptions, an increasing appreciation of the consequences of low-probability high-impact events means that resilience is increasingly being factored into asset management in order to provide robustness and redundancy to components and wider networks. This is particularly important for electricity systems, given that a range of other infrastructure lifelines depend upon their operation. The 2010-2011 Canterbury Earthquake Sequence provides valuable insights into electricity system criticality and resilience in the face of severe earthquake impacts. While above-ground assets are relatively easy to monitor and repair, underground assets such as cables emplaced across wide areas in the distribution network are difficult to monitor, identify faults on, and repair. This study has characterised in detail the impacts to buried electricity cables in Christchurch resulting from seismically-induced ground deformation caused primarily by liquefaction and lateral spread. Primary modes of failure include cable bending, stretching, insulation damage, joint braking and, being pulled off other equipment such as substation connections. Performance and repair data have been compiled into a detailed geospatial database, which in combination with spatial models of peak ground acceleration, peak ground velocity and ground deformation, will be used to establish rigorous relationships between seismicity and performance. These metrics will be used to inform asset owners of network performance in future earthquakes, further assess component criticality, and provide resilience metrics.
Spatial variations in river facies exerted a strong influence on the distribution of liquefaction features observed in Christchurch during the 2010-11 Canterbury Earthquake Sequence (CES). Liquefaction and liquefaction-induced ground deformation was primarily concentrated near modern waterways and areas underlain by Holocene fluvial deposits with shallow water tables (< 1 to 2 m). In southern Christchurch, spatial variations of liquefaction and subsidence were documented in the suburbs within inner meander loops of the Heathcote River. Newly acquired geospatial data, geotechnical reports and eye-witness discussions are compiled to provide a detailed account of the surficial effects of CES liquefaction and ground deformation adjacent to the Heathcote River. LiDAR data and aerial photography are used to produce a new series of original figures which reveal the locations of recurrent liquefaction and subsidence. To investigate why variable liquefaction patterns occurred, the distribution of surface ejecta and associated ground damage is compared with near-surface sedimentologic, topographic, and geomorphic variability to seek relationships between the near-surface properties and observed ground damages. The most severe liquefaction was concentrated within a topographic low in the suburb of St Martins, an inner meander loop of the Heathcote River, with liquefaction only minor or absent in the surrounding areas. Subsurface investigations at two sites in St Martins enable documentation of fluvial stratigraphy, the expressions of liquefaction, and identification of pre-CES liquefaction features. Excavation to water table depths (~1.5 m below the surface) across sand boils reveals multiple generations of CES liquefaction dikes and sills that cross-cut Holocene fluvial and anthropogenic stratigraphy. Based on in situ geotechnical tests (CPT) indicating sediment with a factor of safety < 1, the majority of surface ejecta was sourced from well-sorted fine to medium sand at < 5 m depth, with the most damaging liquefaction corresponding with the location of a low-lying sandy paleochannel, a remnant river channel from the Holocene migration of the meander in St Martins. In the adjacent suburb of Beckenham, where migration of the Heathcote River has been laterally confined by topography associated with the volcanic lithologies of Banks Peninsula, severe liquefaction was absent with only minor sand boils occurring closest to the modern river channel. Auger sampling across the suburb revealed thick (>1 m) clay-rich overbank and back swamp sediments that produced a stratigraphy which likely confined the units susceptible to liquefaction and prevented widespread ejection of liquefied material. This analysis suggests river migration promotes the formation and preservation of fluvial deposits prone to liquefaction. Trenching revealed the strongest CES earthquakes with large vertical accelerations favoured sill formation and severe subsidence at highly susceptible locations corresponding with an abandoned channel. Less vulnerable sites containing deeper and thinner sand bodies only liquefied in the strongest and most proximal earthquakes forming minor localised liquefaction features. Liquefaction was less prominent and severe subsidence was absent where lateral confinement of a Heathcote meander has promoted the formation of fluvial stratum resistant to liquefaction. Correlating CES liquefaction with geomorphic interpretations of Christchurch’s Heathcote River highlights methods in which the performance of liquefaction susceptibility models can be improved. These include developing a reliable proxy for estimating soil conditions in meandering fluvial systems by interpreting the geology and geomorphology, derived from LiDAR data and modern river morphology, to improve the methods of accounting for the susceptibility of an area. Combining geomorphic interpretations with geotechnical data can be applied elsewhere to identify regional liquefaction susceptibilities, improve existing liquefaction susceptibility datasets, and predict future earthquake damage.
