| Title |
Automated Extraction of Construction Safety Accident Patterns via Multi-Rule Combination Based on Association Rule Mining |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.3.387 |
| Keywords |
Construction Accidents; Association Rule Mining; Multi-Rule Combination; Accident Pattern Analysis; Safety Management |
| Abstract |
Construction accidents are characterized by the complex interaction of multiple factors, including work processes, work locations, construction
trades, and safety measures. Due to this complexity, conventional accident analysis approaches that focus on single factors have inherent
limitations. To address these limitations, this study applies Association Rule Mining (ARM) to construction accident data and proposes an
automated multi-rule?based accident pattern derivation process that systematically combines and summarizes multiple association rules by
accident type. First, accident data items not aligned with the analysis objectives were identified and removed, and similar items were
integrated into higher-level categories to construct a categorical data-set. Association rules were then extracted, and meaningful rule sets were
identified through the stepwise application of support, confidence, and lift thresholds reflecting the sparse and heterogeneous nature of
construction accident data. The filtered rules were automatically grouped according to common accident outcomes, and composite accident
patterns were quantitatively derived using the number of rules, average support, average confidence, and average and maximum lift values.
The analysis results showed that cut and laceration accidents exhibited a single-concentration risk structure strongly associated with specific
work processes, whereas fall-related accidents demonstrated a complex risk structure arising from the overlap of multiple work and spatial
conditions. Furthermore, analysis of patterns related to safety facilities and personal protective equipment revealed that accidents may still
occur despite the implementation of safety measures, indicating structural limitations in existing accident prevention systems. By extending
conventional single-rule?centered ARM results to the level of accident scenarios through multi-rule combination, the proposed automated
pattern derivation process provides an interpretable representation of recurring accident structures. This approach is expected to serve as a
foundational analytical framework for identifying high-risk construction activities, establishing safety management priorities, and supporting
intelligent construction safety management systems. |