## 3 Methodology and Research Design
### Fuzzy Delphi Method  
FDM evolved from the Delphi Method, which was developed for prediction and decision making by the RAND Corporation (Santa Monica, CA, USA) in 1948. The Delphi Method is a structured group-communication method and cross-field integration technique [11]. It emphasizes particular issues by asking experts to answer anonymous questionnaires in many rounds, and then leads experts to systematically develop a written consensus on these complex issues using their professional knowledge, experience, and opinions. Structured communication includes feedback based on each group member's message and knowledge, assessments of the group's judgement perspective, the probability of correction of personal views, anonymous responses, and so on. Thus, its information sources are diverse [12].  
The Delphi Method has been modified several times. In order to solve the problem of semantic ambiguity, [PERSON], [PERSON], and [PERSON] integrated the Fuzzy Set with the Delphi Method in 1985. [PERSON] adopted the concepts of cumulative frequency distribution and fuzzy integrals, integrated experts' responses into fuzzy numbers, proposed FDM, and opened its application in related research fields [13]. The steps for using FDM as a tool for screening assessment criteria are as follows:  
1. Set Up Influential Assessment Projects and Decision-Making Groups  
The first step of conducting FDM is to set up assessment projects and give a possible interval value. The minimum value is the most conservative cognitive value of the experts' quantitative score; and \"the maximum\" is the most optimistic cognitive value of the experts' quantitative score.  
2. Collect Opinions from Different Groups  
To develop statistics based on assessment project \"i\", it's necessary to collect every expert's evaluation of the most conservative and optimistic cognitive value, eliminate extreme values in two standard deviations, and separately calculate the minimum \(C_{L}^{i}\), \(O_{L}^{i}\); the geometric mean of \(C_{M}^{i}\), \(O_{M}^{i}\); and the maximum \(C_{UL}^{i}\), \(O_{UL}^{i}\) from the remaining most conservative and optimistic cognitive values.  
Figure 4: Procedure of crash detection between Industry Foundation Classes (IFC) and the system engine: (**a**) the engine system gets coordinates of treads and landings in IFC; (**b**) crash detection; (**c**) crash recording.  
Set up triangular fuzzy numbers of the most conservative and optimistic cognitive value from every calculated assessment project \"i\" by applying \(C^{i}=(C^{i}_{L},C^{i}_{M},C^{i}_{U})\) and \(O^{i}=(O^{i}_{L},O^{i}_{M},O^{i}_{U})\), as illustrated in Figure 5.  
1. Inspect Expert Consensus
2. Inspect Expert Consensus
3. Inspect Expert Consensus
4. Inspect Expert Consensus
5. If \(C^{i}_{U}\leq O^{i}_{L}\), then the value of the importance of consensus is \(G^{i}=\left(C^{i}_{M}+O^{i}_{M}\right)/2\).
6. If \(C^{i}_{U}>O^{i}_{L}\), and the gray zone of fuzzy relation \(Z^{i}=C^{i}_{U}-O^{i}_{L}\) is less than \(M^{i}=O^{i}_{M}-C^{i}_{M}\), which represents the interval range of optimistic cognitive mean and conservative cognitive mean, then Gi equals to the fuzzy set, which is computed by intersecting the relation between two triangular fuzzy numbers and calculating the maximum value to obtain the quantified scores.
7. If \(C^{i}_{U}>O^{i}_{L}\), and the gray zone of fuzzy relation \(Z^{i}=C^{i}_{U}-O^{i}_{L}\) is more than \(M^{i}=O^{i}_{M}-C^{i}_{M}\), which represents the interval range of optimistic cognitive mean and conservative cognitive mean, then it implies experts' inconsistent opinions and the repeated questionnaire survey need to be reconducted until Gi is obtained.