# Research on the Correlation of Safety Risk of Railway Bridge Construction Based on Meta-Analysis
## 4 Results
### Visualization of Risk Correlations  
Drawing upon accident chain theory and system theory, the occurrence of safety risk accidents is attributed to the effect and feedback of various risks within the system. Safety accidents are prevalent in traditional industries not only due to the inherent potential and universality of safety risks but also because these risks interweave and interact with one another, gradually accumulating at different stages of production. This correlation increases the probability of overall security risk and leads to a cumulative effect. Additionally, internal cause-and-effect relationships between risks can give rise to large-scale chain effects and secondary risks. For instance, the failure of lifting machinery and subsequent heavy falling can cause geological disasters, resulting in injuries to individuals and posing serious environmental and safety risks to enterprises, society, and the environment. To comprehensively study risk management strategies, it is imperative to adopt a systematic and dynamic perspective. To analyze the correlations between security risk factors, social network analysis was employed. This involved transforming the correlations into a co-occurrence matrix, where a value of 0 or 1 was used to indicate the presence or absence of correlation. UCIENT and Netdraw tools were then utilized to visualize the relationships between secondary risk factors, as depicted in Figure 3. The K-cores algorithm, commonly used to identify closely related sub-graph structures within a graph, was applied to the social network graph. In this algorithm, each vertex must have a minimum degree of K, and all vertices are connected to at least K other nodes in the subgraph. By analyzing the K-cores of the social network graph, relationship sub-graphs with more pronounced correlations to other secondary security risk factors were identified as red and black. This indicates that these two categories of security risk factors exert a significant and extensive influence on other factors. However, it is important to note that the correlation between different safety risk factors is solely based on the frequency of references and qualitative descriptions. It does not directly reflect the quantitative results pertaining to safety risk probability and consequence. The chord diagram utilizes distinct color blocks to represent different factors, with the length of each block determined by the sum of correlation coefficients corresponding to the secondary factors associated with that factor. If a factor exhibits a high correlation coefficient with other factors, its color block will be longer. The connection between color blocks is depicted by bands, which represent the correlation between two factors. The width of the band corresponds to the absolute value of the correlation, indicating a wider band for higher correlation coefficients. The scale of the band connecting to the color blocks represents the absolute value of the corresponding correlation coefficient, thus indicating a larger correlation coefficient for wider bands. Proportional chords employ proportional layouts to demonstrate the similarity or dissimilarity between datasets. Using the method of social network analysis, the frequency of occurrence of each secondary security risk factor in the literature is employed to examine the correlations between different factors within the first level of security risk factors. This approach provides a more intuitive depiction of the relationships between these factors, as depicted in Figure 4. The proportional chord diagram clearly illustrates the interconnectedness and interdependence of the various factors, showing how they are related to one another. Notably, among all the first-level safety risk factors, the environmental factor is the most frequently mentioned, exerting a significant influence on the safety risk factors associated with the bridge itself. Additionally, there is a certain degree of correlation between technical factors and management factors, as well as material and equipment factors.