Recent tsunami events have highlighted the importance of effective tsunami risk management strategies (including land-use planning, structural and natural mitigation, warning systems, education and evacuation planning). However, the rarity of tsunami means that empirical data concerning reactions to tsunami warnings and evacuation behaviour is rare when compared to findings for evacuations from other hazards. More knowledge is required to document the full evacuation process, including responses to warnings, pre-evacuation actions, evacuation dynamics, and the return home. Tsunami evacuation modelling has the potential to inform evidence-based tsunami risk planning and response. However, to date, tsunami evacuation models have largely focused on the timings of evacuations, rather than behaviours of those evacuating. In this research, survey data was gathered from coastal communities in Banks Peninsula and Christchurch, New Zealand, relating to behaviours and actions during the November 14th 2016 Kaikōura earthquake tsunami. Survey questions asked about immediate actions following the earthquake shaking, reactions to tsunami warnings, pre-evacuation actions, evacuation dynamics and details on congestion. This data was analysed to characterise trends and identify factors that influenced evacuation actions and behaviour, and was further used to develop a realistic evacuation model prototype to evaluate the capacity of the roading network in Banks Peninsula during a tsunami evacuation. The evacuation model incorporated tsunami risk management strategies that have been implemented by local authorities, and exposure and vulnerability data, alongside the empirical data collected from the survey. This research enhances knowledge of tsunami evacuation behaviour and reactions to tsunami warnings, and can be used to refine evacuation planning to ensure that people can evacuate efficiently, thereby reducing their tsunami exposure and personal risk.
Recent global tsunami events have highlighted the importance of effective tsunami risk management strategies (including land-use planning, structural and natural defences, warning systems, education and evacuation measures). However, the rarity of tsunami means that empirical data concerning reactions to tsunami warnings and tsunami evacuation behaviour is rare when compared to findings about evacuations to avoid other sources of hazard. To date empirical research into tsunami evacuations has focused on evacuation rates, rather than other aspects of the evacuation process. More knowledge is required about responses to warnings, pre-evacuation actions, evacuation dynamics and the return home after evacuations. Tsunami evacuation modelling has the potential to inform evidence-based tsunami risk planning and response. However to date tsunami evacuation models have largely focused on timings of evacuations, rather than evacuation behaviours. This Masters research uses a New Zealand case study to reduce both of these knowledge gaps. Qualitative survey data was gathered from populations across coastal communities in Banks Peninsula and Christchurch, New Zealand, required to evacuate due to the tsunami generated by the November 14th 2016 Kaikōura Earthquake. Survey questions asked about reactions to tsunami warnings, actions taken prior to evacuating and movements during the 2016 tsunami evacuation. This data was analysed to characterise trends and identify factors that influenced evacuation actions and behaviour. Finally, it was used to develop an evacuation model for Banks Peninsula. Where appropriate, the modelling inputs were informed by the survey data. Three key findings were identified from the results of the evacuation behaviour survey. Although 38% of the total survey respondents identified the earthquake shaking as a natural cue for the tsunami, most relied on receiving official warnings, including sirens, to prompt evacuations. Respondents sought further official information to inform their evacuation decisions, with 39% of respondents delaying their evacuation in order to do so. Finally, 96% of total respondents evacuated by car. This led to congestion, particularly in more densely populated Christchurch city suburbs. Prior to this research, evacuation modelling had not been completed for Banks Peninsula. The results of the modelling showed that if evacuees know how to respond to tsunami warnings and where and how to evacuate, there are no issues. However, if there are poor conditions, including if people do not evacuate immediately, if there are issues with the roading network, or if people do not know where or how to evacuate, evacuation times increase with there being more bottlenecks leading out of the evacuation zones. The results of this thesis highlight the importance of effective tsunami education and evacuation planning. Reducing exposure to tsunami risk through prompt evacuation relies on knowledge of how to interpret tsunami warnings, and when, where and how to evacuate. Recommendations from this research outline the need for public education and engagement, and the incorporation of evacuation signage, information boards and evacuation drills. Overall these findings provide more comprehensive picture of tsunami evacuation behaviour and decision making based on empirical data from a recent evacuation, which can be used to improve tsunami risk management strategies. This empirical data can also be used to inform evacuation modelling to improve the accuracy and realism of the evacuation models.
Rapid, reliable information on earthquake-affected structures' current damage/health conditions and predicting what would happen to these structures under future seismic events play a vital role in accelerating post-event evaluations, leading to optimized on-time decisions. Such rapid and informative post-event evaluations are crucial for earthquake-prone areas, where each earthquake can potentially trigger a series of significant aftershocks, endangering the community's health and wealth by further damaging the already-affected structures. Such reliable post-earthquake evaluations can provide information to decide whether an affected structure is safe to stay in operation, thus saving many lives. Furthermore, they can lead to more optimal recovery plans, thus saving costs and time. The inherent deficiency of visual-based post-earthquake evaluations and the importance of structural health monitoring (SHM) methods and SHM instrumentation have been highlighted within this thesis, using two earthquake-affected structures in New Zealand: 1) the Canterbury Television (CTV) building, Christchurch; 2) the Bank of New Zealand (BNZ) building, Wellington. For the first time, this thesis verifies the theoretically- and experimentally validated hysteresis loop analysis (HLA) SHM method for the real-world instrumented structure of the BNZ building, which was damaged severely due to three earthquakes. Results indicate the HLA-SHM method can accurately estimate elastic stiffness degradation for this reinforced concrete (RC) pinched structure across the three earthquakes, which remained unseen until after the third seismic event. Furthermore, the HLA results help investigate the pinching effects on the BNZ building's seismic response. This thesis introduces a novel digital clone modelling method based on the robust and accurate SHM results delivered by the HLA method for physical parameters of the monitored structure and basis functions predicting the changes of these physical parameters due to future earthquake excitations. Contrary to artificial intelligence (AI) based predictive methods with black-box designs, the proposed predictive method is entirely mechanics-based with an explicitly-understandable design, making them more trusted and explicable to stakeholders engaging in post-earthquake evaluations, such as building owners and insurance firms. The proposed digital clone modelling framework is validated using the BNZ building and an experimental RC test structure damaged severely due to three successive shake-table excitations. In both structures, structural damage intensifies the pinching effects in hysteresis responses. Results show the basis functions identified from the HLA-SHM results for both structures under Event 1 can online estimate structural damage due to subsequent Events 2-3 from the measured structural responses, making them valuable tool for rapid warning systems. Moreover, the digital twins derived for these two structures under Event 1 can successfully predict structural responses and damage under Events 2-3, which can be integrated with the incremental dynamic analysis (IDA) method to assess structural collapse and its financial risks. Furthermore, it enables multi-step IDA to evaluate earthquake series' impacts on structures. Overall, this thesis develops an efficient method for providing reliable information on earthquake-affected structures' current and future status during or immediately after an earthquake, considerably guaranteeing safety. Significant validation is implemented against both experimental and real data of RC structures, which thus clearly indicate the accurate predictive performance of this HLA-based method.
