ABSTRACT
Coal mine construction projects have high risks, and non-compliant designs generated in the design stage will have adverse effects on subsequent construction and production stages. Therefore, it is of great importance to conduct effective preconstruction compliance inspections on coal mine construction designs. To make the compliance check of coal mine building design more rapid and effective, and to reduce the risks arising from the design phase, this study built a compliance inspection system for coal mine building design from the causes of coal mine accidents, using the Word2Vec word similarity calculation method and BIM platform secondary development technology. The system was tested and was found to be able to detect a 92.82% non-compliant component rate where the correct inspection rate was 97.68%. In addition, the inspection time for a single component was only 0.23 s. The construction of the compliance inspection system based on accident causes has changed the extensive inspection mode in the traditional manual model inspection, and the inspection no longer depends on the experience of inspectors, thus improving the efficiency and accuracy of coal mine building model inspection. The inspection focuses on the building elements with high risks, which achieves the purpose of risk control in the design stage.
Subject(s)
Accidents, Occupational , Coal Mining , China , Causality , CoalABSTRACT
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.
Subject(s)
Accidents, Occupational , Coal Mining , Causality , Coal , Humans , Systems AnalysisABSTRACT
This paper firstly proposes a modified human factor classification analysis system (HFACS) framework based on literature analysis and the characteristics of falling accidents in construction. Second, a Bayesian network (BN) topology is constructed based on the dependence between human factors and organizational factors, and the probability distribution of the human-organizational factors in a BN risk assessment model is calculated based on falling accident reports and fuzzy set theory. Finally, the sensitivity of the causal factors is determined. The results show that 1) the most important reason for falling accidents is unsafe on-site supervision. 2) There are significant factors that influence falling accidents at different levels in the proposed model, including operation violations in the unsafe acts layer, factors related to an adverse technological environment for the unsafe acts layer, loopholes in site management in the unsafe on-site supervision layer, lack of safety culture in the adverse organizational influence layer, and lax government regulation in the adverse external environment layer. 3) According to the results of the BN risk assessment model, the most likely causes are loopholes in site management work, lack of safety culture, insufficient safety inspections and acceptance, vulnerable process management and operation violations.
