A framework for assessing the confidence level of earned value Estimation through machine learning

Earned value management is an important tool for predicting and assessing the time and progress of projects in the construction industry. The aim of this research is to propose a model that can reduce project scheduling problems by using the machine learning time series method, which has a very high accuracy. Accordingly, this study proposes new applications of recurrent neural network architecture based on long-term-short-term memory neural network forecasting models using the Python programming language. In this research, the difference between the project completion time in the real world and the value predicted by the machine will be determined. It should be noted that in the field of machine learning, the number of data is of particular importance. For this purpose, 03 ،EPC projects are reviewed along with the daily reports available in the project management database. Using the AHP software and multi-criteria decision-making method, the 03 reviewed projects are ranked based on three criteria: time, cost, and workload (project scope).