**4. Conclusions**

Perceptron neural networks, especially multilayer perceptron networks, are considered to be some of the best neural networks. In this study, it was observed that these networks were able to perform a non-linear mapping with desirable accuracy by selecting a suitable number of layers and neurons. As these neural networks possess the two main features of experimental data-based learning and parallel generalization ability, they are highly suitable for sophisticated systems that are impossible or difficult to model. Artificial neural networks are more accurate in comparison to other methods due to their usage of proven mathematical formulas possessing the lowest possible errors. One of the aspects that limit the usage of artificial neural networks is the difficulty faced when training them. These networks produce better results when they receive a large group of data. However, adjusting the parameters of network training is a difficult task that requires experience and a lot of trial and error. Furthermore, convergence to an incorrect answer, keeping internal information instead of learning it, and requiring a lot of time for training are other difficulties associated with using artificial neural networks.

In this research, two different models, i.e., an artificial neural network model and a multiple regression model, were designed and analyzed in order to improve the traditional earned value management system. The latter model was used as a validation test for the ANN model. Road construction projects in Fars Province, Iran, between 2010 and 2020 were investigated as a case study. Fourteen factors affecting the earned value of these projects were identified. According to the ANN results, "Project plan", "Payment status", "Inflation rate", "Fortuitous events" and "Qualification of project management team" with coefficients of 0.81, 0.65, −0.58, 0.42 and 0.4 were the top five influencing factors, respectively. On the other hand, according to the multiple regression model results, "Risk management", "Plans", "Project schedule", "Relationship among project's parties" and "Conflicts" with standardized coefficients of 0.333, 0.321, 0.311, 0.297 and 0.254, respectively, were the most important factors. A comparison of the two models illustrated that both models result in better results in comparison to the traditional EVM method. Moreover, the ANN model with an MSE of 0.00206 and an R value of 0.896 was selected as the best model.

The methods used in this study could also be used to tackle other problems in the construction industry. The results obtained in this study will help road construction industry members to predict the earned value of future projects more precisely. ANN models are highly recommended by the authors for use in other construction problems. Furthermore, it is suggested that prospective researchers focus on more complex construction projects in order to investigate the performance criteria more deeply [65].

**Author Contributions:** A.B.: Conceptualization, Methodology, Software, Investigation, Writing—Original draft; A.V.: Methodology, Visualization, Validation, Investigation, Writing—Reviewing and Editing, Supervision; J.A.: Writing—Reviewing and Editing, Supervision; J.Š.: Writing—Reviewing and Editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
