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.

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)

By now, it is clear the built environment could play an important role in fighting climate change, since it accounts for around 39% of global energy-related carbon emissions. Generally speaking, Italian residential stock is over 50 years old and around 16% of that needs large interventions due to its poor maintenance condition. So, the maintenance in this context can play a pivotal role in acheiving both energy efficiency and asset valorization. Introduced by a reference framework for the question in the title, this paper presents the case study: a portion of a working-class neighborhoods near the metropolitan city of Turin, marked by very recurrent typologies for the period (early seventies). The local real estate market is discussed to investigate the extraordinary maintenance impact on the property values: the paper considers the market value increase due to the energy class upgrade and the external look improvement. Individual owners putting money on this group of works get a very cost-effective investment and take advantage of Italian legislation supporting these kinds of interventions: the whole is greater than the sum of its parts and in turn greater than the cost assumed for the renovation work.

The importance of sustainable building maintenance is growing as part of the Sustainable Building concept. The integration and implementation of new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generate a large amount of data that will be utilized to better manage the sustainable building maintenance and staff. Anomaly prediction models assist facility managers in informing operators to perform scheduled maintenance and visualizing predicted facility anomalies on building information models (BIM). This study proposes a Machine Learning (ML) anomaly prediction model for sustainable building facility maintenance using an IoT sensor network and a BIM model. The suggested framework shows the data management technique of the anomaly prediction model in the 3D building model. The case study demonstrated the framework’s competence to predict anomalies in the heating ventilation air conditioning (HVAC) system. Furthermore, data collected from various simulated conditions of the building facilities was utilized to monitor and forecast anomalies in the 3D model of the fan coil. The faults were then predicted using a classification model, and the results of the models are introduced. Finally, the IoT data from the building facility and the predicted values of the ML models are visualized in the building facility’s BIM model and the real-time monitoring dashboard, respectively.

No abstract