Assessing building performance in residential buildings using BIM and sensor data

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

doi:10.1108/ijbpa-01-2019-0012

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.

Highlights

Maintenance and better general management of housing stock has been a national policy in the UK for several decades
The work attempt to address the issues of Building Information Models (BIM) in use scenarios for housing sector
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 olo ath Maintenance and better general management of housing stock has been a national policy in the UK for several decades

Summary

Introduction

Maintenance and better general management of housing stock has been a national policy in the UK for several decades. Repair and maintenance of housing association properties is a routine activity as assets age and falls to organisations commissioned and managed by local authorities. Funded organisations such as social housing landlords, are under increased pressure to reduce costs of repair and maintenance activities. The three most common causes of wear and tear in buildings occur through impact from weather, occupants, and moisture generated from wet areas within buildings such as kitchens and bathrooms (Chong & Low, 2006) Such failure in buildings during use compared to initial design benchmarks can lead to a variety of issues and problems for both occupants and gy at pt da dA an n tio na owners. By better understanding issues such as material of construction, type of occupants, and how and where energy is used, there is an opportunity to investigate methodologies for understanding building performance against design recommendations and benchmarks

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