Abstract 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.

中文翻译：

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使用混合监督机器学习的基于文本挖掘的工地事故分类

摘要 安全是工程师对建设项目进行监控的关键因素之一。项目中的事故可能会导致问题，例如工人受伤和进度延迟，从而导致经济损失。通常，事故叙述会存储相关事件的所有摘要和原因。由于文档快速使用大量资源，人工智能（AI）的实施开始受到关注。尽管如此，在目前的模型中，仍然存在学习性能差和错误率高等缺点。在这方面，本研究开发了一种包含门控循环单元 (GRU) 和共生生物搜索 (SOS) 的混合模型，称为共生门控循环单元 (SGRU)。SOS 搜索 GRU 的最佳参数以确保最佳性能。此外，自然语言处理用于在分类过程之前对文本数据进行预处理。本研究的实验结果展示了 SGRU 作为其他 AI 模型中最好的分类模型。因此，SGRU 共享协助建设项目安全评估的能力。