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Search and rescue (SAR) teams are the first to respond to emergencies. This could include finding lost hikers, shoring buildings, or aiding people post-disaster. SAR combines orienteering, engineering, field medicine, and communication. Technology use in SAR has been changing with the proliferation of information communication technologies; so, we ask, how are established and emerging technologies used in SAR? Understanding how responders are adopting and adapting these technologies during SAR missions can inform future design and improve outcomes for SAR teams. We interviewed SAR volunteers to contextualize their experiences with technology and triangulated with additional questionnaire data. We discuss how technology use in SAR requires an intersection of expert knowledge and creative problem solving to overcome challenges in the field. This research contributes an understanding of the constraints on and implications for future SAR technologies and SAR operators’ creativity in emergent situations. 

Emergency Management (EM) is experiencing a crisis of technology as technologists have attempted to innovate standard operating procedures with minimal input from EM. Unsurprisingly, there has yet to be a success. Instead, technologists have focused on consumer culture and fostered a slow-moving crisis as the gap between what consumers and EM can do is deep. At present, the most ubiquitous aspect of technology in disaster is its capacity to exacerbate response, create new kinds of disaster, and create consumer expectations that EM cannot meet. In the present work, we highlight how and why technological production needs to shift its ontological premises dramatically to meet the needs of technology for first responders. From supporting practice to taking a few steps back from the bleeding edge, we offer a range of suggestions based on the technological capacities of emergency management in the present and in the future. 

This chapter extends application of a framework proposed by the authors (73, 74) for automated damage detection using strain measurements to study feasibility of using sensors that can measure accelerations, tilts, and displacements. The study utilized three-dimensional (3D) finite element models of double track, riveted, steel truss span, and girder bridge span under routine train loads. The chapter also includes three instrumentation schemes for each bridge span (65) to investigate the applicability of the framework to other bridge systems and sensor networks. Connection damage was simulated by reducing rotational spring stiffness at member ends and various responses were extracted for each damage scenario. The methodology utilizes Supervised Machine Learning to automatically determine damage location (DL) and intensity (DI). Simulated experiments showed that DLs and DIs were detected accurately for both spans with various structural responses and using different instrumentation plans.