## 2 Literature Review
### Assessing Resilience  
The inadequate performance of traditional project risk management has prompted a growing demand for research on project resilience. However, the absence of a unified theoretical framework and assessment methodology has resulted in a limited number of studies adopting the concept of \"project resilience\" [PERSON] (1973), in comparison to those on organizational resilience. Project resilience assessments can be broadly categorized, including subjective and objective approaches [PERSON] (1973).  
\begin{table}
\begin{tabular}{c c} \hline
**Journal Name** & **Number of Literature** \\ Archives of Civil And Mechanical Engineering & 1 \\ Journal of Civil Engineering and Management & 1 \\ Computers and Operations Research & 1 \\ Structural safety & 1 \\ Fuzzy Sets and systems & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
(1) Subjective assessment methods are employed to identify and quantify resilience at the individual and organizational levels [16]. These methods include self-assessment, expert assessment, and group discussion, which provide insights into respondents' perceptions and assessments of resilience levels, such as the ability to cope with stress and challenges, adaptability, and other aspects [28]. Modeling studies can typically be conducted using techniques such as fuzzy analysis or Bayesian networks [29]. However, studies based on such methods are inherently subjective because they are not based on specific historical data but rather on expert experience. These assessment methods rely excessively on the a priori knowledge of the experts [30], and at the same time, due to the differing experiences of each expert, there is a certain cognitive bias, and the scoring criteria provided are different, which results in a certain degree of bias in the assessment of project resilience.  
(2) An objective method of assessing project resilience can be achieved by quantifying various indicators such as resource reserves, emergency response capacity, speed of recovery, and other attribute data and indicators [7]. These indicators are obtained through historical data, statistical analysis, and other means, such as data on project schedule delays, cost overruns, and disaster losses [31]. An indicator system is typically developed around the concept of resilience, followed by a comprehensive assessment using quantitative methods such as entropy weights [32], hierarchical analysis [33], TOPSIS [34], Monte Carlo simulations [35], and fuzzy logic systems [36]. Monte Carlo simulation and fuzzy logic are two different, but both play an important role in dealing with complex and uncertain problems. Furthermore, project resilience assessment may be based on the performance level change curve of a project following a disruption [15]. From this curve, various attributes may be extracted, including the magnitude of the decrease in performance level, the degree of recovery, the length of recovery, the speed of recovery, and the average cumulative consequences, which may be subjected to a specific mathematical operation (e.g., multiplication) [10; 32; 37]. While these methods provide an accurate assessment of project resilience, they do not provide forecast results of future trends in the project.