This article explores the scope of small-scale radio to create an auditory geography of place. It focuses on the short-term art radio project The Stadium Broadcast, which was staged in November 2014 in an earthquake-damaged sports stadium in Christchurch, New Zealand. Thousands of buildings and homes in Christchurch have been demolished since the February 22, 2011, earthquake, and by the time of the broadcast the stadium at Lancaster Park had been unused for three years and nine months, and its future was uncertain. The Stadium Broadcast constructed a radio memorial to the Park’s 130-year history through archival recordings, the memories of local people, observation of its current state, and a performed site-specificity. The Stadium Broadcast reflected on the spatiality of radio sounds and transmissions, memory, postdisaster transitionality, and the impermanence of place.
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.
