The city of Christchurch has experienced over 10,000 aftershocks since the 4th of September 2010 earthquake of which approximately 50 have been greater than magnitude 5. The damage caused to URM buildings in Christchurch over this sequence of earthquakes has been well documented. Due to the similarity in age and construction of URM buildings in Adelaide, South Australia and Christchurch (they are sister cities, of similar age and heritage), an investigation was conducted to learn lessons for Adelaide based on the Christchurch experience. To this end, the number of URM buildings in the central business districts of both cities, the extent of seismic strengthening that exists in both cities, and the relative earthquake hazards for both cities were considered. This paper will report on these findings and recommend strategies that the city of Adelaide could consider to significantly reduce the seismic risk posed by URM buildings in future earthquake.
Quick and reliable assessment of the condition of bridges in a transportation network after an earthquake can greatly assist immediate post-disaster response and long-term recovery. However, experience shows that available resources, such as qualified inspectors and engineers, will typically be stretched for such tasks. Structural health monitoring (SHM) systems can therefore make a real difference in this context. SHM, however, needs to be deployed in a strategic manner and integrated into the overall disaster response plans and actions to maximize its benefits. This study presents, in its first part, a framework of how this can be achieved. Since it will not be feasible, or indeed necessary, to use SHM on every bridge, it is necessary to prioritize bridges within individual networks for SHM deployment. A methodology for such prioritization based on structural and geotechnical seismic risks affecting bridges and their importance within a network is proposed in the second part. An example using the methodology application to selected bridges in the medium-sized transportation network of Wellington, New Zealand is provided. The third part of the paper is concerned with using monitoring data for quick assessment of bridge condition and damage after an earthquake. Depending on the bridge risk profile, it is envisaged that data will be obtained from either local or national seismic monitoring arrays or SHM systems installed on bridges. A method using artificial neural networks is proposed for using data from a seismic array to infer key ground motion parameters at an arbitrary bridges site. The methodology is applied to seismic data collected in Christchurch, New Zealand. Finally, how such ground motion parameters can be used in bridge damage and condition assessment is outlined. AM - Accepted manuscript
