Figure 32. Extending the Automatic Detection of Velocities for Slope Monitoring The work presented here emerged from the recognition that addressing this problem requires more than improved signal processing. It requires an operating model in which geotechnical engineering judgement and data science capability are tightly integrated. This paper therefore focuses on the collaborative structure that enabled two prior technical studies on slow movement analysis and automated multi-interval SMA, rather than representing those technical results themselves. The intent is to articulate how collaboration functioned as a critical system component in translating analytics into trusted engineering insights. 2. OBJECTIVES AND SCOPE The primary objective of this work is to document the transdisciplinary collaboration that enabled the development of an interpretable, data driven approach to identifying low velocity slope movement. The study demonstrates how sustained integration between geotechnical engineers and technology specialists supported the translation of analytical techniques into practical engineering decision support. The scope of the work includes the collaborative processes, role definitions, communication practices, and governance mechanisms that underpinned this outcome. It covers how analytical workflows were jointly designed, iteratively refined, and validated against geotechnical understanding of slope behaviour. The scope also includes the architectural concepts required to support future automation and operational deployment of slow movement analysis within existing monitoring environments.
RkJQdWJsaXNoZXIy MTM0Mzk2