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Research papers, University of Canterbury Library

High-quality ground motion records are required for engineering applications including response history analysis, seismic hazard development, and validation of physics-based ground motion simulations. However, the determination of whether a ground motion record is high-quality is poorly handled by automation with mathematical functions and can become prohibitive if done manually. Machine learning applications are well-suited to this problem, and a previous feed-forward neural network was developed (Bellagamba et al. 2019) to determine high-quality records from small crustal events in the Canterbury and Wellington regions for simulation validation. This prior work was however limited by the omission of moderate-to-large magnitude events and those from other tectonic environments, as well as a lack of explicit determination of the minimum usable frequency of the ground motion. To address these shortcomings, an updated neural network was developed to predict the quality of ground motion records for all magnitudes and all tectonic sources—active shallow crustal, subduction intraslab, and subduction interface—in New Zealand. The predictive performance of the previous feed-forward neural network was matched by the neural network in the domain of small crustal records, and this level of predictive performance is now extended to all source magnitudes and types in New Zealand making the neural network applicable to global ground motion databases. Furthermore, the neural network provides quality and minimum usable frequency predictions for each of the three orthogonal components of a record which may then be mapped into a binary quality decision or otherwise applied as desired. This framework provides flexibility for the end user to predict high-quality records with various acceptability thresholds allowing for this neural network to be used in a range of applications.

Research papers, University of Canterbury Library

INTRODUCTION: After the 2011 Canterbury earthquake, the provision of school social work was extended into a larger number of schools in the greater Christchurch region to support discussions of their practice priorities and responses in post-earthquake schools. FINDINGS: Two main interpretations of need are reflected in the school social workers’ accounts of their work with children and families. Firstly, hardship-focused need, which represented children as adversely influenced by their home circumstances; the interventions were primarily with parents. These families were mainly from schools in low socioeconomic areas. Secondly, anxiety-based need, a newer practice response, which emphasised children who were considered particularly susceptible to the impacts of the disaster event. This article considers how these practitioners conceptualised and responded to the needs of the children and their families in this context. METHOD: A qualitative study examining recovery policy and school social work practice following the earthquakes including 12 semi-structured interviews with school social workers. This article provides a Foucauldian analysis of the social worker participants’ perspectives on emotional and psychological issues for children, particularly those from middle-class families; the main interventions were direct therapeutic work with children themselves. Embedded within these practice accounts are moments in which the social workers contested the predominant, individualising conceptualisations of need to enable more open-ended, negotiable, interconnected relationships in post-earthquake schools. IMPLICATIONS: In the aftermath of disasters, school social workers can reflect on their preferred practice responses and institutional influences in schools to offer children and families opportunities to reject the prevalent norms of risk and vulnerability.