## 5 Research Questions
### Construction Sector
At least two or three need to be combined to provide a model with the help of a technique, whether that is neural networks or fuzzy logic. It is confirmed once again that, where variables predominantly have qualitative characteristics, they are more easily managed by fuzzy sets. In contrast, when there are data, the use of neural networks yields better results than quantitative methods, and the predictions can approach reality to a very high degree. What is the future trend? Figure 17: Distribution of citations for the article by [PERSON] et al. [272] over the years. The integration of fuzzy logic, neural networks, genetic algorithms, and machine learning in the evaluation of occupational risks reflects the interdisciplinary nature of modern risk analysis in workplaces. Many researchers and scholars discuss real-time monitoring and feedback [7; 10; 309]. The integration of real-time data from sensors and other monitoring devices into risk assessment models enables dynamic and adaptive risk management [7]. This can enable faster responses to emerging risks and better prevention strategies. Such an approach would be easier when there is a large volume of data to be managed by techniques and models created by neural networks. As artificial intelligence evolves, with AI technologies becoming more sophisticated, there is increasing emphasis on making these systems explainable and interpretable [134; 310]. Understanding the reasoning behind risk assessments generated by AI models is crucial for gaining trust and acceptance across various fields. Machine learning algorithms are able to constantly learn from new data, enabling risk assessment models to adapt to changing workplace conditions and emerging risks over time. While technology plays a critical role, there is increasing emphasis on understanding and incorporating the human element and factor in risk assessment. This includes human behavior, cognitive factors, and the influence of organizational culture on safety [35; 36; 311]. What emerges from several articles and authors is the need for greater collaboration and interdisciplinary research with the help of new technologies. Cloud-based platforms that facilitate collaboration between different stakeholders in risk assessment, including employers, employees, safety professionals, and regulators, may become more widespread. This enables real-time information sharing and collective risk management. The convergence of various fields, including data science, engineering, and environmental science [133; 229; 312; 313], is likely to continue. Interdisciplinary collaborations can lead to more holistic approaches to understanding and mitigating occupational risks. This translates both to costs for employers and physical harm for employees; hence, both are oriented in the same direction, although from different starting points. The increasing volume of data generated in industrial environments [58; 59] can be leveraged through big data analytics. This involves analyzing large datasets to identify patterns, correlations, and trends that may not be apparent through traditional methods. Forms and protocols should be established to enable the dissemination of these data without concerns from the industries and businesses where they are generated. Industry 4.0, characterized by the integration of smart technologies into manufacturing and industrial processes, may influence risk assessment [61]. The use of smart sensors, connected devices, and data analytics in the context of Industry 4.0 can contribute to more comprehensive occupational risk management. From the analysis of the articles, we observe that fuzzy logic follows the annual trend of the total number of articles. The same is true for the techniques analyzed by neural networks. However, this trend stops in 2019. From that point onward, the role of neural networks is replaced by articles that include machine learning.