Improving perceived indoor conditions using building information models and field data

Esa Halmetoja

Research output: ThesisDoctoral ThesisCollection of Articles


Facility management (FM) is known as a rapidly developing business sector. Digitalisation appeared and big data emerged during the last twenty years, but however, the essential processes of FM are still poorly digitalised. Same concerns its sub-region the facility maintenance and operations (FMO). The business models are old-fashioned and poorly support the exploitation of open data. This study presents how data sharing enables the introduction of new business models in facility maintenance and operations. Indoor conditions are one of the most critical issues in the built environment. An essential indicator of indoor conditions is perceived indoor air quality (IAQ), which problems have arisen as a national challenge in Finland during the last years. IAQ problems are difficult to control with traditional FMO processes. Also, perceived indoor conditions are challenging to verify without a dense sensor net, and preventive measures cannot be taken without field data. The aim of this study is present a new way to collect, store, analyse and utilise the field data to improve the perceived indoor conditions. Building information models (BIMs) are widely utilised in the planning and construction processes, but not much in post-construction operations. In this research, the convenient way for post-construction use of BIM is defined. Besides, the BIM-based conceptual architecture for gathering, combining, analysing, distributing, and visualising of field data has described. That solution, named as the conditions data model (CDM), improves the pace and quality of services and enables entirely new services. The CDM also renews FMO's operation models and improves IAQ's management. Besides, new kind of business emerges, and previously undefined values for owner-operators, occupants, and property service companies materialise.The relevant literature was reviewed to form the theoretical background of the study. The existence, types and sources of the field data were considered, based on the literature on human-machine interaction (HMI) and human-building interaction (HBI). Also, empirical analyses using interviews, online surveys, heuristic evaluations and studies of raw material were conducted. The grounded theory (GT) method was used to construct theory from data, using comparative analysis. Finally, the essentials were conceptualised, and conclusions were drawn using inductive inference. The most important finding is that the combination of BIM and field data, created in this study, allows a whole new way of thinking. Property maintenance is transformed from a de-tail level workflow led by the subscriber into a knowledge-based activity, where the quality of the service is the most important factor. In the new operating model, the service provider obtains all the data from the building subscriber through a common platform. Accordingly, the service provider is expected to provide high-quality service to the subscriber and occupants.
Translated title of the contributionHavaittujen sisäolosuhteiden parantaminen rakennuksen tietomallien ja kenttätietojen avulla
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
  • Salonen, Heidi, Supervising Professor
  • Ihasalo, Heikki, Thesis Advisor
  • Främling, Kary, Thesis Advisor
Print ISBNs978-952-60-3933-6
Electronic ISBNs978-952-60-3934-3
Publication statusPublished - 2020
MoE publication typeG5 Doctoral dissertation (article)


  • building information model
  • conditions data model
  • facility maintenance and operations
  • field data

Fingerprint Dive into the research topics of 'Improving perceived indoor conditions using building information models and field data'. Together they form a unique fingerprint.

Cite this