Text mining-based construction site accident classification using hybrid supervised machine learning

Safety is one key consideration in the monitoring of construction projects by engineers. Accidents in the project can potentially cause issues, such as workers' injury and progress delay, which lead to financial losses. Generally, accident narratives store all summaries and causes of the related events. Since documentations rapidly use large quantities of resources, the implementation of Artificial Intelligence (AI) begins to seek attention. Nevertheless, in current models, there are still drawbacks, such as weak learning performance and substantial error rate. In this regard, this study develops a hybrid model incorporating Gated Recurrent Unit (GRU) and Symbiotic Organisms Search (SOS), named Symbiotic Gated Recurrent Unit (SGRU). SOS searches the best parameters of GRU to ensure optimal performance. Furthermore, Natural Language Processing is applied to pre-process the text data prior classification process. The experimental result in this study showcases SGRU as the best classification model among other AI models. Therefore, SGRU shares the capability to aid the safety assessments of construction projects.