Assessing Building Performance in Residential Buildings using BIM and Sensor Data

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Assessing Building Performance in Residential Buildings using BIM and Sensor Data. / Rogage, Kay; Clear, Adrian; Alwan, Zaid; Lawrence, Tom; Kelly, Graham.

In: International Journal of Building Pathology and Adaptation, Vol. 38, No. 1, 24.09.2019, p. 176-191.

Research output: Contribution to journalArticlepeer-review

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@article{722407df4d3242d1873931f2ac0c9026,
title = "Assessing Building Performance in Residential Buildings using BIM and Sensor Data",
abstract = "PurposeBuildings 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.Design/methodology/approachBuilding 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.FindingsData 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.Originality/valueThis 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.",
keywords = "Smart Buildings, Sensor Data, Building Performance, BIM for Facilities Management",
author = "Kay Rogage and Adrian Clear and Zaid Alwan and Tom Lawrence and Graham Kelly",
year = "2019",
month = sep,
day = "24",
doi = "10.1108/IJBPA-01-2019-0012",
language = "English",
volume = "38",
pages = "176--191",
journal = "International Journal of Building Pathology and Adaptation",
issn = "0263-080X",
publisher = "Emerald",
number = "1",

}

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TY - JOUR

T1 - Assessing Building Performance in Residential Buildings using BIM and Sensor Data

AU - Rogage, Kay

AU - Clear, Adrian

AU - Alwan, Zaid

AU - Lawrence, Tom

AU - Kelly, Graham

PY - 2019/9/24

Y1 - 2019/9/24

N2 - PurposeBuildings 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.Design/methodology/approachBuilding 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.FindingsData 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.Originality/valueThis 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.

AB - PurposeBuildings 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.Design/methodology/approachBuilding 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.FindingsData 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.Originality/valueThis 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.

KW - Smart Buildings

KW - Sensor Data

KW - Building Performance

KW - BIM for Facilities Management

UR - http://www.scopus.com/inward/record.url?scp=85074055473&partnerID=8YFLogxK

U2 - 10.1108/IJBPA-01-2019-0012

DO - 10.1108/IJBPA-01-2019-0012

M3 - Article

VL - 38

SP - 176

EP - 191

JO - International Journal of Building Pathology and Adaptation

JF - International Journal of Building Pathology and Adaptation

SN - 0263-080X

IS - 1

ER -