Human machine interaction failure modes in maritime grounding accidents: A decision-oriented data driven analysis

Abstract

Grounding accidents in the maritime industry remain a critical safety concern despite advances in ship bridge automation. This study addresses the lack of structured, data-driven representations in maritime accident analysis by applying a dual geometric approach to a database of 30 grounding accident cases. Using Principal Component Analysis (PCA) as a dimensionality filter, the study demonstrates that grounding accidents cannot always be deemed random events but can be governed by a strong, low-dimensional structure. Subsequent Multiple Correspondence Analysis (MCA) identify two primary failure mechanisms as observed: a navigation phase characterized by a collapse in supervisory monitoring which is called “Passive Navigation” and a navigation phase driven by communication breakdowns and decision latency called an “Overloaded Navigation”. Hierarchical clustering reveals that while most situations reflect baseline variability, high-risk accidents emerge from extreme configurations of fatigue, authority gradients, and automation dependence. The findings are contextualized by referring to the classic Swiss Cheese Model (SCM) of human error and they provide a methodological foundation for the development of Decision Support Systems (DSS) and Bridge Resource Management (BRM) strategies aimed at detecting early warning signs of systemic degradation during maritime navigation.

Keywords: Maritime Navigation, Human Machine Interaction, Accident Analysis, Multiple Correspondence Analysis, Maritime Safety

DOI: 10.54941/ahfe1007887