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.

Keywords: Machine learning, Construction industry, Electrical engineering, Electronics engineering