## 2 Related Work
### Decision Support System  
Decision-making is an inherently complex process, which requires tools that can tackle multidimensional issues and provide the best solutions. Information-based computer systems have been integrated with Decision Support Systems (DSS), making them powerful problem-solving tools [17]. This allows for timely identification of potential solutions and a fair comparison between alternatives according to predetermined characteristics. Furthermore, DSS is a clear outlined process for representing complicated issues and their solutions and adding transparency on decision makers so discovering multipurpose problems can be handled more efficiently [18]. According to [PERSON] [19], DSS can improve decision-making performance and efficiency by providing mechanisms that help to achieve goals and optimize resource usage. These systems enable decision-makers to search various types of information (subjective and objective) to find ideas for the most favorable solutions, meeting competing project demands [20]. Since the 1980s, Multi-Criteria Decision Analysis (MCDA) has been widely recognized as a matrix-based approach within DSS frameworks. MCDA is particularly effective for addressing complex scenarios where projects must simultaneously meet multiple conflicting objectives. MCDA methods enable the clear ranking of variables, criteria, and alternatives while employing weighting systems to prioritize criteria based on their importance [21]. This approach provides decision-makers and analysts with a range of methodologies that integrate human judgment and preferences [22]. DSS can be used for various construction projects, either in building, commercial, and industrial fields or in infrastructure management projects. For instance, regarding bridge management, [PERSON] et al. [23] proposed a DSS for bridge remediation, which utilizes a multi-criteria decision-making system that supports decision-makers through a balanced consideration of multiple criteria. Over the last decades, research has advanced MCDA models by developing algorithms to objectively evaluate individual criteria, further enhancing the utility of these methods in decision-making. Therefore, several MCDA approaches have been presented in the literature over the years. Each multi-criteria method has its own strengths and weaknesses. The choice of method primarily depends on the specific problem, available data, and decision-maker preferences. Different MCDA methods can yield conflicting recommendations, making it difficult for decision-makers to reconcile results and select a final solution, potentially leading to uncertainty and confusion [8]. In an example of bridge research, [PERSON] et al. [24] applied two MCDA methods, the Simple Multi-Attribute Rating Technique (SMART) and the Analytical Hierarchy Process (AHP), which are two commonly employed approaches in various engineering fields and have significant potential for incorporation into asset management of bridge infrastructure. In another study, [PERSON] et al. [25] identified decision-making criteria through a comprehensive process involving literature reviews, surveys, and interviews with industry professionals and modular manufacturers. Utilizing these insights, they developed a streamlined MCDA framework that aids in selecting suitable construction systems and determining optimal modularization levels for building projects, facilitating more informed and efficient decision-making. Furthermore, DSS can be employed for very specific applications, such as work and safety assessment for using tower cranes, as conducted by [PERSON] et al. [26], or Digital Twin development, as proposed by [PERSON] et al. [27]. According to the literature, despite the various applications of MCDA methods in different fields, limited studies on constructability assessment have utilized MCDA tools to make practical and intelligent decisions, allowing decision-makers to choose the optimal solution from suggested options.