## 4 Experimental Results and Discussion
### CLP Modeling Comparison and Results  
To develop the predictive CLP model, four different AI models were developed using the selected factors from HFS as input variables and CLP as the output. The accuracy of the four models was measured by comparing their predictions to the actual field data and calculating two commonly used error measures, mean absolute error (MAE) and RMSE, which are shown in Equations (12) and (13), where \(t_{i}\) and \(y_{i}\) are the actual and predicted CLP values for the \(i\)th instance, respectively, and \(m\) is the number of instances. For this purpose, data were divided into training and testing data sets, in which 70% of data were used for training and 30% for testing. \[RMSE=\sqrt{\sum_{i}{\left({t_{i}-y_{i}}\right)^{2}/m}} \tag{12}\]  
\[MAE=\left({\sum_{i}{\left|{y_{i}-t_{i}}\right|}}\right)/m \tag{13}\] \begin{table}
\begin{tabular}{c c} \hline
**Selected Factor** & **Scale of Measure** \\ \hline (1) Crew size & Integer (Total number of crew members) \\ (2) Crew composition & Proportion (Ratio journeyman to apprentice to helper) \\ (3) Treatment of cartsperson by foreman & 1–5 Predetermined rating \\ (4) Cartsperson trust in foreman & 1–5 Predetermined rating \\ (5) Level of interruption and disruption & Integer (Number of interruptions and disruptions per day) \\ (6) Complexity of task & 1–5 Predetermined rating \\ (7) Working condition (dust and fumes) & 1–5 Predetermined rating \\ (8) Location of work scope (elevation) & Real number (elevation, m) \\ (9) Congestion of work area & Real number (ratio of actual peak manpower to actual average manpower) \\ (10) Fairness in performance review of crew by foreman & 1–5 Predetermined rating \\ (11) Ground conditions & 1–5 Predetermined rating \\ (12) Quality audits & Real number (Number of inspections per month) \\ (13) Risk monitoring and control & 1–5 Predetermined rating \\ (14) Crisis management & 1–5 Predetermined rating \\ \hline \end{tabular}
\end{table} 
Table 1: Input factors for CLP modeling. For development of the ANN model, using MATLAB NN Toolbox, a multilayer feedforward back-propagation network with two hidden layers was considered, and the hidden layer sizes were 5 and 6. The learning rate was set to 0.33, and 200 training cycles were performed. The ANN model resulted in an RMSE of 0.164 and MAE of 0.130 for the training data set, and an RMSE of 0.165 and MAE of 0.135 for the testing data set. The ANFIS model was generated using the ANFIS function of MATLAB Fuzzy Logic Toolbox. The basic learning rules for optimizing membership functions in ANFIS are either hybrid learning or back-propagation gradient descent. Hybrid learning combines the gradient descent and least square methods, and it overcomes the major limitation of the back-propagation method, which is that the learning process gets trapped in the local minima. Therefore, this study used the hybrid learning method. The training data set was grouped using subtractive clustering with an influence range of 0.4, squash factor of 1.15, and accept and reject ratios set at 0.5 and 1.15, respectively. The selected CLP factors were used as input variables and CLP as the output of ANFIS. The ANFIS model resulted in an RMSE of 0.042 and MAE of 0.034 for the training data set and an RMSE = 0.176 and MAE = 0.138 for the testing data set. The ANFIS-GA model, developed using MATLAB, tries to optimize ANFIS parameters, and it showed better performance than ANFIS alone. In this study, the values of 0.2, 0.83, and 60 were assigned for the mutation rate, crossover percentage, and maximum iteration of GA, respectively. These parameters were obtained by trial and error and are the optimum values for this case. Different sizes were tested to find the appropriate population size and based on the results as shown in Table 2, the ANFIS-GA model with a population size of 25 had the best testing performance, which included an MAE of 0.096 for the training data set and MAE of 0.129 for the testing data set. Therefore, a population size of 25 was used in this study.