A machine learning approach for predicting delays in construction logistics

Abstract

Construction project management is vital for accomplishing pre-determined objectives. Despite using construction management, most of the projects do not meet original time schedule or has been delayed. Delay is one of the biggest problems faced by construction industry. This project is a study the critical delay factors for project management in construction focusing contractors in Qatar and to build a prediction model to avoid the same in future projects. The objectives of this research project are to investigate delay factors to help contractors to reach their goals on time during construction. This research will review the delay factors through literature review survey questionnaire targeting professionals at a construction company who are involved in many construction projects in Qatar. The correlation between them is examined to produce the best ways in preventing delays.

This study was carried out based on comprehensive literature review, which was done to provide the background, history and delay factors of delays in construction. The information of literature review was then used to design and conduct a survey questionnaire to investigate delay factors in construction projects in Qatar and was distributed to the targeted respondents at the contractors company. Later the top delay factors achieved from the questionnaire were combined with secondary data collected from an ongoing mega project for the same company to build a prediction model using WEKA software.

Keywords:

• delay factors
• construction industry
• project management
• delay prediction
• data mining
• machine learning
• Qatar