Using greater Christchurch as a case study, this research seeks to understand the key drivers of residential choice of families with children who live in recently developed, low-density greenfield subdivisions. In particular, the research examines the role that transport-related implications play in families’ choice of residence and location. It also explores the lived experience of the quotidian travel of these households, and the intrinsic value of their time in the car. While the research is situated in one particular location, it is designed to gain an understanding of urban processes and residents’ experiences of these as applicable to broader settings. Concerns about the pernicious environmental, fiscal, and wellbeing effects of sprawling urban form have been growing over the past few decades, inciting many cities including Christchurch to start shifting planning policies to try and achieve greater intensification and a denser development pattern. The 2010/2011 Christchurch earthquake sequence and its destruction of thousands of homes however created huge pressure for housing development, the bulk of which is now occurring on greenfield sites on the peripheries of Christchurch City and its neighbouring towns. Drawing on the insights provided by a wide body of both qualitative and quantitative literature on residential choice, transport and urban form, and mobilities literature as a basis, this research is interested in the attraction of these growing neighbourhoods to families, and puts the focus firmly on the attitudes, values, motivations, decisions, and lived experience of those who live in the growing suburbs of Christchurch.
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.
According to TS 1170.5, designing a building to satisfy code-prescribed criteria (e.g., drift limit, member safety, P-Δ stability) at the ultimate limit state and relying on the inherent margins within the design code would lead to an acceptable mean annual frequency of collapse (λ꜀) in the range of 10−⁴ to 10−⁵. Modern performance objectives, such as λ꜀ and expected annual loss (EAL), are not explicitly considered. Although buckling-restrained braced frame (BRBF) buildings were widely adopted as lateral load-resisting systems for office and car park buildings in the Christchurch rebuild following the Canterbury earthquakes in New Zealand, there are currently no official guidelines for their design. The primary focus of this study is to develop a risk-targeted design framework for BRBF buildings that can achieve the performance objectives desired by stakeholders. To this extent, key factors influencing λ꜀ and EAL of BRBF buildings are identified. These factors include gusset plate design, number of storeys, design drift limit, BRBF beam-column connection, brace configuration, brace angle, brace material grade, and analysis method (equivalent lateral force vs. modal response spectrum). A novel 3D BRBF modelling approach capable of simulating out-of-plane buckling failure of buckling-restrained brace (BRB) gusset plates is developed. Prior experimental studies on sub-assemblies conducted elsewhere have demonstrated that gusset plates and end zones may buckle out of plane prematurely, before BRBs reach their maximum axial compression load carrying capacity. Current 2D BRBF macro models, typically used in research, cannot simulate this failure mode. A conventional 2D BRBF model underestimates the λ꜀ of a case-study 4-storey super-X configured steel BRBF building (designed according to NZS-3404) by a factor of two compared to the estimate from the proposed 3D model. These findings suggest that the current NZS-3404 gusset plate design method may undersize gusset plates and that using a 2D BRBF model in this case can significantly underestimate λ꜀. Three improved alternative gusset plate design methods that are easy to implement in practice are identified from the literature. Gusset plates in two case-study 4-storey steel BRBF buildings with super-X and diagonal configurations are designed using both the NZS-3404 method and alternative methods. All three alternative design methods are found to be conservative, resulting in an almost three-fold lower λ꜀ for both case-study BRBF buildings compared to those designed using the NZS-3404 method. Analysis results indicate that (i) bidirectional interaction has no significant effect on gusset plate buckling and (ii) mid-span gusset plates are more susceptible to buckling than corner gusset plates. A framework for seismic loss assessment using incremental dynamic analysis (IDA), called loss-oriented hazard-consistent incremental dynamic analysis (LOHC-IDA), is developed. IDA can be conducted with a generic record set, eliminating the arduous site-specific record selection required to conduct multiple stripe analysis (MSA). Traditional IDA, however, is limited in producing hazard-consistent estimates of engineering demand parameters (EDPs), which LOHC-IDA overcomes. LOHC-IDA improves upon existing methods by: (i) incorporating correlations among engineering demand parameters across intensity levels and (ii) using peak ground acceleration (PGA) to predict peak floor acceleration (PFA). For two case-study steel BRBF buildings, LOHC-IDA estimates the EAL and loss distributions conditioned on the intensity level that closely match the MSA results, with an average absolute error of 5%. The influence of factors beyond gusset plate design on the λ꜀ and EAL of 26 case-study steel BRBF buildings (designed in accordance with TS 1170.5) is examined. Hazard-consistent λ꜀ and EAL for these buildings are estimated using the FEMA P-58 loss and risk assessment framework. Among the 26 case-study buildings, 23 satisfy the maximum code-specified λ꜀ limit of 10−⁴. The EAL, normalised by the total building replacement cost, is highest for 2-storey BRBFs (0.22% on average), followed by 4-storey BRBFs (0.16% on average) and 8-storey BRBFs (0.11% on average). Reducing the design drift limit has the most significant effect on lowering λ꜀ (all BRBF designs were drift governed), followed by transitioning from pinned to moment-resisting beam-column connections, reducing the brace angle, and increasing brace strength. BRBF buildings designed using the equivalent lateral force method, on average, have a lower λ꜀ compared to those designed using the modal response spectrum method. Diagonally configured BRBFs exhibit the lowest λ꜀, followed by super- X and chevron configured BRBFs. Most design variables, apart from drift limit and beam-column connection, have limited influence on EAL. A simple method for EDP-targeted design of steel BRBF buildings is proposed. For this purpose, linear regression and CatBoost machine learning models are developed to predict steel BRBF building EDPs using peak storey drift ratio (PSDR) and PFA estimates from the 26 case-study buildings at intensity levels ranging from 80% to 0.5% probability of exceedance in 50 years. The R²ₐₔⱼ of these models is around 0.98, while the average prediction error is less than 10%. Fundamental period (T₁), total building height (Hₜ), and pseudospectral acceleration at T₁, denoted as Sₐ(T₁), are selected as the features to predict PSDR, while T₁, Hₜ, and PGA are the features selected to predict PFA. The EDP-targeted design has three steps: (i) for a given Hₜ value, the PSDR prediction model is used to identify a suitable T₁ that can achieve a desired PSDR target at the design intensity, (ii) a force-based design is then conducted iteratively to achieve the target T₁ by using an appropriate ductility factor and design drift limit, and (iii) based on the T₁ in the final design iteration, the PFA demand estimated by the PFA prediction models is used as a conservative input for the design of acceleration-sensitive non-structural elements. An equation to predict λ꜀ at the design stage is proposed for collapse risk-targeted seismic design of buildings. This equation comprises three principal components: reserve building strength, a proxy for effective structural stiffness, and reserve building deformation capacity. This equation is calibrated for the collapse risk-targeted design of BRBF buildings in New Zealand using results from 26 case-study BRBF buildings. The validity of this equation is demonstrated with three design verification examples designed to specific λ꜀ targets. Considering λ꜀ from hazard-consistent incremental dynamic analysis as the benchmark, the mean absolute percentage error in the design-stage prediction of λ꜀ of the verification buildings is approximately 10%.
