Abstract This study provides a simplified methodology for pre-event data collection to support a faster and more accurate seismic loss estimation. Existing pre-event data collection frameworks are reviewed. Data gathered after the Canterbury earthquake sequences are analysed to evaluate the relative importance of different sources of building damage. Conclusions drawns are used to explore new approaches to conduct pre-event building assessment.
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:
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.
Semi-empirical models based on in-situ geotechnical tests have become the standard of practice for predicting soil liquefaction. Since the inception of the “simplified” cyclic-stress model in 1971, variants based on various in-situ tests have been developed, including the Cone Penetration Test (CPT). More recently, prediction models based soley on remotely-sensed data were developed. Similar to systems that provide automated content on earthquake impacts, these “geospatial” models aim to predict liquefaction for rapid response and loss estimation using readily-available data. This data includes (i) common ground-motion intensity measures (e.g., PGA), which can either be provided in near-real-time following an earthquake, or predicted for a future event; and (ii) geospatial parameters derived from digital elevation models, which are used to infer characteristics of the subsurface relevent to liquefaction. However, the predictive capabilities of geospatial and geotechnical models have not been directly compared, which could elucidate techniques for improving the geospatial models, and which would provide a baseline for measuring improvements. Accordingly, this study assesses the realtive efficacy of liquefaction models based on geospatial vs. CPT data using 9,908 case-studies from the 2010-2016 Canterbury earthquakes. While the top-performing models are CPT-based, the geospatial models perform relatively well given their simplicity and low cost. Although further research is needed (e.g., to improve upon the performance of current models), the findings of this study suggest that geospatial models have the potential to provide valuable first-order predictions of liquefaction occurence and consequence. Towards this end, performance assessments of geospatial vs. geotechnical models are ongoing for more than 20 additional global earthquakes.
Disasters, either man-made or natural, are characterised by a multiplicity of factors including loss of property, life, environmental degradation, and psychosocial malfunction of the affected community. Although much research has been undertaken on proactive disaster management to help reduce the impacts of natural and man-made disasters, many challenges still remain. In particular, the desire to re-house the affected as quickly as possible can affect long-term recovery if a considered approach is not adopted. Promoting recovery activities, coordination, and information sharing at national and international levels are crucial to avoid duplication. Mannakkara and Wilkinson’s (2014) modified “Build Back Better” (BBB) concept aims for better resilience by incorporating key resilience elements in post-disaster restoration. This research conducted an investigation into the effectiveness of BBB in the recovery process after the 2010–2011 earthquakes in greater Christchurch, New Zealand. The BBB’s impact was assessed in terms of its five key components: built environment, natural environment, social environment, economic environment, and implementation process. This research identified how the modified BBB propositions can assist in disaster risk reduction in the future, and used both qualitative and quantitative data from both the Christchurch and Waimakariri recovery processes. Semi-structured interviews were conducted with key officials from the Christchurch Earthquake Recovery Authority, and city councils, and supplemented by reviewing of the relevant literature. Collecting data from both qualitative and quantitative sources enabled triangulation of the data. The interviewees had directly participated in all phases of the recovery, which helped the researcher gain a clear understanding of the recovery process. The findings led to the identification of best practices from the Christchurch and Waimakariri recovery processes and underlined the effectiveness of the BBB approach for all recovery efforts. This study contributed an assessment tool to aid the measurement of resilience achieved through BBB indicators. This tool provides systematic and structured approach to measure the performance of ongoing recovery.
