Exploring Machine Learning Techniques to Maximize Efficiency in Construction Industry Electrical and Electronics Engineering Projects

Abstract

The construction industry plays a vital role in the global economy but grapples
with inefficiencies in electrical and electronics engineering projects, resulting in
delays, increased costs, and reduced productivity. This study explores the
application of machine learning techniques to enhance efficiency in these projects.
Specifically, it focuses on developing and implementing machine learning
algorithms for optimizing project scheduling, material procurement, and
equipment utilization. Additionally, predictive analytics will be examined for
risk identification and mitigation in electrical and electronics engineering tasks
within construction. The research combines literature review and empirical
analysis to understand industry challenges and the potential benefits of using
machine learning. Empirical analysis involves creating and testing machine
learning models using real-world project data. The expected outcome is a set of
practical recommendations for project managers, engineers, and stakeholders in
construction to improve efficiency and reduce costs. Overall, this research
contributes to ongoing efforts to enhance construction industry efficiency and
productivity through the application of machine learning techniques in electrical
and electronics engineering projects.