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Birgonul, Z (2021) A receptive-responsive tool for customizing occupant's thermal comfort and maximizing energy efficiency by blending BIM data with real-time information. Smart and Sustainable Built Environment, 10(3), 504-35.

  • Type: Journal Article
  • Keywords: energy efficiency; building information modeling; internet of things; air conditioning; urban environments; ventilation; climate change; building management systems; energy consumption; meteorological data
  • ISBN/ISSN:
  • URL: http://dx.doi.org/10.1108/SASBE-11-2020-0175
  • Abstract:
    The heating, ventilation and air conditioning systems are responsible for a significant proportion of the energy consumption of the built environment, on which the occupant's pursuit of thermal comfort has a substantial impact. Regarding this concern, current software can assess and visualize the conditions. However; integration of existing technologies and real-time information could enhance the potential of the solution proposals. Therefore, the purpose of this research is to explore new possibilities of how to upgrade building information modeling (BIM) technology to be interactive; by using existing BIM data during the occupation phase. Moreover, the research discusses the potential of enhancing energy efficiency and comfort maximization together by using the existing BIM database and real-time information concomitantly. The platform is developed by designing and testing via prototyping method thanks to Internet of things technologies. The algorithm of the prototype uses real-time indoor thermal information and real-time weather information together with user's body temperature. Moreover, the platform processes the thermal values with specific material information from the existing BIM file. The final prototype is tested by a case study model The outcome of the study, “Symbiotic Data Platform” is an occupant-operated tool, that has a hardware, software and unique Revit-Dynamo definition that implies to all BIM files. The paper explains the development of “Symbiotic Data Platform”, which presents an interactive phase for BIM, as creating a possibility to use the existing BIM database and real-time values during the occupation phase, which is operated by the occupants of the building; without requiring any prior knowledge upon any of the BIM software or IoT technology.

Brandín, R and Abrishami, S (2021) Information traceability platforms for asset data lifecycle: blockchain-based technologies. Smart and Sustainable Built Environment, 10(3), 364-86.

Eiris, R, Albeaino, G, Gheisari, M, Benda, W and Faris, R (2021) InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection. Smart and Sustainable Built Environment, 10(3), 438-56.

Faris, E, Matarneh, S, Talebi, S, Kagioglou, M, Hosseini, M R and Abrishami, S (2021) Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review. Smart and Sustainable Built Environment, 10(3), 345-63.

Hosseini, M R, Jupp, J, Papadonikolaki, E, Mumford, T, Joske, W and Nikmehr, B (2021) Position paper: digital engineering and building information modelling in Australia. Smart and Sustainable Built Environment, 10(3), 331-44.

Karsten Winther, J, Nielsen, R, Schultz, C and Teizer, J (2021) Automated activity and progress analysis based on non-monotonic reasoning of construction operations. Smart and Sustainable Built Environment, 10(3), 457-86.

Lamptey, T, De-Graft, O-M, Acheampong, A, Adesi, M and Ghansah, F A (2021) A framework for the adoption of green business models in the Ghanaian construction industry. Smart and Sustainable Built Environment, 10(3), 536-53.

Mahmoudi, E, Stepien, M and König, M (2021) Optimisation of geotechnical surveys using a BIM-based geostatistical analysis. Smart and Sustainable Built Environment, 10(3), 420-37.

Oke, A E and Arowoiya, V A (2021) Evaluation of internet of things (IoT) application areas for sustainable construction. Smart and Sustainable Built Environment, 10(3), 387-402.

Xiong, R and Tang, P (2021) Machine learning using synthetic images for detecting dust emissions on construction sites. Smart and Sustainable Built Environment, 10(3), 487-503.