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Images, UC QuakeStudies

A Civil Defence staff member completing a Level 1 Rapid Assessment inspection form for a damaged house. Some of the brickwork has collapsed from the outer wall and the awnings over the windows have collapsed.

Images, UC QuakeStudies

A Civil Defence staff member completing a Level 1 Rapid Assessment inspection form for a damaged house. Some of the brickwork has collapsed from the outer wall of the house and the awnings over the windows have collapsed.

Images, eqnz.chch.2010

The kids in this house won't be getting presents from Santa this Christmas; collapsed chimney stack following the magnitude 7.1 earthquake in Christchurch on Saturday 4-9-2010.

Research papers, University of Canterbury Library

Predicting building collapse due to seismic motion is critical in design and more so after a major event. Damaged structures can appear sound, but collapse under following major events. There can thus be significant risk in decision making after a major seismic event concerning the safe occupation of a building or surrounding areas, versus the unknown impact of unknown major aftershocks. Model-based pushover analyses are effective if the structural properties are well understood, which is not valid post-event when this risk information is most useful. This research combines Hysteresis Loop Analysis (HLA) structural health monitoring (SHM) and Incremental Dynamic Analysis (IDA) methods to determine collapse capacity and probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. The nonlinear dynamic analysis method presented enables constant updating of building performance predictions using post-event SHM results. The resulting combined methods provide near real-time updating of collapse fragility curves as events progress, quantifying the change of collapse probability or seismic induced losses for decision-making - a novel, higher resolution risk analysis than previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model. Results show significant potential benefits and a clear evolution of risk. They also show clear need for extending SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010-2011 had significant post-event aftershocks after each main event. Finally, the overall method is generalisable to any typical engineering demand parameter.