Intelligent identification of causative factors and construction of accident chain for crane operation

Cranes are widely used in the construction industry for the installation, maintenance and repair of large, specialized equipment. With the increased use of cranes, there has been more frequent occurrence of crane accidents, which makes it difficult to identify key causative factors and create the crane accident chain due to large amounts of accident data. In this study, an intelligent method was established to determine the key factors and crane accident chain by combining text mining, complex network and integrated interpretive structure modeling (ISM) in this study. Causative factors of crane accidents were firstly identified using text mining based on 203 accidents that occurred from 2011 to 2022 in China. Meanwhile, an indicator system for crane operation causative factors was established by calculating the term frequency-inverse document frequency value. Subsequently, a complex network was constructed to determine the key causative factors. Furthermore, the crane accident chain was established using ISM. Finally, a practical tower crane accident was used to verify the rationality of the accident chain. The results showed that factors such as "cross parallel operation" and "insufficient safety management" were found to play a key role of crane operation. This study can provide a reference for the targeted safety management of crane operation and improve crane operation safety in the construction.