{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:34:47Z","timestamp":1781760887837,"version":"3.54.5"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Sciences Project of Ministry of Education in China","award":["23YJAZH123"],"award-info":[{"award-number":["23YJAZH123"]}]},{"name":"Natural Science Foundation Project of Tianjin","award":["24JCYBJC00810"],"award-info":[{"award-number":["24JCYBJC00810"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems.<\/jats:p>","DOI":"10.3390\/systems14010099","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:27:53Z","timestamp":1768570073000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Risk Assessment of Coal Mine Ventilation System Based on Fuzzy Polymorphic Bayes: A Case Study of H Coal Mine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1836-5879","authenticated-orcid":false,"given":"Jin","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Management, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Management, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Management, Tianjin University of Technology, Tianjin 300384, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.resourpol.2023.104425","article-title":"Accident case-driven study on the causal modeling and prevention strategies of coal-mine gas-explosion accidents: A systematic analysis of coal-mine accidents in China","volume":"88","author":"Wang","year":"2024","journal-title":"Resour. Pol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.psep.2020.01.024","article-title":"Root causes of coal mine accidents: Characteristics of safety culture deficiencies based on accident statistics","volume":"136","author":"Zhang","year":"2020","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.psep.2018.12.007","article-title":"An impacting factors analysis of miners\u2019 unsafe acts based on HFACS-CM and SEM","volume":"122","author":"Liu","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102777","DOI":"10.1016\/j.resourpol.2022.102777","article-title":"Reform and development of coal mine safety in China: An analysis from government supervision, technical equipment, and miner education","volume":"77","author":"Wang","year":"2022","journal-title":"Resour. Pol."},{"key":"ref_5","first-page":"207","article-title":"The Impact of the Chinese Coal Economy on Coal Mine Deaths from 1992 to 2021","volume":"41","author":"Kang","year":"2024","journal-title":"Min. Metall. Explor."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.psep.2022.04.054","article-title":"Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques","volume":"162","author":"Li","year":"2022","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Niu, L., Zhao, J., and Yang, J. (2023). Risk Assessment of Unsafe Acts in Coal Mine Gas Explosion Accidents Based on HFACS-GE and Bayesian Networks. Processes, 11.","DOI":"10.3390\/pr11020554"},{"key":"ref_8","first-page":"46","article-title":"A comprehensive evaluation of intelligent coal mine ventilation systems in the internet of things","volume":"13","author":"Xu","year":"2023","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s12665-021-10144-4","article-title":"A reinforcement method of floor grouting in high-water pressure working face of coal mines: A case study in Luxi coal mine, North China","volume":"81","author":"Zhai","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/cplx.21538","article-title":"Classification of structural complexity for mine ventilation networks","volume":"21","author":"Wei","year":"2015","journal-title":"Complexity"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bi, Q., Li, Y., and Shen, C. (2021). Screening of evaluation index and construction of evaluation index system for mine ventilation system. Sustainability, 13.","DOI":"10.3390\/su132111810"},{"key":"ref_12","unstructured":"Hartman, H.L., Mutmansky, J.M., Ramani, R.V., and Wang, Y. (1997). Mine Ventilation and Air Conditioning, John Wiley & Sons."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/BF02515663","article-title":"Reliability analysis of ventilation systems","volume":"22","author":"Petrov","year":"1986","journal-title":"Sov. Min. Sci."},{"key":"ref_14","first-page":"24","article-title":"The evaluation method of mine ventilation system","volume":"9","author":"Huang","year":"1983","journal-title":"Saf. Coal Mines"},{"key":"ref_15","first-page":"153","article-title":"AHP-FCE based reliability assessment of coal mine ventilation system","volume":"22","author":"Ding","year":"2013","journal-title":"J. Nat. Disasters"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104234","DOI":"10.1016\/j.coal.2023.104234","article-title":"Predicting methane emissions and developing reduction strategies for a Central Appalachian Basin, USA, longwall mine through analysis and modeling of geology and degasification system performance","volume":"270","author":"Karacan","year":"2023","journal-title":"Int. J. Coal