# The Full Lifecycle Evolution Model of Accidents: A Case Study of Underground Metal Mines in China
## 4 Discussion
### Comparison with Existing Accident Causation Models  
Accident causation tracing and the formulation of control measures are typical application scenarios for accident causation models. In this study, we comprehensively review the relationships among the factors that may be involved in the evolution of production accidents, clarifying the core role of personnel as the only factor with subjective initiative in the accident evolution process. We use the failure modes of personnel in controlling accident hazards and subsequent response behaviors as the basis for tracing the genesis and development of accidents. This approach effectively distinguishes between the unsafe behaviors of individuals and unsafe conditions in accident causation analysis. Prior to the initiation of an accident, the emphasis is primarily on the unsafe conditions associated with various factors, whereas after the initiation, attention shifts to the unsafe behaviors of individuals [28]. This model effectively resolves issues related to incomplete element analysis and the unclear distinctions between unsafe conditions and unsafe behaviors that may arise in chain-type accident causation models, resulting in a coherent and clear analytical framework. Constructing this framework facilitates more accurate identification of potential accident risks and provides a solid foundation for formulating effective control measures.  
This model is also systematic. The systematization of the accident causation model is reflected in the abstract description of system elements and the organization of their hierarchical structure and complex feedback relationships [10]. For example, the FRAM abstracts each element involved in the analysis into functional modules characterized by six attributes: inputs, preconditions, resources, time, constraints, and outputs [6]. The elements in the model discussed in this study are derived from a synthesis of existing modular accident causation models. During the analysis phase, we conduct an in-depth examination of the possible interactions among these elements, ensuring the comprehensiveness and systematic nature of both the elements and their interactions. Furthermore, this model offers clear analytical logic and a specific framework, with risk information primarily sourced from laws, regulations, and standards. Consequently, researchers only need a certain level of industry experience and the ability to analyze data and extract information to conduct accident analysis or even trace the development paths of accidents within an organization [5]. This enhances the model's operability, reducing both the time and complexity involved in the analysis process.  
Visualization is a crucial method for clarifying accident development paths and enhancing the efficiency of analyzing accident evolution chains. This study has systematically organized and graphically depicted the potential evolutionary logic of accidents [42]. This graphical approach lays a strong foundation for utilizing information technologies to trace the evolution of accidents. By representing the accident development process visually, stakeholders can more intuitively grasp the key nodes and responsible parties involved in an incident. This clarity facilitates data mining to explore the causes of accidents at various levels of granularity. In accident investigations, the constructed accident evolution path model enables investigators to swiftly identify critical links and accountable individuals, thereby improving the overall efficiency of the investigation process.