Estimating Construction Project Duration using Machine Learning Algorithm

Project Details

• Student(s): Joshua Nasr
• Advisor(s): Dr. Caesar Abi Shdid
• Department: Civil
• Academic Year(s): 2023-2024

Abstract

Construction project delays remain one of the most relevant problems in the construction sector. Yet the construction industry remains one of the least digitalized industries. This research aims to use the power of artificial intelligence and machine learning to deepen our understanding of the delays encountered in building construction projects and forecast them before project commencement. A thorough review was conducted to find the main causes of construction delays. A model was subsequently developed utilizing the machine learning algorithm extreme gradient boosting (XGBoost) based on factors that quantify the primary causes of delay ascertainable prior to project initiation. The model was trained and tested using a dataset created from a survey that was filled by several construction companies for their building construction projects. The model estimated project delays with an error of 24% and an adjusted R² of 74.3%. This shows that the model was able to achieve relatively accurate results and explain 74.3% of the variability of the delay while utilizing only 10 factors causing delay. The results show that the factors mostly affecting delay in Lebanese construction projects are the client’s performance, legal issues faced by the project, the project manager’s expertise, and the quality of design documents. No prior research has been done utilizing machine learning to create regression models that can predict project delay before project commencement. This research thus provides a major improvement on existing predictive models into construction project delays that only classify projects by their delay risk level.