Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Jinlong Zhao, Huaying Cui, Guru Wang, Jianping Zhang, Rui Yang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
18 Downloads (Pure)


Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.
Original languageEnglish
Article number105054
JournalJournal of Loss Prevention in the Process Industries
Early online date1 Apr 2023
Publication statusPublished online - 1 Apr 2023

Bibliographical note

Funding Information:
This study was sponsored by the National Natural Science Foundation of China (No. 51906253 ), the Fundamental Research Funds for the Central Universities (No. 2020QN05 and No. 2021JCCXAQ01 ).

Publisher Copyright:
© 2023 The Authors


  • University laboratory safety
  • Bayesian network
  • Questionnaire investigation
  • Risk assessment


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