KLUE-BERT-Based Classification of Project Ownership in Korean Construction Accident Records for Comparative Safety Analysis of Public and Private Projects

Project ownership is a critical factor that shapes safety management systems and accident patterns in construction. However, the Ministry of Employment and Labor (MOEL) industrial accident database, which is the largest construction accident database in Korea, does not include project ownership information. To address this limitation, this study developed a fine-tuned KLUE-BERT framework that automatically classifies project ownership using unstructured text fields (site name, client name, and workplace name) in MOEL data. Training data were constructed through manual classification of the 2018–2023 approved statistics and data augmentation. The proposed model achieved high classification performance. Multilayered statistical analyses were conducted using the classified 2014–2023 construction accident data across six key accident variables: accident type, accident cause, construction scale, accident severity, occupation, and worker tenure. The results revealed statistically significant associations between project ownership and all six variables. Public projects exhibited relatively high proportions of accidents involving construction machinery and vehicles, whereas private projects exhibited higher proportions of fall- and scaffold-related accidents. This study presents a novel artificial intelligence-based framework that generates analytical variables absent from the original data and demonstrates its utility through large-scale statistical analysis. The findings provide empirical evidence to support the development of project ownership-specific construction safety policies. Limitations include potential data leakage from pre-split augmentation and generalizability limited to Korean construction data.