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Images, eqnz.chch.2010

While the whole of the North Island is under drought conditions and parts of the South Island likely to follow suit, I doubt it will happen in Christchurch. With hundreds of earthquake road, water and sewer repairs underway many are spilling hundreds of litres of water per minute, like this one outside my house. It has been running like this fo...

Images, eqnz.chch.2010

Went for a drive down to South New Brighton/Southshore after work today to see what interesting birds I could find on the Estuary (godwits, skuas, terns etc), but passing Jellico Street, I saw this. T-Rex the seismic survey truck from the University of Texas that is visiting the city (first time out of USA). Weighs 30 tonne and from the marks o...

Research papers, The University of Auckland Library

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