Causal factors discovering from Chinese construction accident cases
This addresses safety analysis for the construction industry in China, but it is incremental as it applies existing NLP methods to a specific dataset.
The paper tackled the problem of identifying causal factors in Chinese construction accidents by extracting and analyzing data from incident case texts using NLP technologies, discovering three kinds of neglected causal factors.
In China, construction accidents have killed more people than any other industry since 2012. The factors which led to the accident have complex interaction. Real data about accidents is the key to reveal the mechanism among these factors. But the data from the questionnaire and interview has inherent defects. Many behaviors that impact safety are illegal. In China, most of the cases are from accident investigation reports. Finding out the cause of the accident and liability affirmation are the core of incident investigation reports. So the truth of some answers from the respondents is doubtful. With a series of NLP technologies, in this paper, causal factors of construction accidents are extracted and organized from Chinese incident case texts. Finally, three kinds of neglected causal factors are discovered after data analysis.