© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines hysteresis loop analysis (HLA) structural health monitoring (SHM) and incremental dynamic analysis (IDA) methods to identify and then analyse collapse capacity and the probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher-resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010–2011 had significant post-event aftershocks.
The supply of water following disasters has always been of significant concern to communities. Failure of water systems not only causes difficulties for residents and critical users but may also affect other hard and soft infrastructure and services. The dependency of communities and other infrastructure on the availability of safe and reliable water places even more emphasis on the resilience of water supply systems. This thesis makes two major contributions. First, it proposes a framework for measuring the multifaceted resilience of water systems, focusing on the significance of the characteristics of different communities for the resilience of water supply systems. The proposed framework, known as the CARE framework, consists of eight principal activities: (1) developing a conceptual framework; (2) selecting appropriate indicators; (3) refining the indicators based on data availability; (4) correlation analysis; (5) scaling the indicators; (6) weighting the variables; (7) measuring the indicators; and (8) aggregating the indicators. This framework allows researchers to develop appropriate indicators in each dimension of resilience (i.e., technical, organisational, social, and economic), and enables decision makers to more easily participate in the process and follow the procedure for composite indicator development. Second, it identifies the significant technical, social, organisational and economic factors, and the relevant indicators for measuring these factors. The factors and indicators were gathered through a comprehensive literature review. They were then verified and ranked through a series of interviews with water supply and resilience specialists, social scientists and economists. Vulnerability, redundancy and criticality were identified as the most significant technical factors affecting water supply system robustness, and consequently resilience. These factors were tested for a scenario earthquake of Mw 7.6 in Pukerua Bay in New Zealand. Four social factors and seven indicators were identified in this study. The social factors are individual demands and capacities, individual involvement in the community, violence level in the community, and trust. The indicators are the Giving Index, homicide rate, assault rate, inverse trust in army, inverse trust in police, mean years of school, and perception of crime. These indicators were tested in Chile and New Zealand, which experienced earthquakes in 2010 and 2011 respectively. The social factors were also tested in Vanuatu following TC Pam, which hit the country in March 2015. Interestingly, the organisational dimension contributed the largest number of factors and indicators for measuring water supply resilience to disasters. The study identified six organisational factors and 17 indicators that can affect water supply resilience to disasters. The factors are: disaster precaution; predisaster planning; data availability, data accessibility and information sharing; staff, parts, and equipment availability; pre-disaster maintenance; and governance. The identified factors and their indicators were tested for the case of Christchurch, New Zealand, to understand how organisational capacity affected water supply resilience following the earthquake in February 2011. Governance and availability of critical staff following the earthquake were the strongest organisational factors for the Christchurch City Council, while the lack of early warning systems and emergency response planning were identified as areas that needed to be addressed. Economic capacity and quick access to finance were found to be the main economic factors influencing the resilience of water systems. Quick access to finance is most important in the early stages following a disaster for response and restoration, but its importance declines over time. In contrast, the economic capacity of the disaster struck area and the water sector play a vital role in the subsequent reconstruction phase rather than in the response and restoration period. Indicators for these factors were tested for the case of the February 2011 earthquake in Christchurch, New Zealand. Finally, a new approach to measuring water supply resilience is proposed. This approach measures the resilience of the water supply system based on actual water demand following an earthquake. The demand-based method calculates resilience based on the difference between water demand and system capacity by measuring actual water shortage (i.e., the difference between water availability and demand) following an earthquake.
