水利施工事故文本智能分析的BERT-BiLSTM混合模型

水利工程施工往往具有施工环境复杂、施工难度大的特点，施工事故频发。事故报告作为事故分析的文件，通常包含了事故发生的总结和原因，可以作为预防事故发生的依据。然而，目前水利领域的事故分析多依赖于现场管理人员的手工分析，不仅容易出错，而且耗时耗力。此外，现有的模型无法直接对水利事故文本进行高精度的分析和预测。因此，本文提出了一种结合变压器双向编码表示（BERT）和双向长短时记忆模型（BiLSTM）的混合深度学习模型深入分析水利工程施工事故原因。混合模型的上游采用BERT模型生成事故文本的字符级动态特征向量，模型下游基于改进的BiLSTM模型挖掘事故报告文本的语义特征，实现事故报告文本智能分析。最后，通过将本文提出的模型和目前先进的七种深度学习模型进行实验对比，对所提出的混合模型的有效性进行验证。结果表明，本文提出的混合模型优于其他几种深度学习算法，该模型可为水利施工事故的分析与决策提供算法支撑和依据。

Text intelligent analysis for hydraulic construction accidents based on BERT-BiLSTM hybrid model

Construction of a hydraulic project often features a complex environment, difficult operation, and frequent occurrence of accidents. The accident report, as a document for accident analysis, usually includes a summary of the accident and its cause analysis and is used as a basis for accident prevention. However, most of the current analyses on hydraulic construction accidents rely on manual analysis by on-site managers, which is not only of more mistakes but time-consuming and labor-intensive; previous numerical models cannot directly analyze or predict the accident texts with high accuracy. This paper develops a hybrid deep learning model combining the bidirectional encoding representation of transformers (BERT) and the bidirectional long short-term memory model (BiLSTM) for in-depth analysis of the causes of hydraulic construction accidents. This hybrid model uses the BERT model in its upstream to generate a character-level dynamic feature vector of the accident text, and mines the semantic features of the accident report text in its downstream using an improved BiLSTM model, so as to realize an intelligent analysis of accident report texts. Its efficacy is compared with those of seven state-of-the-art deep learning models, and the results show it is superior and useful for analysis and decision-making of hydraulic construction accidents.