Providing a pattern to improve the productivity of earthmoving machinery in infrastructure projects using machine learning algorithms (Case Study: Gas pipeline)

Considering the outstanding position of the country in the world from the point of view of energy resources, especially the gas field, the implementation of energy infrastructure projects, especially the gas pipeline, is essential. Nevertheless, one of the main challenges in this type of projects is the lack of proper productivity of resources (machinery, etc.). Therefore, the main goal of the current research is to improve the productivity of earthmoving machinery for gas pipeline construction projects, with the help of machine learning algorithms. In this research, by using library studies, documents (daily reports of seven gas transmission pipeline projects), expert judgment, text mining method (and Rapidminer software), effective criteria for determining the productivity of earthmoving machinery in gas pipeline construction projects, identified and final became. In short, the main result of the current research indicates that the prediction of the excavation volume, through the predictive algorithm (as a basis for optimizing the productivity of earthmoving machinery) and also the classification algorithm and using the deep learning model (as the selected model with the best performance in the prediction of the excavation volume), is applicable. In fact, the findings of the current research can be used in order to predict the volume of earthmoving, before the start of the project and prepare a general schedule that ultimately improves the productivity of earthmoving machinery in gas pipeline projects.