Examining past near-miss reports can provide us with information that can be used to learn about how we can mitigate and control hazards that materialise on construction sites. Yet, the process of analysing near-miss reports can be a time-consuming and labour-intensive process. However, automatic text classification using machine learning and ontology-based approaches can be used to mine reports of this nature. Such approaches tend to suffer from the problem of weak generalisation, which can adversely affect the classification performance. To address this limitation and improve classification accuracy, we develop an improved deep learning-based approach to automatically classify near-miss information contained within safety reports using Bidirectional Transformers for Language Understanding (BERT). Our proposed approach is designed to pre-train deep bi-directional representations by jointly extracting context features in all layers. We validate the effectiveness and feasibility of our approach using a database of near-miss reports derived from actual construction projects that were used to train and test our model. The results demonstrate that our approach can accurately classify ‘near misses’, and outperform prevailing state-of-the-art automatic text classification approaches. Understanding the nature of near-misses can provide site managers with the ability to identify work-areas and instances where the likelihood of an accident may occur.

查看过去的未命中报告可以为我们提供信息，这些信息可用于了解我们如何减轻和控制在建筑工地上出现的危害。但是，分析未命中报告的过程可能是一个耗时且劳动密集的过程。但是，使用机器学习和基于本体的方法进行自动文本分类可用于挖掘此类报告。这样的方法倾向于遭受泛化不强的问题，这可能对分类性能产生不利影响。为了解决此限制并提高分类准确性，我们开发了一种改进的基于深度学习的方法，使用双向理解语言（BERT）来对安全报告中包含的未命中信息进行自动分类。我们提出的方法旨在通过联合提取所有层中的上下文特征来预先训练深度双向表示。我们使用从实际建设项目中获得的，用于训练和测试模型的险兆报告数据库来验证我们方法的有效性和可行性。结果表明，我们的方法可以准确地对“差错”进行分类，并且优于目前流行的最新自动文本分类方法。了解未遂事件的性质可以使站点管理员能够识别工作区域和可能发生事故的情况。我们使用从实际建设项目中获得的，用于训练和测试模型的险兆报告数据库来验证我们方法的有效性和可行性。结果表明，我们的方法可以准确地对“差错”进行分类，并且优于目前流行的最新自动文本分类方法。了解未遂事件的性质可以使站点管理员能够识别工作区域和可能发生事故的情况。我们使用从实际建设项目中获得的，用于训练和测试模型的险兆报告数据库来验证我们方法的有效性和可行性。结果表明，我们的方法可以准确地对“差错”进行分类，并且优于目前流行的最新自动文本分类方法。了解未遂事件的性质可以使站点管理员能够识别工作区域和可能发生事故的情况。