Assessing Building Performance in Residential Buildings using BIM and Sensor Data

Kay Rogage, Adrian Clear, Zaid Alwan, Tom Lawrence, Graham Kelly

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
77 Downloads (Pure)


Buildings and their use is a complex process from design to occupation. Buildings produce huge volumes of data such as building information modelling (BIM), sensor (e.g. from building management systems), occupant and building maintenance data. These data can be spread across multiple disconnected systems in numerous formats, making their combined analysis difficult. The purpose of this paper is to bring these sources of data together, to provide a more complete account of a building and, consequently, a more comprehensive basis for understanding and managing its performance.

Building data from a sample of newly constructed housing units were analysed, several properties were identified for the study and sensors deployed. A sensor agnostic platform for visualising real-time building performance data was developed.

Data sources from both sensor data and qualitative questionnaire were analysed and a matrix of elements affecting building performance in areas such as energy use, comfort use, integration with technology was presented. In addition, a prototype sensor visualisation platform was designed to connect in-use performance data to BIM.

This work presents initial findings from a post occupancy evaluation utilising sensor data. The work attempts to address the issues of BIM in-use scenarios for housing sector. A prototype was developed which can be fully developed and replicated to wider housing projects. The findings can better address how indoor thermal comfort parameters can be used to improve housing stock and even address elements such as machine learning for better buildings.
Original languageEnglish
Pages (from-to)176-191
Number of pages23
JournalInternational Journal of Building Pathology and Adaptation
Issue number1
Publication statusPublished - 24 Sept 2019


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