Optimising scheduled maintenance on operational buildings: a microservice-based BIM framework
DOI: https://doi.org/10.3846/jcem.2025.24957Abstract
Operation and Maintenance (O&M) aims to preserve the quality of the building throughout its life, keeping maintenance costs within acceptable limits. Maintenance involves different tasks, from replacing air conditioning filters to restoring structural elements. Each task has an optimal frequency, which can be flexible within a specific time range, a cost, and a duration. These maintenance activities may disrupt building operations by repeatedly interrupting ongoing activities. This research seeks to reduce these disruptions by grouping tasks within reasonably close time frames to schedule preventive maintenance plans while respecting their frequency. We propose an optimisation model, solvable using a general-purpose solver, which identifies the best time range for grouping O&M tasks. By penalising deviations from the optimal period, the model ensures that tasks are performed at the most cost-effective time. Integrated within a microservice-based architecture, the optimisation engine seamlessly links an input database and a BIM model, orchestrated using Dynamo for Revit. A case study illustrates the effectiveness of this system, consolidating multiple tasks into optimised work clusters and significantly reducing operational disruptions. The originality of this work lies in its innovative combination of optimisation techniques and BIM tools, providing a practical and scalable solution for efficient O&M management.
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maintenance, architecture, project management, O&M, BIM, microservice, scheduling, optimisation, Integer Linear ProgrammingHow to Cite
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Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
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References
Abuhussain, M. A., Waqar, A., Khan, A. M., Othman, I., Alotaibi, B. S., Althoey, F., & Abuhussain, M. (2024). Integrating Building Information Modeling (BIM) for optimal lifecycle management of complex structures. Structures, 60, Article 105831. https://doi.org/10.1016/j.istruc.2023.105831
Ahmed, R., Nasiri, F., & Zayed, T. (2022). Two-stage predictive maintenance planning for hospital buildings: A multiple-objective optimization-based clustering approach. Journal of Performance of Constructed Facilities, 36(1), Article 04021105. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001691
Ashouri, A., Fux, S. S., Benz, M. J., & Guzzella, L. (2013). Optimal design and operation of building services using mixed-integer linear programming techniques. Energy, 59, 365–376. https://doi.org/10.1016/j.energy.2013.06.053
Dallasega, P., Revolti, A., Follini, C., Schimanski, C. P., & Matt, D. T. (2019). BIM-Based construction progress measurement of non-repetitive HVAC installation works. In Proceedings of the 27th Annual Conference of the International Group for Lean Construction (IGLC) (pp. 819–830). IGLC.net. https://doi.org/10.24928/2019/0152
Davtalab, O. (2017). Benefits of real-time data driven BIM for FM departments in operations control and maintenance. In Computing in Civil Engineering 2017 (pp. 202–210). https://doi.org/10.1061/9780784480823.025
Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2024). Benchmarking operations and supply chain management practices using Generative AI: Towards a theoretical framework. Transportation Research Part E: Logistics and Transportation Review, 189, Article 103689. https://doi.org/10.1016/j.tre.2024.103689
Gao, X., & Pishdad-Bozorgi, P. (2019). BIM-enabled facilities operation and maintenance: A review. Advanced Engineering Informatics, 39, 227–247. https://doi.org/10.1016/j.aei.2019.01.005
Gnekpe, C., Tchuente, D., Nyawa, S., & Dey, P. K. (2024). Energy performance of building refurbishments: Predictive and prescriptive AI-based machine learning approaches. Journal of Business Research, 183, Article 114821. https://doi.org/10.1016/j.jbusres.2024.114821
Golabchi, A., Akula, M., & Kamat, V. (2016). Automated building information modeling for fault detection and diagnostics in commercial HVAC systems. Facilities, 34(3/4), 233–246. https://doi.org/10.1108/F-06-2014-0050
Gurobi Optimization, LLC. (2024). Gurobi optimizer reference manual. https://www.gurobi.com
Horner, R. M. W., El‐Haram, M. A., & Munns, A. K. (1997). Building maintenance strategy: A new management approach. Journal of Quality in Maintenance Engineering, 3(4), 273–280. https://doi.org/10.1108/13552519710176881
Hosseinzadeh, A., Frank Chen, F., Shahin, M., & Bouzary, H. (2023). A predictive maintenance approach in manufacturing systems via AI-based early failure detection. Manufacturing Letters, 35, 1179–1186. https://doi.org/10.1016/j.mfglet.2023.08.125
Kelly, G., Serginson, M., Lockley, S., Dawood, N., & Kassem, M. (2013). BIM for facility management: A review and a case study investigating the value and challenges. In Proceedings of the 13th International Conference on Construction Applications of Virtual Reality (pp. 191–199), London, UK.
