FRAM-FDM: A Quantitative Safety-II Framework for Runway Overrun Risk Assessment Using Functional Resonance Analysis and Flight Data Monitoring

Runway overruns rank among the most frequent and economically significant accident categories in commercial aviation, typically arising from the nonlinear combination of performance variabilities during the approach and landing phases. Existing risk models either rely on historical accident databases or treat runway overrun precursors as independent binary events, overlooking the interactive dynamics of everyday flight operations. This paper introduces FRAM-FDM, a quantitative methodology that integrates the Functional Resonance Analysis Method (FRAM) with Flight Data Monitoring (FDM) to assess and manage operational runway overrun risk from a Safety-II perspective. The methodology deconstructs the approach-and-landing sociotechnical system into functional couplings, quantifies their output variabilities using real flight data recorder (FDR) data, and aggregates them through: (i) linear regression combined with Monte Carlo Simulation (MCS) for probabilistic fleet-level risk estimation; (ii) logistic regression with odds-ratio analysis to rank precursor influence; and (iii) metadata-driven scenario simulation in the FRAM Model Visualiser (FMV). Applied to a case study of 110 landings — including one overrun event — from a business jet fleet, the framework identifies airspeed excess at the runway threshold and brake application profile as the dominant risk contributors, demonstrates synergistic risk amplification when multiple variabilities co-occur, and provides actionable guidance for training and procedure reinforcement. FRAM-FDM advances FRAM toward a fully quantitative, data-driven implementation and aligns FDM programmes with Safety-II principles.