## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **Aim** & **Main Limitation** & **Reference** \\ \hline The advanced deterministic approach & Quantify risk through conservative calculations that included boundary assumptions & - & Present a fixed value It is difficult to respond to accidents & [11; 12] \\  & Quantitatively assessing the safety of both equipment and systems by analyzing the underlying causes of accidents & - & Requiring considerable time and cost for model generation and analysis & \\ Fault tree analysis (FTA) & Quantify risk through conservative calculations that included boundary assumptions & - & Suitable for quantification of operational stage risk & [13; 14; 15] \\  & Quantitatively assessing the safety of both equipment and systems by analyzing the underlying causes of accidents & - & Real situations require detailed explanations that involve the creation of large event trees & [16; 17; 18] \\  & & & & \\ Event tree analysis (ETA) & Inductive analysis of safety through expression of chain form from initial events to accidents & - & Real situations require detailed explanations that involve the creation of large event trees & [16; 17; 18] \\  & Statistical analysis based on the assumption that mathematics subjective probability is actually possible in uncertain situations & - & Various problems can occur when applied to actual problems. & [19; 20; 21] \\  & & & & \\ The Dempster-Shafer (D-S) approach & Statistical analysis dealing with uncertainty that does not fit the probability of the Bayesian rule & - & Data or evidence observed from multiple sources may involve unavoidable biases or errors due to measurement errors and human factors. & [22; 23; 24; 25] \\  & & & & \\ Fuzzy inference approach & Quantify ambiguity that is accepted as consecutive values between 0 and 1 & It is difficult to accurately estimate the degree of uncertainty. & [2; 26; 27] \\ \hline \end{tabular}
\end{table} 
Table 1: The most frequently used applications of risk-related techniques in nuclear power plant field. trees, researchers have suggested ways to simplify the converted fault tree within the event tree [17; 18]. The resulting risk monitoring system, dubbed Risk Angel, was developed to demonstrate the effect of reducing the risk calculation time. However, nuclear energy-related operations are inherently more uncertain and involve more risk factors than other industries, so optimizing a model takes a lot of time and money. Moreover, there is a lack of mathematical data of the type required for the analysis. There are ways to deal with this uncertainty such as the Bayesian approach, the Dempster-Shafer(D-S) approach and fuzzy inference approach. The Bayesian theory is theoretically organized. However, the application of this method requires knowledge of the prior probability and likelihood probability. Therefore, this approach is useful for troubleshooting areas where probabilistic estimates are easy or not complex [21]. The D-S theory is intended to extend uncertainty to segments including the degree of likelihood without limiting the axiom of probability theory [24]. However, all the terminology used in knowledge is handled independently. There is no distinction between uncertainty due to the incompleteness of knowledge and uncertainty due to the ambiguity of the predicate itself used for knowledge. Fuzzy theory is used to solve these problems [2]. The Bayesian theory and the D-S theory represent various estimates numerically. Fuzzy theory, on the other hand, presents these estimates in the form of distributions, which are useful because they are chosen and used according to the analyst's intuition. However, even if the form of the membership function use is flexibly set, the usefulness of deriving meaningful outcomes may be limited if the fuzzy rules are not properly constructed. In previous research, this fuzzy application was limited to managing the risks associated with the construction and operation phases of nuclear power plant projects.