Machine Learning Framework for the Sustainable Maintenance of Building Facilities

Valentina Villa, Giulia Bruno, Khurshid Aliev, Paolo Piantanida, Alessandra Corneli and Dario Antonelli
Additional contact information
Valentina Villa: Department of Structural, Geotechnical and Building Engineering , Politecnico di Torino, 10129 Turin, Italy
Giulia Bruno: Dipartimento di Ingegneria Gestionale e della Produzione, Politecnico di Torino, 10129 Turin, Italy
Khurshid Aliev: Department of Structural, Geotechnical and Building Engineering , Politecnico di Torino, 10129 Turin, Italy
Paolo Piantanida: Department of Structural, Geotechnical and Building Engineering , Politecnico di Torino, 10129 Turin, Italy
Alessandra Corneli: Dipartimento di Ingegneria Civile, Edile e Architettura, Università Politecnica delle Marche, 60121 Ancona, Italy
Dario Antonelli: Dipartimento di Ingegneria Gestionale e della Produzione, Politecnico di Torino, 10129 Turin, Italy

Sustainability, 2022, vol. 14, issue 2, 1-17

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/2/681/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/2/681/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:2:p:681-:d:720507

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().