Optimizing Construction Productivity Through Automation and Artificial Intelligence

Authors

  • Nwosu Obinnaya Chikezie Victor1* 1Department of Electrical and Electronics Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2006, South Africa.

DOI:

https://doi.org/10.51483/IJAIML.3.2.2023.28-44

Keywords:

Artificial intelligence, Construction productivity, Automated construction, Automation technology

Abstract

This paper examines the potential of automation and Artificial Intelligence (AI)
to enhance construction productivity. Automation and AI can be used to increase
the productivity of the construction industry by reducing labor costs, boosting
productivity, and enhancing safety. Automation and AI can also be used to increase
the precision of project estimates, decrease the time required to complete projects
and decrease the amount of manual labor required. Beginning with a discussion
of the present state of construction productivity, the paper identifies the obstacles
that must be overcome to optimize it. The section then investigates the potential
of automation and AI to increase construction productivity. It examines how
automation and AI can be utilized to reduce labor costs, enhance safety, and
boost precision. The paper also examines the potential for AI to improve project
estimations, reduce project completion times, and reduce the amount of manual
labor required. Additionally, the research investigates the difficulties and dangers
associated with automation and AI in the construction industry. These include
the possibility of an increase in errors, the danger of over-automation, and the
need for personnel to be properly trained. The paper then describes the steps that
can be taken to maximize the benefits of automation and AI while minimizing
the associated risks.


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Published

2023-07-05

How to Cite

Nwosu Obinnaya Chikezie Victor1*. (2023). Optimizing Construction Productivity Through Automation and Artificial Intelligence. International Journal of Artificial Intelligence and Machine Learning, 3(02), 28–44. https://doi.org/10.51483/IJAIML.3.2.2023.28-44