Geol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108048","DOI":"10.1016\/j.engappai.2024.108048","article-title":"Ventilation on demand in underground mines using neuro-fuzzy models: Modeling and laboratory-scale experimental validation","volume":"133","author":"Ihsan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.ergon.2018.08.009","article-title":"Human factors analysis of major coal mine accidents in China based on the HFACS-CM model and AHP method","volume":"68","author":"Liu","year":"2018","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.psep.2020.01.003","article-title":"Risk assessment of gas explosion in coal mines based on fuzzy AHP and bayesian network","volume":"135","author":"Li","year":"2020","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5388","DOI":"10.1016\/j.eswa.2010.10.016","article-title":"Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects","volume":"38","author":"Vidal","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jclepro.2019.04.133","article-title":"Risk assessment for long-distance gas pipelines in coal mine gobs based on structure entropy weight method and multi-step backward cloud transformation algorithm based on sampling with replacement","volume":"227","author":"Liang","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107600","DOI":"10.1016\/j.cie.2021.107600","article-title":"Monitoring the coefficient of variation: A literature review","volume":"161","author":"Jalilibal","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1007\/s11771-021-4646-5","article-title":"Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region","volume":"28","author":"Yan","year":"2021","journal-title":"J. Cent. South Univ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s12613-020-2002-7","article-title":"Risk management for mine closure: A cloud model and hybrid semi-quantitative decision method","volume":"27","author":"Cui","year":"2020","journal-title":"Int. J. Miner. Metall. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.psep.2021.08.023","article-title":"Coal and gas outburst prediction model based on extension theory and its application","volume":"154","author":"Wang","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108374","DOI":"10.1016\/j.ress.2022.108374","article-title":"Risk assessment of buried gas pipelines based on improved cloud-variable weight theory","volume":"221","author":"Chen","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.3390\/su12062294","article-title":"Coal resource security assessment in China: A study using entropy-weight-based TOPSIS and BP neural network","volume":"12","author":"Yang","year":"2020","journal-title":"Sustainability"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17115","DOI":"10.1038\/s41598-022-22098-z","article-title":"Mine ventilation system reliability evaluation based on a Markov chain","volume":"12","author":"Liu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9579500","DOI":"10.1155\/adce\/9579500","article-title":"Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm","volume":"2025","author":"Jing","year":"2025","journal-title":"Adv. Civ. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.psep.2023.02.058","article-title":"An approach towards the implementation of a reliable resilience model based on machine learning","volume":"172","author":"Vairo","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.jes.2022.03.030","article-title":"Key emergency response technologies for abrupt air pollution accidents in China","volume":"123","author":"Duan","year":"2023","journal-title":"J. Environ. Sci."},{"key":"ref_32","first-page":"1209","article-title":"Analysis of accident risk factors in chemical industry based on ISM-BN","volume":"238","author":"Ma","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part O J. Risk Reliab."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/00223131.2018.1551814","article-title":"A summary of the \u2018ALARP\u2019 principle and associated thinking","volume":"56","author":"Hurst","year":"2019","journal-title":"J. Nucl. Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107222","DOI":"10.1016\/j.ress.2020.107222","article-title":"On the importance of systems thinking when using the ALARP principle for risk management","volume":"204","author":"Langdalen","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jsr.2022.04.002","article-title":"Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network","volume":"82","author":"Li","year":"2022","journal-title":"J. Saf. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.psep.2021.03.017","article-title":"Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident","volume":"149","author":"Guo","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhao, J., and Shao, L. (2023). Risk Assessment of Coal Mine Gas Explosion Based on Fault Tree Analysis and Fuzzy Polymorphic Bayesian Network: A Case Study of Wangzhuang Coal Mine. Processes, 11.","DOI":"10.3390\/pr11092619"},{"key":"ref_38","first-page":"216","article-title":"Method to Determine Index Weight Based on S Curve","volume":"42","author":"He","year":"2015","journal-title":"Comput. Sci."