## 4 Experimental Results and Discussion
### CLP Optimization Results
\[Minimize\left(Z=0.27\times(0.75-CLP_{Prad})^{2}+0.73\times\sum_{i=1}^{16}\left( F_{Proli}-F_{Avgi}\right)^{2}\right) \tag{14}\]  
Based on the selected inputs, the result of the RF-PSO model indicated 0.057 as a minimum value of \(Z\), which is the minimum value of objective function (Equation (14)), and 0.522 was achieved as a maximum value of predicted CLP (\(CLP_{Prad}\)). The optimum value of each factor is shown in Table 6.  
Figure 3: Predicted CLP from sensitivity analysis results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(CLP_{tgt}\) & \(\omega\) & **0.27** & **0.40** & **0.50** & **0.60** & **0.73** & **1.00** \\ \hline \multirow{2}{*}{**0.45**} & \(Z\) & 0.041 & 0.045 & 0.038 & 0.056 & 0.033 & \(1.15\times 10^{-5}\) \\  & \(CLP_{Pred}\) & 0.374 & 0.430 & 0.441 & 0.439 & 0.448 & 0.449 \\ \hline \multirow{2}{*}{**0.60**} & \(Z\) & 0.042 & 0.129 & 0.049 & 0.078 & 0.055 & \(1.19\times 10^{-6}\) \\  & \(CLP_{Pred}\) & 0.386 & 0.565 & 0.586 & 0.599 & 0.596 & 0.599 \\ \hline \multirow{2}{*}{**0.75**} & \(Z\) & 0.057 & 0.049 & 0.079 & 0.124 & 0.116 & 0.0005 \\  & \(CLP_{Pred}\) & 0.522 & 0.561 & 0.616 & 0.649 & 0.671 & 0.721 \\ \hline \multirow{2}{*}{**0.90**} & \(Z\) & 0.071 & 0.190 & 0.184 & 0.186 & 0.157 & 0.032 \\  & \(CLP_{Pred}\) & 0.555 & 0.558 & 0.664 & 0.678 & 0.685 & 0.728 \\ \hline \multirow{2}{*}{**1.00**} & \(Z\) & 0.152 & 0.146 & 0.205 & 0.189 & 0.162 & 0.054 \\  & \(CLP_{Pred}\) & 0.713 & 0.697 & 0.714 & 0.728 & 0.737 & 0.769 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of sensitivity analysis. The optimum value of each factor was obtained from the RF-PSO model as the predicted values for CLP factors (\(F_{\textit{Prodi}}\)) and the deviation of the optimum value from the average value for each factor. In other words, deviation from average value was achieved using Equation (15):  
\[Deviation=F_{\textit{Prodi}}-F_{\textit{Avgi}} \tag{15}\]  
As shown in Table 6, the optimum value of the factors \"Ground condition,\" \"Crisis management,\" and \"Risk monitoring and control\" have the least deviation from the average value of selected factors from dataset (\(F_{\textit{Avgi}}\)) with values of 0.004, \(-\)0.005, and 0.007, respectively. Therefore, these factors do not need major changes to achieve the optimum CLP value, which is 0.522. It is notable in Table 6 that the optimum values of \"Level of interruption and disruption,\" \"Working condition (dust and fumes),\" and \"Fairness in performance review of crew by foreman\" have the largest deviation from the average value of the factors, which are \(-\)0.119, \(-\)0.110, and 0.114, respectively. In other words, \"Level of interruption and disruption\" needs to be reduced to 0.043, \"Working condition (dust and fumes)\" needs to be reduced to 0.108, and \"Fairness in performance review of crew by foreman\" needs to increase to 0.808 in order to obtain the optimum CLP value. Improving factors with high deviation helps companies reach optimum predicted CLP. In order to improve factors that have a high deviation from their average value, a number of improvement strategies and corrective measures can be implemented. For example, for reducing dust and fumes in the working area, preventative maintenance for the air-conditioning system can be conducted. The proposed HFS-RF-PSO model has the potential to benefit construction companies in achieving their preferred labor productivity by applying the minimum changes to factors influencing CLP. Another capability of the proposed model is that companies can define their targeted value for each factor influencing CLP instead of the average value of factors. The results of the model will give them the values of predicted CLP and predicted factors in regard to having the minimum deviation from their targeted values for CLP as well as each factor.