Vol. 4 No. 2 (2024): Hong Kong Journal of AI and Medicine
Articles

AI-Driven Predictive Maintenance in Construction Project Management: Reducing Downtime and Ensuring Timely Delivery

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  • Emily Roberts
Emily Roberts
PhD, Assistant Professor, Department of Civil Engineering, Stanford University, Stanford, California, USA

Published 19-09-2024

Keywords

  • Artificial Intelligence,
  • predictive maintenance
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
E. Roberts, “AI-Driven Predictive Maintenance in Construction Project Management: Reducing Downtime and Ensuring Timely Delivery”, Hong Kong J. of AI and Med., vol. 4, no. 2, pp. 60–66, Sep. 2024, Accessed: Nov. 23, 2024. [Online]. Available: https://hongkongscipub.com/index.php/hkjaim/article/view/65

Abstract

The construction industry faces numerous challenges, particularly in managing equipment downtime and ensuring timely project delivery. This paper explores the application of Artificial Intelligence (AI) for predictive maintenance in construction project management, emphasizing its role in minimizing equipment failures and enhancing operational efficiency. By leveraging machine learning algorithms and data analytics, predictive maintenance enables construction managers to anticipate equipment issues before they occur, thus reducing unplanned downtime. This proactive approach not only enhances productivity but also ensures that projects are completed within budget and on schedule. The paper examines the key AI technologies employed in predictive maintenance, discusses case studies highlighting their effectiveness in construction projects, and addresses the challenges and future directions of AI adoption in this field. Ultimately, the research underscores the significance of AI-driven predictive maintenance as a transformative strategy for improving project outcomes in the construction sector.

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