The Role of Artificial Intelligence Techniques in Enhancing Project Completion Speed: A Study on Using LSTM Networks for Predicting Delay Times
DOI:
https://doi.org/10.33095/n7rfbg91Keywords:
: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), LSTM Networks, Project Management (PM), Delay Times (DT).Abstract
This study aimed to investigate mechanisms for enhancing project completion speed through the application of artificial intelligence techniques. The study adopted the approach of "Using LSTM Networks for Predicting Project Delay Times," and the researchers utilized data from 3530 residential units for training, testing, and prediction. Selecting project delay times as a focus was driven by their significant impact on vital project completion. The research problem centers around the main question, "Can LSTM networks be successfully used to predict project delay times?" The significance of the study lies in utilizing Long Short-Term Memory (LSTM) neural network techniques to improve the prediction of project delay times, thereby enhancing project planning and management, reducing delays, and increasing efficiency in execution. Among the key findings, it was revealed that LSTM networks can effectively enhance the prediction of construction project delay times, exhibiting high accuracy and retrieval rates. Introducing this advanced technology to project management can lead to improved scheduling, planning, and reduced delays, ultimately contributing to enhanced work efficiency, productivity, and more accurate strategic decision-making.
Research Type: Research Paper.
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Conclusions :
Based on the achieved results, the researchers arrived at the following conclusions:
The potential use of artificial intelligence (neural networks) in project management aims to expedite project completion.
This study demonstrated that LSTM networks performed exceptionally well in predicting construction project delays based on planned features, achieving an accuracy of 89% and a recall of 93%. This underscores their effectiveness.
The main objectives of evaluating the LSTM model were successfully achieved.
Predicting delay times enables organizations, engineers, and contractors to leverage these techniques for improved project planning and more efficient schedule management.
Employing advanced technology like neural networks enables the early anticipation of potential issues and the implementation of strategies to mitigate their impact on schedules.
The use of artificial intelligence in project management is poised to enhance project team effectiveness.
Artificial intelligence contributes to forming insights about the environment and making informed decisions.
Applications of artificial intelligence are spreading across various fields, including project management.
Artificial intelligence in project management supports numerous initiatives and aids in managing diverse projects.
Project managers benefit from artificial intelligence by streamlining daily tasks and increasing productivity.
Authors Declaration:
Conflicts of Interest: None
-We Hereby Confirm That All The Figures and Tables In The Manuscript Are Mine and Ours. Besides, The Figures and Images, Which are Not Mine, Have Been Permitted Republication and Attached to The Manuscript.
- Ethical Clearance: The Research Was Approved By The Local Ethical Committee in The University.
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