From Data to Decisions: Closing the Communication Gap in Structural Health Monitoring of Dams

Advances in data science have enabled the large-scale acquisition of structural information in large dams through modern monitoring systems, fostering the development of predictive models, analytical platforms, and advanced visualization tools that provide increasingly detailed diagnostics of structural behavior. Nevertheless, despite this high level of technical sophistication, a critical limitation persists: the difficulty of translating complex analytical outputs—such as time series, dashboards, and multivariate indicators—into information that is clear, relevant, and actionable for operational and strategic decision-making. This paper contends that the primary constraint on the effectiveness of data-driven initiatives in Structural Health Monitoring is not technical but human, arising from a persistent communication gap between data analysts and structural safety managers that hinders the transformation of analytical results into consistent and timely decisions. Consequently, the interpretation of monitoring outputs often remains subjective, weakly standardized, and highly dependent on individual expertise, potentially undermining the reliability of operational responses. Drawing on recent studies and practical evidence from dam monitoring, this work analyzes the underlying causes and manifestations of this gap, evaluates its implications for structural safety, and proposes a set of mitigation strategies, including the standardization of technical language, the development of interdisiplinary skill profiles, the redesign of decision-oriented visualizations, and the implementation of protocols that explicitly connect analytical outcomes with defined operational actions.