## 3 Methodology
### Quantifying Strategies' Impact on CLP
FCM Expert, developed by [PERSON] et al. [18], is used as a software platform for modeling the proposed FCM model of CLP. Figure 5 shows the flow chart of the proposed framework for constructing the FCM model, which consists of three major tasks, described below. Initial FCM construction included causal relationships among the initial FCM parameters (i.e., the most value-adding factors and CLP) and initial states of the parameters according to the dataset, surveys results, and past studies. Figure 5: Flow chart for construction of FCM model for quantifying improvement strategies’ impact on CLP. Optimal weight searching entails applying the RCGA algorithm to find an optimal weight matrix based on its global search and generalization ability. The weight matrix comprises the causal relationships among the factors. The data used in the RCGA is based on the dataset of [PERSON] and [PERSON] [4; 11] discussed in the methodology section above. For use in the FCM model, the dataset including the value of factors and CLP is normalized between 0 and 1. Figure 6 shows the real-time visualization of the error curve for searching the optimal weight matrix by performing 50 iterations. Optimal weight refining applies the NHL algorithm in order to fine-tune the optimal weight matrix and get closer to the optimized structure. The output of the RCGA is used as the input of the NHL algorithm, thus, no expert knowledge is required for conducting NHL. Based on the refined optimal weight matrix, the final FCM model of CLP is developed as shown in Figure 7, F1, F2, and so on through F19 are the identified most value-adding CLP factors (listed in Table 1), and the directions and values of the arrows demonstrate the direction and strength of causalities among the factors. For example, the strength of causality from factor 4 (F4) \"Owner staff on site\" toward factor 5 (F5) \"Congestion of work area\" is +0.8, which means F4 has a strong positive influence on F5; the strength of causality from factor 19 (F19) \"Cleanliness of work area\" toward factor 2 (F2) \"Complexity of task\" is \(-0.2\), which means F19 has a weak negative influence on F2. By considering the importance of improvement strategies derived from fuzzy TOPSIS, the quantified impact of each strategy on CLP is determined through the steps shown in Figure 8.  
As shown in Figure 8, the final FCM model is run one time without applying any strategies in order to determine the base CLP that equals 0.5310 for the project under study. The base CLP is the current value of the project's CLP. Then, a single strategy is applied to the model according to its rank as shown in Table 3. In the case study, strategy 11, \"Develop clear instructions about the equipment used to transport materials\", is selected first. The model is run and the resulting CLP is called the \"improved CLP\", since it is obtained from applying the CLP improvement strategy to the FCM model. The improved CLP is 0.5452 for strategy 11. Other strategies are selected one by one according to their rank as shown in Table 3, and their improved CLPs are determined, as shown in Table 4.  
Figure 6: Visualization of error curve in the process of finding the optimal weight matrix.