Critical infrastructure networks are highly relied on by society such that any disruption to service can have major social and economic implications. Furthermore, these networks are becoming increasingly dependent on each other for normal operation such that an outage or asset failure in one system can easily propagate and cascade across others resulting in widespread disruptions in terms of both magnitude and spatial reach. It is the vulnerability of these networks to disruptions and the corresponding complexities in recovery processes which provide direction to this research. This thesis comprises studies contributing to two areas (i) the modelling of national scale in-terdependent infrastructure systems undergoing major disruptions, and (ii) the tracking and quantification of infrastructure network recovery trajectories following major disruptions. Firstly, methods are presented for identifying nationally significant systemic vulnerabilities and incorporating expert knowledge into the quantification of infrastructure interdependency mod-elling and simulation. With application to the interdependent infrastructures networks across New Zealand, the magnitudes and spatial extents of disruption are investigated. Results high-light the importance in considering interdependencies when assessing disruptive risks and vul-nerabilities in disaster planning applications and prioritising investment decisions for enhancing resilience of national networks. Infrastructure dependencies are further studied in the context of recovery from major disruptions through the analysis of curves measuring network functionality over time. Continued studies into the properties of recovery curves across a database of global natural disasters produce statistical models for predicting the trajectory and expected recovery times. Finally, the use of connectivity based metrics for quantifying infrastructure system functionality during recovery are considered with a case study application to the Christchurch Earthquake (February 22, 2011) wastewater network response.
A significant portion of economic loss from the Canterbury Earthquake sequence in 2010-2011 was attributed to losses to residential buildings. These accounted for approximately $12B of a total $40B economic losses (Horspool, 2016). While a significant amount of research effort has since been aimed at research in the commercial sector, little has been done to reduce the vulnerability of the residential building stock.
Background and methodology The Mw 7.8, 14th November 2016 earthquake centred (item b, figure 1) in the Hurunui District of the South Island, New Zealand, damaged critical infrastructure across North Canterbury and Marlborough. We investigate the impacts to infrastructure and adaptations to the resulting service disruption in four small rural towns (figure 1): Culverden (a), Waiau (c), Ward (d) and Seddon (e). This is accomplished though literary research, interviews and geospatial analysis. Illustrating our methods, we have displayed here a Hurunui District hazard map (figure 2b) and select infrastructure inventories (figures 2a, 3).
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.
This research aims to explore how business models of SMEs revolve in the face of a crisis to be resilient. The business model canvas was used as a tool to analyse business models of SMEs in Greater Christchurch. The purpose was to evaluate the changes SMEs brought in their business models after hit by a series of earthquake in 2010 and 2011. The idea was to conduct interviews of business owners and analyse using grounded theory methods. Because this method is iterative, a tentative theoretical framework was proposed, half way through the data collection. It was realised that owner specific characteristics were more prominent in the data than the elements business model. Although, SMEs in this study experienced several operational changes in their business models such as change of location and modification of payment terms. However, the suggested framework highlights how owner specific attributes influence the survival of a small business. Small businesses and their owners are extremely interrelated that the business models personify the owner specific characteristics. In other words, the adaptation of the business model reflects the extent to which the owner possess these attributes. These attributes are (a) Mindsets – the attitude and optimism of business owner; (b) Adaptive coping – the ability of business owner to take corrective actions; and (c) Social capital – the network of a business owner, including family, friends, neighbours and business partners.
Research indicates that aside from the disaster itself, the next major source of adverse outcomes during such events, is from errors by either the response leader or organisation. Yet, despite their frequency, challenge, complexity, and the risks involved; situations of extreme context remain one of the least researched areas in the leadership field. This is perhaps surprising. In the 2010 and 2011 (Christchurch) earthquakes alone, 185 people died and rebuild costs are estimated to have been $40b. Add to this the damage and losses annually around the globe arising from natural disasters, major business catastrophes, and military conflict; there is certainly a lot at stake (lives, way of life, and our well-being). While over the years, much has been written on leadership, there is a much smaller subset of articles on leadership in extreme contexts, with the majority of these focusing on the event rather than leadership itself. Where leadership has been the focus, the spotlight has shone on the actions and capabilities of one person - the leader. Leadership, however, is not simply one person, it is a chain or network of people, delivering outcomes with the support of others, guided by a governance structure, contextualised by the environment, and operating on a continuum across time (before, during, and after an event). This particular research is intended to examine the following: • What are the leadership capabilities and systems necessary to deliver more successful outcomes during situations of extreme context; • How does leadership in these circumstances differ from leadership during business as usual conditions; • Lastly, through effective leadership, can we leverage these unfortunate events to thrive, rather than merely survive?
This poster presents work to date on ground motion simulation validation and inversion for the Canterbury, New Zealand region. Recent developments have focused on the collection of different earthquake sources and the verification of the SPECFEM3D software package in forward and inverse simulations. SPECFEM3D is an open source software package which simulates seismic wave propagation and performs adjoint tomography based upon the spectral-element method. Figure 2: Fence diagrams of shear wave velocities highlighting the salient features of the (a) 1D Canterbury velocity model, and (b) 3D Canterbury velocity model. Figure 5: Seismic sources and strong motion stations in the South Island of New Zealand, and corresponding ray paths of observed ground motions. Figure 3: Domain used for the 19th October 2010 Mw 4.8 case study event including the location of the seismic source and strong motion stations. By understanding the predictive and inversion capabilities of SPECFEM3D, the current 3D Canterbury Velocity Model can be iteratively improved to better predict the observed ground motions. This is achieved by minimizing the misfit between observed and simulated ground motions using the built-in optimization algorithm. Figure 1 shows the Canterbury Velocity Model domain considered including the locations of small-to-moderate Mw events [3-4.5], strong motion stations, and ray paths of observed ground motions. The area covered by the ray paths essentially indicates the area of the model which will be most affected by the waveform inversion. The seismic sources used in the ground motion simulations are centroid moment tensor solutions obtained from GeoNet. All earthquake ruptures are modelled as point sources with a Gaussian source time function. The minimum Mw limit is enforced to ensure good signal-to-noise ratio and well constrained source parameters. The maximum Mw limit is enforced to ensure the point source approximation is valid and to minimize off-fault nonlinear effects.
