  Citation: Villa, V.; Bruno, G.;Aliev, K.; Piantanida, P.; Corneli, A.; Antonelli, D. Machine Learning Framework for the Sustainable Maintenance of Building Facilities. Sustainability 2022, 14, 681. https:// doi.org/10.3390/su14020681 Academic Editor: Jaejun Kim Received: 30 November 2021 Accepted: 5 January 2022 Published: 8 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Machine Learning Framework for the Sustainable Maintenance of Building Facilities Valentina Villa 1, * , Giulia Bruno 2 , Khurshid Aliev 1 , Paolo Piantanida 1 , Alessandra Corneli 3 and Dario Antonelli 2 1 Department of Structural, Geotechnical and Building Engineering , Politecnico di Torino, 10129 Turin, Italy; khurshid.aliev@polito.it (K.A.); paolo.piantanida@polito.it (P.P.) 2 Dipartimento di Ingegneria Gestionale e della Produzione, Politecnico di Torino, 10129 Turin, Italy; giulia.bruno@polito.it (G.B.); dario.antonelli@polito.it (D.A.) 3 Dipartimento di Ingegneria Civile, Edile e Architettura, Università Politecnica delle Marche, 60121 Ancona, Italy; a.corneli@univpm.it * Correspondence: valentina.villa@polito.it Abstract: 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. Keywords: Internet of things (IoT); sustainable buildings maintenance; management; smart buildings; artificial intelligence 1. Introduction The environmental impact of buildings is astonishing. Every year, building construc- tion consumes 25% of world wood harvest, 40% of materials entering the global economy, 3 billion tons of raw materials transformed into foundations, walls, pipes, and panels, and 50% of copper utilized in the United States [1]. Building construction accounts for half of the worldwide output of greenhouse gases and acid rain agents. Buildings, as a critical component of a habitat, have an influence on their local and surrounding areas, which can have unintended consequences for people and the community. Addressing the sustainability in building can significantly reduce these negative effects [2]. In Europe, the building maintenance industry accounts for the same amount of build- ing production as the new construction market. Due to the aging buildings, the maintenance industry grows at a rate of 1.5 percent each year [3]. The size and expansion of the existing market needs a greater focus on sustainable building maintenance [4]. Sustainable buildings may ensure that their constructions and services are suitable for living, working, and other daily activities [5]. Building maintenance is essential to ensuring Sustainability 2022, 14, 681. https://doi.org/10.3390/su14020681 https://www.mdpi.com/journal/sustainability