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Operational text-mining methods for enhancing building maintenance management

Marco Marocco
•
Ilaria Garofolo
2021
  • journal article

Periodico
BUILDING RESEARCH AND INFORMATION
Abstract
Facility managers can significantly benefit from operational data, such as maintenance requests, stored in computerized maintenance management systems (CMMSs). This data is a valuable means to assess building performance and gain insights for preventive maintenance actions. However, databases are not always organized in such a way that allow undertaking analytics, therefore resulting in troubles when trying to generate useful information from raw data. This paper presents two methods based on a text-mining approach to extract valuable information from textual maintenance requests. The first method aims to extract the room identifier (ID) numbers where faults mainly occur, while the second one aims to identify the most problematic building elements and systems. The text-mining-based methods were tested by using a data set which contains 12,655 maintenance requests derived from a cluster of 33 buildings managed by the local administration of the Municipality of Trieste (Italy).
DOI
10.1080/09613218.2021.1953368
WOS
WOS:000679859900001
Archivio
http://hdl.handle.net/11368/2993408
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85111930870
https://www.tandfonline.com/doi/full/10.1080/09613218.2021.1953368
Diritti
closed access
FVG url
https://arts.units.it/request-item?handle=11368/2993408
Soggetti
  • Text mining

  • maintenance request

  • facilities management...

  • computerized maintena...

Scopus© citazioni
0
Data di acquisizione
Jun 14, 2022
Vedi dettagli
Web of Science© citazioni
6
Data di acquisizione
Mar 8, 2024
Visualizzazioni
2
Data di acquisizione
Apr 19, 2024
Vedi dettagli
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