Having a quick but reliable insight into the likelihood of damage to bridges immediately after an earthquake is an important concern especially in the earthquake prone countries such as New Zealand for ensuring emergency transportation network operations. A set of primary indicators necessary to perform damage likelihood assessment are ground motion parameters such as peak ground acceleration (PGA) at each bridge site. Organizations, such as GNS in New Zealand, record these parameters using distributed arrays of sensors. The challenge is that those sensors are not installed at, or close to, bridge sites and so bridge site specific data are not readily available. This study proposes a method to predict ground motion parameters for each bridge site based on remote seismic array recordings. Because of the existing abundant source of data related to two recent strong earthquakes that occurred in 2010 and 2011 and their aftershocks, the city of Christchurch is considered to develop and examine the method. Artificial neural networks have been considered for this research. Accelerations recorded by the GeoNet seismic array were considered to develop a functional relationship enabling the prediction of PGAs. http://www.nzsee.org.nz/db/2013/Posters.htm
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.
