# Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects
## 2 Framework of an Integrated Decision Support System for Scaffolding in Construction Projects
### Simulation Modules
#### 2.2.2 Phase 2: Alternative Ranking Module  
In this phase, the user can compare the scaffolding alternatives obtained from Phase 1 if the number of applicable alternatives is more than two. The user manually selects the types with the lowest cost within the same manufacturing series to avoid the type with the higher performance and save operation cost. In this phase, FAHP (Fuzzy Analytical Hierarchy Process) matrices enable the system to generate a ranking list and weights for all of the selection criteria. Then the potential alternatives are compared based on qualitative criteria, and the comparison process enables the user to rank the alternatives against each criterion. Alternatives are compared with one another using a score ranking from 1 to 10, with 1 being the lowest and 10 the highest performance. After the user has inputted the scores for each alternative, they are normalized into values ranging from 0 to 1. They are then multiplied by the weight of each criterion. Finally, the scores of alternatives are compared, and therefore reveal the most suitable scaffolding type. Through this module, the user makes comparisons among the most applicable scaffolding systems generated from Phase 1. Phase 2 starts with the identification of influential factors for decision making and gives a detailed description of the ranking process. The Influential Factors for Scaffolding Decision Making in Phase 2  
Based on an intensive review of the literature and interviews with field experts, the scaffolding selection criteria were identified in this study. To collect data, an investigation survey was designed, and the survey forms were given to the selected experts and practitioners, who have rich working experience in the construction field. In the investigation, each of the experts compared and assessed the relative importance of each two of the criteria with a score from 1/9 (extreme vital unimportant) to 9 (extreme vital important). The relative importance values were prepared to calculate the weights of criteria in subsequent evaluations. The interviewed experts include two superintendents, two project managers, two operation managers, a safety manager, a cost manager and two scaffolding installation workers with rich working experience ranging from 8 to 16 years. The assessment criteria were categorized into four main groups and fifteen sub-criteria as shown in Figure 4. Four main criteria on the first level of hierarchy involve site conditions, productivity, safety, and financial factors, and the second level of hierarchy includes fifteen corresponding sub-criteria [10; 12; 16; 33; 34; 35]. Analysis Method of Fuzzy Analytical Hierarchy Processing  
The AHP (analytical hierarchy processing) method was proposed by [PERSON], and is a widely used approach for multi-criteria decision making problems [36; 37]. In previous studies, AHP is frequently applied to determine the weights through pair-wise comparisons. The scale numbers interpret the relative importance for each pair of criteria from decision-makers' opinions regarding the given problem. However, the actual construction decision-making process is often mixed with uncertain and ambiguous information, which AHP cannot deal with. It can be seen from Figure 4 that this involves a mix of both qualitative and quantitative criteria and sub-criteria. Under such evaluation circumstances, this research employed the FAHP method for the scaffolding selection problem, which provides an effective method to quantify parameters from imprecise and vague information [38; 39; 40; 41]. The FAHP method is more powerful in handling the uncertainties and human judgement subjectivity that significantly affect decision results. Therefore, the value of the criteria could be expressed through linguistic terms followed by its conversion into corresponding fuzzy numbers, and finally can be translated into crisp values. The pairwise comparison numbers can be scaled using the TFN (triangular fuzzy number) to further obtain the accurate weight of each criterion.