The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.

Both public administrations and private owners of large building stocks need to work out plans for the management of their property, while having to deal with yearly budget limitations. Particularly for the former, this is a rather critical challenge, since public administrations are given the responsibility of sticking to very strict budget distributions over the years. As a consequence, when planning the actions to be taken on their building stocks in order to comply with their current use and the legislation in-force, they need to classify refurbishment priorities. The aim of this paper is to develop a first tool based on Bayesian Networks that offers an effective decision support service for owners even in case some information is incomplete. This tool can be used to evaluate the compliance of existing buildings with the latest standards. The decision support platform proposed includes a multi-criteria evaluation approach combining several performance indicators, each of which related to a specific regulatory area. This tool can be applied to existing buildings, where the building with the lowest score shows the highest priority of intervention. Also, the platform performs an assessment of expected costs for required refurbishment or renovation actions.

The aim of the research project presented in this paper is to experiment actions to improve the existing school building management and maintenance through a technological and process innovation based on the Building Information Modeling (BIM). In the field of refurbishment and reuse of existing buildings, some of the most sensible and specific sectors are investigated, particularly focusing on energy efficiency, structural improvement, up-to-date information on completed works and quality control. The project's goal is to take advantage of the information technologies, beginning from software interoperability, and defining a new working philosophy that should use the BIM also in the monitoring, managing and maintenance phases. This will result in an advanced drafting of the design standards for refurbishment/reuse of public buildings by a hierarchic data structure (Preliminary Requirements). The optimization of the process will lead to a complete building modeling (architecture, structure, facilities, deterioration) in order to describe both the residual building performance and the outgoings of the refurbishment design (Final BIM Requirements). The research has been validated by on-field application (school buildings)

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While on the one hand the BIM methodology is an essential reference for the construction of new buildings, on the other hand it is receiving particular attention and interest also from owners of large building stocks who want to take advantage of the benefits of Building Information Modelling so as to have a coordinated system for the sharing of information and data. This, especially in a process that concerns the management and maintenance of a large building stocks, involves the processing of uncertain information in BIM, particularly when dealing with existing buildings, due to the lack of and/or incomplete documentation, entailing a significant investment in terms of time and additional costs. Therefore, to represent the reliability of existing building data, we suggest introducing a tool based on Bayesian Network that offers a valid decision support under conditions of uncertainty and is used to evaluate the compliance with the latest standard. This paper presents a process to provide an integrated database defined by a minimum information level that can be used both to extrapolate and query specific information from a digital building model and populate the decision model in order to evaluate the performance parameters of existing buildings which is based on a Multicriteria decision making approach (AHP).