In the Architecture, Engineering, Construction and Operations (AECO) there is a growing interest in the use of the Building Information Modelling (BIM). Through integration of information and processes in a digital model, BIM can optimise resources along the lifecycle of a physical asset. Despite the potential savings are much higher in the operational phase, BIM is nowadays mostly used in design and construction stages and there are still many barriers hindering its implementation in Facility Management (FM). Its scarce integration with live data, i.e. data that changes at high frequency, can be considered one of its major limitations in FM. The aim of this research is to overcome this limit and prove that buildings or infrastructures operations can benefit from a digital model updated with live data. The scope of the research concerns the optimisation of FM operations. The optimisation of operations can be further enhanced by the use of maintenance smart contracts allowing a better integration between users’ behaviour and maintenance implementation. In this case study research, the Image Recognition (ImR), a type of Artificial Intelligence (AI), has been used to detect users’ movements in an office building, providing real time occupancy data. This data has been stored in a BIM model, employed as single reliable source of information for FM. This integration can enhance maintenance management contracts if the BIM model is coupled with a smart contract. Far from being a comprehensive case study, this research demonstrates how the transition from BIM to the Asset Information Model (AIM) and, finally, to the Digital Twin (i.e. a near-real-time digital clone of a physical asset, of its conditions and processes) is desirable because of the outstanding benefits that have already been measured in other industrial sectors by applying the principles of Industry 4.0.
Building Information Modeling, Facility Management, Image recognition, smart contracts.
 Manyika, J. et al., “Digital America: A tale of the haves and have-mores,” 2015. [Online]. available: https://www.mckinsey.com/industries/high-tech/our-insights/digital- america-a-tale-of-the-haves-and-have-mores
 ISO 19650‑2:2018, Organization and Digitization of Information About Buildings and Civil Engineering Works, Including Building Information Modelling (BIM)—Information Management Using Building Information Modelling, 2018.
 Chung, S.W., Kwon, S.W., Moon, D.Y. & Ko, T.K., Smart facility management systems utilizing open BIM and augmented/virtual reality. ISARC 2018—35th Int. Symp. Autom. Robot. Constr. Int. AEC/FM Hackathon Futur. Build. Things, 2018.
 2010/31/EU, Directive 2010/31/EU of the european parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings. pp. 13–35, 2010.
 Akanmu, A., Anumba, C. & Messner, J., Critical review of approaches to integrating virtual models and the physical construction. International Journal of Construction Management, 14(4), pp. 267–282, 2014. https://doi.org/10.1080/15623599.2014.972021
 U.S. DoE, Buildings energy databook, 2011.
 Martinaitis, V., Zavadskas, E.K., Motuziene, V. & Vilutiene, T., Importance of occupancy information when simulating energy demand of energy efficient house : A case study. Energy and Buildings, 101, pp. 64–75, 2015. https://doi.org/10.1016/j.enbuild.2015.04.031
 Azar, E. & Menassa, C.C., Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings. Energy and Buildings, 97, pp. 205–218, 2015. https://doi.org/10.1016/j.enbuild.2015.03.059
 Kleiminger, W., Mattern, F. & Santini, S., Predicting household occupancy for smart heating control : A comparative performance analysis of state-of-the-art approaches. Energy and Buildings, 85, pp. 493–505, 2014. https://doi.org/10.1016/j.enbuild.2014.09.046
 Christensen, K., Melfi, R., Nordman, B., Rosenblum, B. & Viera, R., Using existing network infrastructure to estimate building occupancy and control plugged-in devices in user workspaces Bruce Nordman and Ben Rosenblum Raul Viera. International Journal of Communication Networks and Distributed Systems, 12(1), 2014. https://doi.org/10.1504/ijcnds.2014.057985
 Harle, R.K. & Hopper, A., The potential for location-aware power management. in Proceedings of the 10th International Conference on Ubiquitous Computing, 2008, pp. 302–311.
 Erickson, V.L. & Cerpa, A.E., Occupancy based demand response HVAC control strategy. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, 2010, pp. 7–12.
 Dodier, R.H., Henze, G.P., Tiller, D.K. & Guo, X., Building occupancy detection through sensor belief networks. Energy and Buildings, 38(9), pp. 1033–1043, 2006. https://doi.org/10.1016/j.enbuild.2005.12.001
 Von Neida, B., Maniccia, D. & Tweed, A., An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems an analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems. Journal of the Illuminating Engineering Society, 30(2), pp. 111–125, 2001. https://doi.org/10.1080/00994480.2001.10748357
 Labeodan, T., Zeiler, W., Boxem, G. & Zhao, Y., Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation. Energy and Buildings, 93, pp. 303–314, 2015. https://doi.org/10.1016/j.enbuild.2015.02.028
 Volk, R., Stengel, J. & Schultmann, F., Building information modeling (BIM) for Existing buildings - Literature review and future needs. Automation in Construction, 43, p. 204, 2014.
 Centre for Digital Built Britain, The Gemini Principles, 2018.
 Li, N., Calis, G. & Becerik-gerber, B., Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations. Automation in Construction, 24, pp. 89–99, 2012. https://doi.org/10.1016/j.autcon.2012.02.013
 Kuutti, J., A Test Setup for Comparison of People Flow Sensors, pp. 1–89, 2012.
 Chen, J. Chen, H. & Luo, X., Collecting building occupancy data of high resolution based on WiFi and BLE network. Automation in Construction, 102, pp. 183–194, 2019. https://doi.org/10.1016/j.autcon.2019.02.016
 Zhao, Y., Zeiler, W., Boxem, G. & Labeodan, T., Virtual occupancy sensors for realtime occupancy information in buildings. Building and Environment, 93, pp. 9–20, 2015. https://doi.org/10.1016/j.buildenv.2015.06.019
 Benezeth, Y., Laurent, H., Emile, B. & Rosenberger, C., Towards a sensor for detecting human presence and characterizing activity. Energy and Buildings, 43(2–3), pp. 305–314, 2011. https://doi.org/10.1016/j.enbuild.2010.09.014
 Sarkar, A., Fairchild, M. & Salvaggio, C., Integrated daylight harvesting and occupancy detection using digital imaging. Sensors, Cameras, and Systems for Industrial/Scientific Applications IX, 6816, p. 68160F, 2008.
 Moretti, N. & Re Cecconi, F., Blockchain application to maintenance smart contracts. In Research in Building Engineering - EXCO’18, 2018.