Khan, S., Panuwatwanich, K., & Usanavasin, S. (2023). Integrating building information modeling with augmented reality: Application and empirical assessment in building facility management. Engineering, Construction and Architectural Management, 31(7), 2809–2828. https://doi.org/10.1108/ECAM-12-2021-1146
Konig, M., Koch, C., Habenicht, I., & Spieckermann, S. (2012). Intelligent BIM-based construction scheduling using discrete event simulation. In Proceedings of the 2012 Winter Simulation Conference (WSC), Berlin, Germany. https://doi.org/10.1109/WSC.2012.6465232
Li, A., Xiao, F., Xiao, Z., Yan, R., Li, A., Lv, Y., & Su, B. (2024). Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling. Advances in Applied Energy, 16, Article 100189. https://doi.org/10.1016/j.adapen.2024.100189
Malucelli, F., & Nicoloso, S. (2007). Shiftable intervals. Annals of Operations Research, 150(1), 137–157. https://doi.org/10.1007/s10479-006-0161-1
Nadareishvili, I., Mitra, R., McLarty, M., & Amundsen, M. (2016). Microservice architecture: Aligning principles, practices, and culture. O’Reilly Media, Inc.
Ni, M., Luh, P. B., & Moser, B. (2003). An optimization-based approach for distributed project scheduling. In 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) (Vol. 2, pp. 1756–1761), Taipei, Taiwan. IEEE. https://doi.org/10.1109/ROBOT.2003.1241848
Peng, Y., Lin, J.-R., Zhang, J.-P., & Hu, Z.-Z. (2017). A hybrid data mining approach on BIM-based building operation and maintenance. Building and Environment, 126, 483–495. https://doi.org/10.1016/j.buildenv.2017.09.030
Scaife, A. D. (2024). Improve predictive maintenance through the application of artificial intelligence: A systematic review. Results in Engineering, 21, Article 101645. https://doi.org/10.1016/j.rineng.2023.101645
Schrijver, A. (1998). Theory of linear and integer programming. John Wiley & Sons.
Torres-Sainz, R., Lorente-Leyva, L. L., Arbella-Feliciano, Y., Trinchet-Varela, C. A., Pérez-Vallejo, L. M., & Pérez-Rodríguez, R. (2024). Data-enabled Bayesian inference for strategic maintenance decisions in industrial operations. Data in Brief, 57, Article 111058. https://doi.org/10.1016/j.dib.2024.111058
Wettewa, S., Hou, L., & Zhang, G. (2024). Graph Neural Networks for building and civil infrastructure operation and maintenance enhancement. Advanced Engineering Informatics, 62, Article 102868. https://doi.org/10.1016/j.aei.2024.102868
Yussuf, R. O., & Asfour, O. S. (2024). Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview. Energy and Buildings, 305, Article 113903. https://doi.org/10.1016/j.enbuild.2024.113903
Zhang, A., Yang, J., & Wang, F. (2023). Application and enabling digital twin technologies in the operation and maintenance stage of the AEC industry: A literature review. Journal of Building Engineering, 80, Article 107859. https://doi.org/10.1016/j.jobe.2023.107859
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Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
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