**2. Methodology 2. Methodology**

The methodology of the current study was determined according to the research aim. The main purpose of this research was to improve the prediction of the traditional EVM system in Fars road construction projects using an artificial neural network, as well as comparing it with a multiple regression model. The abovementioned main aim can be divided into three stages. Firstly, factors affecting the earned value of Fars road construction projects were determined using the existing literature. An artificial neural network was built in MATLAB, and the identified factors were introduced to the ANN model. In the next stage, the identified factors were prioritized in MATLAB using the ANN model. Finally, multiple regression was used as the analyzing tool, and the obtained results were compared with the ANN model. The abovementioned stages are summarized in Figure 1. The methodology of the current study was determined according to the research aim. The main purpose of this research was to improve the prediction of the traditional EVM system in Fars road construction projects using an artificial neural network, as well as comparing it with a multiple regression model. The abovementioned main aim can be divided into three stages. Firstly, factors affecting the earned value of Fars road construction projects were determined using the existing literature. An artificial neural network was built in MATLAB, and the identified factors were introduced to the ANN model. In the next stage, the identified factors were prioritized in MATLAB using the ANN model. Finally, multiple regression was used as the analyzing tool, and the obtained results were compared with the ANN model. The abovementioned stages are summarized in Figure 1.

**Figure 1.** Research methodology according to the study aim**. Figure 1.** Research methodology according to the study aim.

#### *2.1. Predicting Earned Value Using Artificial Neural Network 2.1. Predicting Earned Value Using Artificial Neural Network*

Intelligent dynamic systems, such as ANNs, have been under researchers' focus recently [44– 51]. ANNs are able to identify the relationship among data by analyzing them and to then exploit this relationship in further analyses [52]. In fact, these computational intelligence-based systems attempt to model the neurosynaptic structure of the brain and are able to contribute to estimation, prediction and categorization problems effectively [53]. Generally, ANNs consist of three layers, namely, the input, hidden and output layers. Each of the abovementioned layers possesses its own neurons. It is important to mention that the number of hidden layers may be more than one according to the problem. In the current study, a multilayer perceptron network was used. Intelligent dynamic systems, such as ANNs, have been under researchers' focus recently [44–51]. ANNs are able to identify the relationship among data by analyzing them and to then exploit this relationship in further analyses [52]. In fact, these computational intelligence-based systems attempt to model the neurosynaptic structure of the brain and are able to contribute to estimation, prediction and categorization problems effectively [53]. Generally, ANNs consist of three layers, namely, the input, hidden and output layers. Each of the abovementioned layers possesses its own neurons. It is important to mention that the number of hidden layers may be more than one according to the problem. In the current study, a multilayer perceptron network was used.

#### 2.1.1. Input Data 2.1.1. Input Data

Variables affecting the status of the project must be identified in order to investigate its future status. In fact, these variables are the input data of the artificial neural network. In this study, 14 factors affecting a project's success were identified by investigating the existing literature, including books, journal papers and documents from the Fars State Road Administration. Due to the high sensitivity of this paper's topic, the authors were not able to reduce the abovementioned number of factors. Some of the variables possessed numerical values, such as inflation rate. The inflation rate was derived from the Central Bank of Iran. However, there were variables that were not numerical, such as the qualification of the project management team. The abovementioned data were then quantified by scoring the variables from 1 to 5, where 1 and 5 stand for the worst and best status of a variable, respectively. In order to make it clearer, the qualitive status of a variable and its Variables affecting the status of the project must be identified in order to investigate its future status. In fact, these variables are the input data of the artificial neural network. In this study, 14 factors affecting a project's success were identified by investigating the existing literature, including books, journal papers and documents from the Fars State Road Administration. Due to the high sensitivity of this paper's topic, the authors were not able to reduce the abovementioned number of factors. Some of the variables possessed numerical values, such as inflation rate. The inflation rate was derived from the Central Bank of Iran. However, there were variables that were not numerical, such as the qualification of the project management team. The abovementioned data were then quantified by scoring the variables from 1 to 5, where 1 and 5 stand for the worst and best status of a variable, respectively. In order to make it clearer, the qualitive status of a variable and its corresponding

quantitative value are illustrated in Table 1. Ten questionnaires were filled out by experts for each project. Thus, 500 questionnaires were used for data gathering.


**Table 1.** Qualitive status of a variable and its corresponding value for analysis.

Using Microsoft Project files of the studied projects, the Cost Performance Index (*CPI*) of each project was extracted. Then, using Microsoft Excel, Mean Squared Error (*MSE*) was calculated. This error was used to compare the results of the ANN, multiple regression and the traditional EVM method. The BOX-COX method was used in order to normalize data using SPSS software. Then, the obtained data were exported to MATLAB software for further stages. *CPI* and *MSE* formulas are presented as follows [1,8,54,55]:

$$MSE = \frac{\sum (desired\ output - predicted\ output)2}{no\ of\ data} \tag{1}$$

$$\text{CPI} = \frac{\text{BCWP}}{\text{ACWP}} \tag{2}$$

where *BCWP* and *ACWP* stand for the actual cost of the work performed and the budgeted cost of the work performed, respectively.
