Meeting the Sustainable Development Goals by 2030 involves transformational change in the business of business, and social enterprises can lead the way in such change. We studied Cultivate, one such social enterprise in Christchurch, New Zealand, a city still recovering from the 2010/11 Canterbury earthquakes. Cultivate works with vulnerable youth to transform donated compost into garden vegetables for local restaurants and businesses. Cultivate’s objectives align with SDG concerns with poverty and hunger (1 & 2), social protection (3 & 4), and sustainable human settlements (6 & 11). Like many grant-supported organisations, Cultivate is required to track and measure its progress. Given the organisation’s holistic objectives, however, adequately accounting for its impact reporting is not straightforward. Our action research project engaged Cultivate staff and youth-workers to generate meaningful ways of measuring impact. Elaborating the Community Economy Return on Investment tool (CEROI), we explore how participatory audit processes can capture impacts on individuals, organisations, and the wider community in ways that extend capacities to act collectively. We conclude that Cultivate and social enterprises like it offer insights regarding how to align values and practices, commercial activity and wellbeing in ways that accrue to individuals, organisations and the broader civic-community.
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.
©2019. American Geophysical Union. All Rights Reserved. Earthquakes have been inferred to induce hydrological changes in aquifers on the basis of either changes to well water-levels or tidal behavior, but the relationship between these changes remains unclear. Here, changes in tidal behavior and water-levels are quantified using a hydrological network monitoring gravel aquifers in Canterbury, New Zealand, in response to nine earthquakes (of magnitudes M w 5.4 to 7.8) that occurred between 2008 and 2015. Of the 161 wells analyzed, only 35 contain water-level fluctuations associated with “Earth + Ocean” (7) or “Ocean” (28) tides. Permeability reduction manifest as changes in tidal behavior and increased water-levels in the near field of the Canterbury earthquake sequence of 2010–2011 support the hypothesis of shear-induced consolidation. However, tidal behavior and water-level changes rarely occurred simultaneously (~2%). Water-level changes that occurred with no change in tidal behavior reequilibrated at a new postseismic level more quickly (on timescales of ~50 min) than when a change in tidal behavior occurred (~240 min to 10 days). Water-level changes were more than likely to occur above a peak dynamic stress of ~50 kPa and were more than likely to not occur below ~10 kPa. The minimum peak dynamic stress required for a tidal behavior change to occur was ~0.2 to 100 kPa.
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines hysteresis loop analysis (HLA) structural health monitoring (SHM) and incremental dynamic analysis (IDA) methods to identify and then analyse collapse capacity and the probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher-resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010–2011 had significant post-event aftershocks.
