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.

Office building occupancy monitoring through image recognition sensors

Mannino Antonino;Moretti Nicola;Dejaco Mario Claudio;Baresi Luciano;Re Cecconi Fulvio
2019-01-01

Abstract

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
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1118738
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