The magnitude 6.2 Christchurch earthquake struck the city of Christchurch at 12:51pm on February 22, 2011. The earthquake caused 186 fatalities, a large number of injuries, and resulted in widespread damage to the built environment, including significant disruption to lifeline networks and health care facilities. Critical facilities, such as public and private hospitals, government, non-government and private emergency services, physicians’ offices, clinics and others were severely impacted by this seismic event. Despite these challenges many systems were able to adapt and cope. This thesis presents the physical and functional impact of the Christchurch earthquake on the regional public healthcare system by analysing how it adapted to respond to the emergency and continued to provide health services. Firstly, it assesses the seismic performance of the facilities, mechanical and medical equipment, building contents, internal services and back-up resources. Secondly, it investigates the reduction of functionality for clinical and non-clinical services, induced by the structural and non-structural damage. Thirdly it assesses the impact on single facilities and the redundancy of the health system as a whole following damage to the road, power, water, and wastewater networks. Finally, it assesses the healthcare network's ability to operate under reduced and surged conditions. The effectiveness of a variety of seismic vulnerability preparedness and reduction methods are critically reviewed by comparing the observed performances with the predicted outcomes of the seismic vulnerability and disaster preparedness models. Original methodology is proposed in the thesis which was generated by adapting and building on existing methods. The methodology can be used to predict the geographical distribution of functional loss, the residual capacity and the patient transfer travel time for hospital networks following earthquakes. The methodology is used to define the factors which contributed to the overall resilence of the Canterbury hospital network and the areas which decreased the resilence. The results show that the factors which contributed to the resilence, as well as the factors which caused damage and functionality loss were difficult to foresee and plan for. The non-structural damage to utilities and suspended ceilings was far more disruptive to the provision of healthcare than the minor structural damage to buildings. The physical damage to the healthcare network reduced the capacity, which has further strained a health care system already under pressure. Providing the already high rate of occupancy prior to the Christchurch earthquake the Canterbury healthcare network has still provided adequate healthcare to the community.
Existing unreinforced masonry (URM) buildings are often composed of traditional construction techniques, with poor connections between walls and diaphragms that results in poor performance when subjected to seismic actions. In these cases the application of the common equivalent static procedure is not applicable because it is not possible to assure “box like” behaviour of the structure. In such conditions the ultimate strength of the structure relies on the behaviour of the macro-elements that compose the deformation mechanisms of the whole structure. These macroelements are a single or combination of structural elements of the structure which are bonded one to each other. The Canterbury earthquake sequence was taken as a reference to estimate the most commonly occurring collapse mechanisms found in New Zealand URM buildings in order to define the most appropriate macroelements.
Unreinforced masonry (URM) structures comprise a majority of the global built heritage. The masonry heritage of New Zealand is comparatively younger to its European counterparts. In a country facing frequent earthquakes, the URM buildings are prone to extensive damage and collapse. The Canterbury earthquake sequence proved the same, causing damage to over _% buildings. The ability to assess the severity of building damage is essential for emergency response and recovery. Following the Canterbury earthquakes, the damaged buildings were categorized into various damage states using the EMS-98 scale. This article investigates machine learning techniques such as k-nearest neighbors, decision trees, and random forests, to rapidly assess earthquake-induced building damage. The damage data from the Canterbury earthquake sequence is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. On getting a high accuracy the model is then run for building database collected for Dunedin to predict expected damage during the rupture of the Akatore fault.
he 2016 Building (Earthquake Prone Building) Amendment Act aims to improve the system for managing earthquake-prone buildings. The proposed changes to the Act were precipitated by the Canterbury earthquakes, and the need to improve the seismic safety of New Zealand’s building stock. However, the Act has significant ramifications for territorial authorities, organisations and individuals in small New Zealand towns, since assessing and repairing heritage buildings poses a major cost to districts with low populations and poor rental returns on commercial buildings.
Unreinforced masonry churches in New Zealand, similarly to everywhere else in the word have proven to be highly vulnerable to earthquakes, because of their particular construction features. The Canterbury (New Zealand) earthquake sequence, 2010-2011 caused an invaluable loss of local architectural heritage and of churches, as regrettably, some of them were demolished instead of being repaired. It is critical for New Zealand to advance the data collection, research and understanding pertaining to the seismic performance and protection of church buildings, with the aim to:
Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.
