Using the ISM-ANP-SD Combination Model to Explore the Mechanism and Intervention Strategies of Influencing Factors of Coal Mine Safety System

Overview

Journal Front Public Health

Specialty Public Health

Date 2022 Dec 12

PMID 36504987

Authors

Xue Yang

Qiyu Xing

Kang Tian

Chen Liu

Juan Yang

Affiliations

Soon will be listed here.

Abstract

Background: With the intelligent construction of coal mines, the number of coal mine accidents is gradually decreasing, but the complexity of accidents is increasing. Understanding the interaction mechanism among the influencing factors of the coal mine safety system is an essential part of improving and enhancing the safety of the coal mine system.

Methods: The descriptive, structural model-network hierarchical analysis (ISM-ANP) was used to explore the interaction between the factors influencing the coal mine safety system and determine each factor's importance. A system dynamics simulation model was constructed to clarify the mechanism of each factor's effect on the safety system.

Results: The results show that Individual miners' factors directly influence coal mine system safety, organizational management factors, and group factors indirectly influence system safety and play the role of macro regulation. The intelligent system is the most profound factor influencing system safety. There are apparent differences in the influence of different subsystems on system safety, with organizational management having the most significant influence on system safety, followed by individual miners and group factors, and intelligent system factors and external environmental factors having a more negligible influence on system safety.

Conclusion: There is a complex interaction between the factors affecting the safety of the coal mine system, and there are apparent differences in the influence of different subsystems on the safety level of the coal mine system. This study puts forward the intervention strategy to improve the safety of the coal mine system, which provides theoretical support and method guidance for preventing coal mine accidents and improving the safety level of the coal mine system.

Citing Articles

Research on the mechanism of human-machine security collaboration of miners considering automation trust.

Yang J, Yang X, Chai S, Ni L, Wang X, Pan L Heliyon. 2024; 10(23):e39456.

PMID: 39687112 PMC: 11648194. DOI: 10.1016/j.heliyon.2024.e39456.

The application obstacles of BIM technology in green building project and its key role path analysis.

Meng G, Hu H, Chen L Sci Rep. 2024; 14(1):30330.

PMID: 39639080 PMC: 11621689. DOI: 10.1038/s41598-024-81360-8.

References

Maass U, Kuhne F, Hahn D, Weck F . Group cohesion in group-based personal practice. Behav Cogn Psychother. 2021; 50(1):28-39. DOI: 10.1017/S1352465821000369. View

Beller J, Heesen M, Vollrath M . Improving the driver-automation interaction: an approach using automation uncertainty. Hum Factors. 2014; 55(6):1130-41. DOI: 10.1177/0018720813482327. View

Korber M, Baseler E, Bengler K . Introduction matters: Manipulating trust in automation and reliance in automated driving. Appl Ergon. 2017; 66:18-31. DOI: 10.1016/j.apergo.2017.07.006. View

Bandura A . Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977; 84(2):191-215. DOI: 10.1037//0033-295x.84.2.191. View

Yang L, Birhane G, Zhu J, Geng J . Mining Employees Safety and the Application of Information Technology in Coal Mining: Review. Front Public Health. 2021; 9:709987. PMC: 8416457. DOI: 10.3389/fpubh.2021.709987. View

Chavaillaz A, Wastell D, Sauer J . System reliability, performance and trust in adaptable automation. Appl Ergon. 2015; 52:333-42. DOI: 10.1016/j.apergo.2015.07.012. View

Yu M, Li J . Psychosocial safety climate and unsafe behavior among miners in China: the mediating role of work stress and job burnout. Psychol Health Med. 2019; 25(7):793-801. DOI: 10.1080/13548506.2019.1662068. View

Zhang Y, Shao W, Zhang M, Li H, Yin S, Xu Y . Analysis 320 coal mine accidents using structural equation modeling with unsafe conditions of the rules and regulations as exogenous variables. Accid Anal Prev. 2016; 92:189-201. DOI: 10.1016/j.aap.2016.02.021. View

Stowers K, Oglesby J, Sonesh S, Leyva K, Iwig C, Salas E . A Framework to Guide the Assessment of Human-Machine Systems. Hum Factors. 2017; 59(2):172-188. DOI: 10.1177/0018720817695077. View

10.

Korner U, Muller-Thur K, Lunau T, Dragano N, Angerer P, Buchner A . Perceived stress in human-machine interaction in modern manufacturing environments-Results of a qualitative interview study. Stress Health. 2019; 35(2):187-199. DOI: 10.1002/smi.2853. View

11.

Bai G, Xu T . Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model. Comput Intell Neurosci. 2022; 2022:5233845. PMC: 8938146. DOI: 10.1155/2022/5233845. View

12.

Salmon P, Plant K . Distributed situation awareness: From awareness in individuals and teams to the awareness of technologies, sociotechnical systems, and societies. Appl Ergon. 2021; 98:103599. DOI: 10.1016/j.apergo.2021.103599. View

13.

Bennett-Levy J, Lee N . Self-practice and self-reflection in cognitive behaviour therapy training: what factors influence trainees' engagement and experience of benefit?. Behav Cogn Psychother. 2012; 42(1):48-64. DOI: 10.1017/S1352465812000781. View