},{"key":"ref_39","first-page":"485","article-title":"Exploring worker experience as a predictor of routine and non-routine safety performance outcomes in the mining industry","volume":"39","author":"Hoebbel","year":"2022","journal-title":"Min. Metall. Explor."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.psep.2023.01.060","article-title":"A BN-based risk assessment model of natural gas pipelines integrating knowledge graph and DEMATEL","volume":"171","author":"Bai","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104883","DOI":"10.1016\/j.jlp.2022.104883","article-title":"BN & CFD-based quantitative risk assessment of the natural gas explosion in utility tunnels","volume":"80","author":"Bai","year":"2022","journal-title":"J. Loss Prev. Process Indust."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"19","DOI":"10.17159\/2411-9717\/1506\/2023","article-title":"Development status of coal mining in China","volume":"123","author":"Liu","year":"2023","journal-title":"J. South. Afr. Inst. Min. Metall."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.psep.2019.10.014","article-title":"Statistical analysis the characteristics of extraordinarily severe coal mine accidents (ESCMAs) in China from 1950 to 2018","volume":"133","author":"Zhang","year":"2020","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.psep.2018.11.013","article-title":"A statistical analysis of coalmine fires and explosions in China","volume":"121","author":"Zhu","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105295","DOI":"10.1016\/j.resourpol.2024.105295","article-title":"Prediction of coal mine risk based on BN-ELM: Gas risk early warning including human factors","volume":"98","author":"Yu","year":"2024","journal-title":"Resour. Pol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.psep.2019.03.029","article-title":"Risk assessment of mine ignition sources using fuzzy Bayesian network","volume":"125","author":"Li","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_47","first-page":"3484","article-title":"Risk assessment of gas explosion in coal mines based on game theory and Bayesian network","volume":"49","author":"Lin","year":"2024","journal-title":"J. China Coal Soc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.ijmst.2018.07.007","article-title":"Assessment of gas and dust explosion in coal mines by means of fuzzy fault tree analysis","volume":"28","author":"Shi","year":"2018","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.ssci.2011.08.003","article-title":"Data mining applications in evaluating mine ventilation system","volume":"50","author":"Cheng","year":"2012","journal-title":"Saf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/09537287.2019.1680891","article-title":"Supply chain multi-state risk assessment using universal generating function","volume":"31","author":"Mohib","year":"2020","journal-title":"Prod. Plan. Control"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"107043","DOI":"10.1016\/j.ress.2020.107043","article-title":"A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study","volume":"202","author":"Abrishami","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yang, J., Wei, J., and Zhao, J. (2025). Risk assessment of miners\u2019 unsafe acts in intelligent coal mines and conventional coal mines based on HFACS and ISM-BN model. Cogn. Technol. Work, 1\u201313.","DOI":"10.1007\/s10111-025-00841-6"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Niu, L., Zhao, J., and Yang, J. (2023). An Impacting Factors Analysis of Unsafe Acts in Coal Mine Gas Explosion Accidents Based on HFACS-ISM-BN. Processes, 11.","DOI":"10.3390\/pr11041055"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105420","DOI":"10.1016\/j.ssci.2021.105420","article-title":"Applications of artificial intelligence for coal mine gas risk assessment","volume":"143","author":"You","year":"2021","journal-title":"Saf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104492","DOI":"10.1016\/j.ssci.2019.09.015","article-title":"Applications of machine learning methods for engineering risk assessment\u2014A review","volume":"122","author":"Hegde","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1007\/s00439-012-1194-y","article-title":"Risk estimation and risk prediction using machine-learning methods","volume":"131","author":"Kruppa","year":"2012","journal-title":"Hum. Genet."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/1\/99\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:40:39Z","timestamp":1768570839000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/1\/99"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["systems14010099"],"URL":"https:\/\/doi.org\/10.3390\/systems14010099","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]}}}