Lab safety problems have become an impeding factor that cannot be ignored in normal teaching and scientific research activities at colleges and universities. As the risk factors of lab accidents can be conceptualized as occurring at multiple levels, systematically improving and optimizing lab safety is the crucial route to accident prevention in labs. In this paper, a novel method that integrates a structural equation model (SEM) and system dynamics (SD) is presented to dynamically assess lab safety with the characteristics of insufficient data and uncertainty. On the basis of a questionnaire investigation, the SEM was utilized to determine the influencing factors on lab safety and acquire the path coefficients among these factors, which were embedded into the SD model as the weight of the influencing factors. An illustration was carried out to test and validate the proposed method, and a sensitivity analysis was also conducted to recognize variables contributing the most to the improvement of lab safety. The results demonstrated that the safety input of human and management subsystems is the most effective to improve the lab safety; meanwhile, “safety awareness”, “emergency ability”, “operation skills”, “safety culture” and “safety training” are the top five contributing factors, which can promote lab safety in the shortest time.
Safety of the hazardous chemicals road transportation system (HCRTS) is an important, complex, social, and environmental sensitive problem. The complexity, dynamics, and multi-link features of HCRTS have made it necessary to think beyond traditional risk analysis methods. Based on the relevant literature, Functional Resonance Analysis Method (FRAM) is a relatively new systemic method for modeling and analyzing complex socio-technical systems. In this study, a methodology that integrates FRAM, fuzzy sets, and risk matrix is presented to quantitatively assess the risks factors representing failure function links in HCRTS. As the strength of function links can be illustrated by the RI (risk index) of risk factors identified in failure function links, 32 risk factors representing 12 failure function links were first identified by accident causes analysis and the framework of FRAM. Fuzzy sets were then utilized to calculate the weight of the likelihood and consequence of the risk factors. Finally, according to the assessment results of the identified risk factors by a two-dimensional risk matrix, the weaker function links in the whole HCRTS chain were identified. HCs road companies, regulatory authorities, relevant practitioners, and other stakeholders should pay more attention to these links.
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