## 5 Research Questions
### Construction Sector  
The construction industry is increasingly adopting these technologies to improve safety practices, although the degree of implementation can vary. Some researchers have developed specific tools and systems tailored to the construction environment. It is worth noting that the application of these technologies may differ across regions, companies, and projects within the construction industry [266]. The effectiveness of these approaches also depends on factors such as data quality, integration with existing safety protocols, and the willingness of stakeholders to adopt innovative solutions [265; 267]. Fuzzy logic deals with uncertainty and imprecision and is an effective tool for solving problems where knowledge uncertainty may arise [73]. The five levels of Bayesian Networks (BNs) include (i) the root causes level, (ii) the triggering events level, (iii) the events level, (iv) the accident level, and (v) the consequences level. To analyze and model the defined safety of an offshore installation, a BN model was created following the proposed five-level framework 82 [164; 268; 269; 270; 271; 272]. Data collection and recording of accidents are minimal and inadequate, especially in construction. Estimating the severity of potential consequences of work-related accidents is a crucial step in any occupational risk assessment process. For this reason, the severity of work accidents in construction is a highly arbitrary process and not a systematic and rigorous evaluation [268]. [PERSON] et al. [269] also propose a fuzzy approach to assessing the severity level of work accidents in the construction sector. In 2009, [PERSON] and [PERSON] [95] used fuzzy Layer of Protection Analysis (fLOPA) as the basis for risk assessment, applied to the transport of flammable substances through large pipelines. They claim that calculations using the LOPA methodology and fuzzy logic  
One of the first efforts to analyze and synthesize safety using fuzzy sets resulted in a proposed method that can be used as an alternative approach to safety analysis, particularly in situations where it is difficult or impossible to solve variations through human logic. Thus, the model helps in many industrial environments [293; 294]. The development of such models for effective occupational health and safety systems has led to the integration of fuzzy logic and multi-criteria decision-making (MCDM) methods [281; 282]. In the first phase, traditional risk analysis factors are positioned, and, in the second phase, preventive actions are arranged using F-TOPSIS [283; 284; 285; 286]. In a recent study [263], a 3D ergonomic risk assessment is proposed for more effective workplace design and modifications. This can evaluate risks using integer numbers to avoid perception errors, improving movement and body posture to reduce potential risks. The model utilizes triangular and trapezoidal functions in terms of the general fuzzy coding model. Another new model [185; 295] consists of three MCDM methods: DEMATEL, AHP or ANP, and the TOPSIS methodology. DEMATEL is used to determine the causal relationships between risks, AHP or ANP to determine the risk weighting factors, and TOPSIS to prioritize risks [296; 297; 298; 300; 301]. The authors claim that the dynamic nature of this model can provide result analysis in many scientific fields. Another promising model for fuzzy occupational risk assessment in any type of construction site is the model proposed by [PERSON] and colleagues [12]. It is a dynamic Takagi-Sugeno fuzzy inference system that enables the configuration of risk factors while considering accidents and injuries as input parameters. It has been applied at various construction sites and shows progress in existing safety strategies.