Analysis of Construction Accident Reports Based on C-BiLSTM Model

Safety accidents frequently occur in the construction industry, which leads to heavy work in the reports and investigations of safety accidents. Thus, under the background of accelerating the establishment of informationization of construction safety, it is of particular importance to deeply study the informationization of the reports on construction safety accidents and investigations. This paper explored a C-BiLSTM-based approach that combines with deep learning approaches to automatically classify the reports on construction safety accidents and is capable of extracting semantic features from the construction accident reports and automatically classifies them into pre-defined category. The results showed that the proposed C-BiLSTM-based classification performed better in the classification of construction accident reports with less manual intervention and complex engineering. In addition, the reasons and limitations of classification errors were discussed, and suggestions were proposed for improvement. The results of this study can help building safety managers to develop risk management systems.