## 2 Literature Review
It primarily examines the issue using criteria based on the riskiness of the work, drawing more from classical techniques such as VIKOR, TOPSIS, and AHP [62]. However, the review emphasizes the value of fuzzy models in occupational risk, as highlighted in eleven of the eighty-eight total research articles. Additionally, one of these eleven articles uses neural networks with fuzzy inference systems, mainly focusing on construction activities [63]. The others are based on specific systems and rules, such as Pythagorean fuzzy AHP and Takagi-Sugeno fuzzy systems, evaluating their use in factories [8,11,12,64]. In 2021, prompted by the rapid urbanization of the global population and the need for the fast development of many megacities [65], and, by extension, the construction of underground rail transportation systems, [PERSON] and colleagues [65] acknowledged the need for a literature review concerning underground construction. Focusing on underground construction and safety, they drew from a literature review between 2010 and 2020, gathering one-thousand-six-hundred-twenty-one articles related to safety and accidents in underground construction. Despite the vast volume of articles, only eleven were found to use fuzzy logic theories, and seven articles used machine learning methods, employing some ANN and BN methods in the background [63,66]. [PERSON] and colleagues [67] conducted a comprehensive literature review on the topic of safety related to artificial intelligence (AI) in industries. They managed a volume of over 5000 articles that had at least one reference to safety through artificial intelligence, of which a subset of 329 successfully secured AI safety using modern techniques. Additionally, one of the key findings of the research, which set its article search to start from 1994, is that, until 2016, the number of published articles remained in the single digits (one or two). There has been a surge in this topic from 2019 onward, where the numbers have reached double digits and are approaching a three-digit number of articles. Moreover, particular attention is being paid to Artificial Neural Networks (ANNs). Security systems in the industry using artificial intelligence have increasingly implemented ANNs, from 32% in 2018 to nearly 70% in 2022 [67]. The authors in [10] carried out a literature review of 124 articles on algorithmic machine learning techniques in the field of risk assessment. This particular literature review studied occupational risks across various sectors. One of the most noteworthy observations of the research is that the automotive industries are adopting techniques that involve machine learning to assess their risks. Furthermore, they argued that Artificial Neural Networks [68,69] combined with machine learning comprise a method that helps to better assess risk. From the analysis, it emerges that ANNs are used more frequently for risk assessment compared to other methods. This is due to the use of non-linear mathematical equations that the ANN technique essentially develops [70]. Additionally, Support VectorMachines (SVMs) play an important role in machine learning for safety evaluation. Another important finding is that only 20% of the articles use real data to create machine learning models, while over 70% use historical data and derive theoretical conclusions [10]. [PERSON] and colleagues [71] conducted a literature review on safety in oil refineries. Oil refining possibly has the highest number of fatal accidents in absolute terms relative to the workforce compared to other industrial sectors. They examined safety risks from a different perspective, primarily focusing on accidents caused by mechanical failure, electrocution, chemical exposure, scaffold failure, and oil spills. The research collected 30 articles dealing with accidents in both floating and land-based oil refining structures. Interestingly, the review found that only one study used the concept of fuzzy logic to quantify risk [72].