Deep learning and network analysis: Classifying and visualizing accident narratives in construction

Abstract

If headway is to be made to improve safety performance in construction, then there is a need to learn from past accidents. Accident reports provide a useful source of information to make sense as to why and how events occurred. Analyzing such reports, however, can be a lengthy and challenging process as there is a tendency for data to be presented in an unstructured or semi-structured free-text format. Thus, being able to classify and analyze the narrative that surrounds accidents and to better understand their causal nature is a challenge. Text classification using shallow machine learning with sophisticated manual lexical, syntactic, and semantic features engineering has been typically used to mine accident data. However, this approach requires highly skilled experts with domain knowledge to undertake this task. A limited number of studies have employed deep learning models to examine the text of safety reports in construction. In consideration of this limitation, word embedding is used to model the semantic narratives of accidents. Then, a Convolution Neural Network (CNN) model is trained to automatically extract text features and classify accident narratives without manual feature processing. The Latent Dirichlet Allocation (LDA) model is used to examine the interdependency that exists between causal variables to visualize the accident narratives. The proposed automated classification model and LDA-based network analysis method provide a useful approach to enable machine-assisted interpretation of texts-based accident narratives. Moreover, the proposed approach can provide managers with much-needed information and knowledge to improve safety on-site.