The College of Administration and Economics at the University of Baghdad discussed , a master’s thesis in field of Industrial Management by the student (Abdulrahman Ragheb Abdulrazaq ) and tagged with ( Application of Artificial Intelligence Techniques in Project Management and Their Impact in Completion Speed), Under supervision of (Assist.Pro.Dr.. Aws Hatem Mahmoud )
This study aimed to assess the cognitive and practical knowledge of project managers in Iraqi provinces regarding the utilization of artificial intelligence in project management. It also aimed to explore the feasibility of implementing these techniques in the Iraq Gate project, along with strategies for leveraging artificial intelligence to enhance project completion speed. The research utilized a questionnaire administered to a group of project managers, and after analysis, The Iraq Gate project was selected to apply a specific checklist for evaluating aspects of artificial intelligence implementation in project operations. Gap analysis and Pareto charts were used to
identify current gaps and issues in implementation. Based on the identified gaps, “project delay times” were selected as a significant obstacle to project completion. Two artificial intelligence models were built using Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. These models were trained on a dataset of 3530 housing units to predict project delay times, and their predictive accuracy was compared. The research’s main questions revolved around whether project managers were able to employ artificial intelligence applications to expedite project completion within time, cost, and quality constraints and how artificial intelligence systems could support project managers in becoming more efficient in project-related tasks.
The significance of this research lies in utilizing artificial intelligence techniques, particularly ANN and LSTM networks, to enhance project delay prediction for accelerating project completion. Key findings include a substantial underutilization of artificial intelligence applications in project management and the potential for artificial intelligence (neural networks) to effectively improve delay prediction in construction projects. The study’s results suggest that the use of neural networks in project management can contribute to better prediction of delay times and, consequently, improved project performance. These findings can serve as a basis for developing new project management strategies in the future.
Keywords: Project Management (PM), Delay Times (DT), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Completion Speed.
