Causal factors discovering from Chinese construction accident cases

TL;DR

This paper uses NLP techniques to extract and analyze causal factors from Chinese construction accident reports, revealing previously overlooked factors and improving understanding of accident causes.

Contribution

It introduces a novel NLP-based method to identify and organize causal factors from Chinese accident reports, uncovering neglected causes in construction safety.

Findings

Three neglected causal factors identified

NLP effectively extracts causal information from Chinese texts

Enhanced understanding of accident mechanisms in Chinese construction industry

Abstract

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.