AI-Enabled Predictive Maintenance and Inventory Management for Smart Buildings: A GIS–BIM Data Fusion Approach to Supply Chain Optimization

Siyang Huang

doi:10.54254/2755-2721/2025.25571

Applied and Computational EngineeringOpen access

AI-Enabled Predictive Maintenance and Inventory Management for Smart Buildings: A GIS–BIM Data Fusion Approach to Supply Chain Optimization

Research Article

Open Access

AI-Enabled Predictive Maintenance and Inventory Management for Smart Buildings: A GIS–BIM Data Fusion Approach to Supply Chain Optimization

Siyang Huang ^1*

¹ China Construction Fourth Engineering Division Corp.Ltd, Guangdong, China

^*Corresponding author: rara481846778@gmail.com

Published on 30 July 2025

ACE Vol.176

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-239-3

ISBN (Online): 978-1-80590-240-9

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Abstract

Smart buildings generate massive spatial and operational data, but existing facility management systems are often not fully utilized due to the separation of geographic information system (GIS) and building information modeling (BIM) platforms. This paper proposes a data integration framework integrating GIS and BIM, providing support for AI-driven predictive maintenance and dynamic inventory management of smart buildings. By aligning the Internet of Things sensor data with the fine-grained BIM asset model and GIS spatial background, this system constructs rich feature vectors and introduces the gradient hoist stacked model and long short-term memory network to achieve accurate fault time prediction. Meanwhile, the prediction results stimulate the real-time dynamic replenishment strategy and adjust the spare parts inventory threshold. For large commercial facilities, this method reduced the mean absolute error of defect prediction at 12 hours (a 30% increase over the baseline model), reduced the inventory shortage rate by 40%, and reduced the number of inventory days on hand by 25%. Overall, this integration solution accelerated maintenance response by 20% and saved an average of US$50,000 in annual holding costs. These achievements demonstrate the value of GIS-BIM integration in promoting the shift from passive operations and maintenance to active and efficient supply chain strategies. Subsequent work will focus on real-time flow processing and the multi-facility collaboration mechanism.

Keywords:

Predictive maintenance, adaptive inventory, smart buildings, GIS–BIM data fusion, IoT

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References

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[2]. Abdel Sater, R., & Ben Hamza, A. (2020). A federated learning approach to anomaly detection in smart buildings. arXiv preprint arXiv: 2010.10293.

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References

[1]. Zheng, H., Paiva, A. R., & Gurciullo, C. S. (2020). Advancing from predictive maintenance to intelligent maintenance with AI and IIoT. arXiv preprint arXiv: 2009.00351.

[2]. Abdel Sater, R., & Ben Hamza, A. (2020). A federated learning approach to anomaly detection in smart buildings. arXiv preprint arXiv: 2010.10293.

[6]. Li, C., & Wang, J. (2024). IoT—A promising solution to energy management in smart buildings. Buildings, 14(11), 3446. https: //doi.org/10.3390/buildings14113446

[10]. Smith, J., & Patel, R. (2024). Internet of things (IoT) for safety and efficiency in construction projects. Scientific Reports, 14, Article 78931. https: //doi.org/10.1038/s41598-024-78931-0

Cite this article

Huang,S. (2025). AI-Enabled Predictive Maintenance and Inventory Management for Smart Buildings: A GIS–BIM Data Fusion Approach to Supply Chain Optimization. Applied and Computational Engineering,176,30-36.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Machine Learning and Automation

ISBN: 978-1-80590-239-3(Print) / 978-1-80590-240-9(Online)

Editor: Hisham AbouGrad

Conference website: 978-1-80590-240-9

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.176

ISSN: 2755-2721(Print) / 2755-273X(Online)

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