## 4 Statistical Analysis and Results
Additionally, genetic algorithms require enormous computational resources, whereas newer techniques are easier to use, implement, and develop compared to GAs. Overall, the decline in the use of genetic algorithms in the subject matter under review is primarily due to the rise of more powerful, efficient, and user-friendly AI techniques that offer better performance and flexibility [94]. In Figure 5, the fields in which the articles under review are applied are presented. The results showed that the overwhelming majority (117 articles) apply these techniques in the industrial sector. The literature review also found that 65 articles outline a general framework for workplace safety, which can be applied across various work environments. A significant number of articles (56) deal with occupational risk in transportation. Moreover, the literature review revealed that 17 articles explore the application of these techniques in the construction sector. Additionally, the same absolute number of articles (11) apply risk assessment techniques to mining--whether underground or surface mining--or in healthcare environments. Table 4 highlights the top 10 most prominent sub-techniques used in the 289 articles based on both quantitative and qualitative data analysis. Researchers in workplace risk assessment utilize one of the four main techniques, often combining them with classical risk assessment methods. About 13.2% of the articles present mathematical models that, when combined with other techniques, create methods with unique characteristics. Moreover, 7.6% of the articles (twenty-two papers) refer to case studies, primarily focusing on how these cases could be solved using neural networks and fuzzy logic. Additionally, nine  
Figure 5: Distribution of risk assessment articles by sector. papers provide theoretical analysis of specific techniques. The number of papers using various models decreases significantly but still incorporates well-known techniques in workplace safety and risk assessment. These techniques evolve into dynamic methods when combined with neural networks, fuzzy logic, and genetic algorithms. The main quantitative or qualitative techniques used are FTA, ANN, BN, LOPA, ANFIS, AHP, QRA, TOPSIS, CREAM, and HAZOP. Data extracted from the database yielded 150 subcategories. Therefore, to facilitate the results in Table A1, it was deemed necessary to display the largest of these subcategories in absolute numbers, totaling 16 (Figure 6). The top five subcategories manage data and mathematical models widely used in the field of risk, while the remaining methods are well-known in workplace risk assessment. Table 5 shows the 10 most frequently cited international journals out of 63 covered in the research. The literature review indicated that, in recent years, the dominance of classical journals in workplace risk assessment has shifted. Nevertheless, 10 journals, mostly focused on workplace safety, cover two-thirds of the articles in the study. The journal with the most frequent application of the examined techniques is JLPPI, with 39 articles. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Number of** & **Percentage** \\  & **Articles** & **Percentage** \\ \hline Mathematical Model & 38 & 13.2\% \\ Case Study & 22 & 7.6\% \\ Theoretical analysis & 9 & 3.1\% \\ ANN model & 8 & 2.8\% \\ FMEA & 8 & 2.8\% \\ Theoretical models & 7 & 2.4\% \\ FTA & 5 & 1.7\% \\ AHP & 4 & 1.4\% \\ Bayesian Networks & 4 & 1.4\% \\ Theoretical foundations & 4 & 1.4\% \\ Mathematical Model & 38 & 13.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Distribution of risk assessment articles by sector. Figure 6: Distribution of articles by modeling. Figure 7 displays the distribution of articles in the top 10 journals by publication frequency. Data from the database revealed that the journal with the most references in this field is JLPPI, with 39 articles. Following closely behind is RESS, with 36 articles, and in third and fourth place are AAP and SS, with 27 articles each.