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Research papers, Lincoln University

Artificial Neural Networks (ANN) as a tool offers opportunities for modeling the inherent complexity and uncertainty associated with socio-environmental systems. This study draws on New Zealand ski fields (multiple locations) as socio- environmental systems while considering their perceived resilience to low probability but potential high consequences catastrophic natural events (specifically earthquakes). We gathered data at several ski fields using a mixed methodology including: geomorphic assessment, qualitative interviews, and an adaptation of Ozesmi and Ozesmi’s (2003) multi-step fuzzy cognitive mapping (FCM) approach. The data gathered from FCM are qualitatively condensed, and aggregated to three different participant social groups. The social groups include ski fields users, ski industry workers, and ski field managers. Both quantitative and qualitative indices are used to analyze social cognitive maps to identify critical nodes for ANN simulations. The simulations experiment with auto-associative neural networks for developing adaptive preparation, response and recovery strategies. Moreover, simulations attempt to identify key priorities for preparation, response, and recovery for improving resilience to earthquakes in these complex and dynamic environments. The novel mixed methodology is presented as a means of linking physical and social sciences in high complexity, high uncertainty socio-environmental systems. Simulation results indicate that participants perceived that increases in Social Preparation Action, Social Preparation Resources, Social Response Action and Social Response Resources have a positive benefit in improving the resilience to earthquakes of ski fields’ stakeholders.

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