Assessing building performance in residential buildings using BIM and sensor data

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

“…Integration of various sensors types is a useful approach for covering disadvantages of some sensors by other types for example the electromagnetic NDT sensor has geolocalisation problems which covers by using terrestrial laser scanning (TLS) and Structure-from-Motion (SfM) approaches or combining laser scanning and thermographic images technique is useful for vulnerability detection [ 139 , 140 ]. Developing an agnostic platform to link real-time data and static occupant data of sensors to BIM models, solving storage challenges due to WSN nodes movement, optimizing the sensor deployment for reducing dissipation of wireless energy, combining sensor data with BIM by Domain-Specific Language (DSL) technique showed suitable results in WSN activities [ 35 , 46 , 141 , 142 ]. Despite various efforts for atomization of remote monitoring with pressure sensors, recently researchers conducted a method for automatic generation of BIM models with economic sensors to increase accuracy, time-saving, and quality of modeling than TLS [ 135 , 143 ].…”

Building Performance and Occupancy Evaluation for Public Building Stock Management: A State of the Art

“…To date, the use of sensors as a part of POEs to monitor existing buildings have been mostly focused on analyzing energy performances and consumptions and indoor environmental quality (IEQ) (Costa et al 2015, Delzendeh et al 2017, Yan et al 2017, Saralegui et al 2018, Demian et al 2018, Rogage et al 2019, Wang et al 2019a, Wang et al 2019b. Recently, applications of sensors to detect occupancy flows have been explored, mainly referring to building energy consumption analyses and predictive models (Diraco et al 2015, Yan et al 2017, Saralegui et al 2018, Rouleau et al 2019, Wang et al 2019b, as presented in Table 1.…”

“…The integration of POEs in a BIM approach enables the connection between POE data and the building model (Costa et al 2015, Machado et al 2017, Rogage et al 2019, with sensors' data and ML-based occupancy predictions assigned to each room of the model (Pin et al 2018, Rogage et al 2019. The integration in a BIM approach has the following advantages:…”

“…• 3D plan views allow the visual representation of the building space, the quick contextualization of POE results and the quick recognition of areas with higher/lower occupancy levels, for instance, using different filling colors (Pin et al 2018); • The model enables the visual identification of the spread of similar issues in an area with the identification of major problems that can cause minor related issues, as well as the recognition of seasonal patterns or recurring issues in time series data (Pin et al 2018); • Each room can be linked to alerts and warnings according to POEs (Rogage et al 2019);…”

“…The DSS would provide predictions related to occupancy values and need for cleaning activities, as well as optimized space reorganizations to support the decision-making process of facility managers. The implementation of a web-based platform can enable the visualization of the model linked to POE data and predictions with the following benefits: i) supporting the definition of actions for better space management to fit variable occupancy (Pin et al 2018); ii) enabling a user-friendly consultation of the model and related data by all those involved in the building management process (Rogage et al 2019); iii) providing online surveys on the web platform: the FM could check if the actions carried out are effective and whether or not users' satisfaction increased (Pin et al 2018) The advantages presented above are even larger considering large building stocks. In fact, while single case studies are more in-depth analyses but can barely be applied to other studies because of the peculiarity of the case study itself, large building stock analyses seems to be more reliable and more robust to variable parameters (Rouleau et al 2019).…”