Abstract
It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China’s coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing “trajectories” in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
In the coal-mining industry, Patterson and Shappell analyzed 508 coal mine accidents in Australia, and the results indicated that skill-based errors in the Human Factors Analysis and Classification System (HFACS) model were the most common unsafe acts in miners [20]
This paper aims to discover the association patterns of unsafe factors under the guidance of the HFACS framework, which has its own causal and logical characteristics, giving the antecedents and consequents in the association rules a certain causality, such that the rules mined are strong and contain more valuable knowledge than traditional models
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As the resource with the highest proportion in China’s energy consumption, the rapid growth of coal mine production guarantees the steady development of its national economy [1]. The overall safety situation of China’s coal-mining industry has been progressively improving, with the mortality rate per million tons, the number of total deaths and the number of accidents showing a downward trend annually. In 2020, the mortality rate per million tons was only 0.058, which was the lowest in the safety records of
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