The construction industry is facing a challenge to move towards a more sustainable sector with energy-efficient buildings and sustainable design features. Building design and construction process are conditioned by numerous sustainability regulations and assessment measures. With the increasing use of building simulations, the potential of improving design features and promoting efficient construction has become a routine practice, starting at early stages of design and carried out throughout the life cycle of a building. Nevertheless, the construction process is currently lacking the presence of integrated systems that allow dynamic compliance checking of design features with building regulations using instant results from building simulation tools. Such integrated system requires access to regulatory compliance data and appropriate information exchange mechanism between building information model, regulatory requirements and building simulations tools. This paper will present an initiative for developing an integrated system that facilitates managing building performance dynamically through an appropriate information management process combining sustainability regulatory and building simulations with building information modeling. The paper will present a valid implementation results of compliance checking against some criteria of BREEAM assessment process. The quantitative analysis of the results revealed that more than 50% of compliance requirements cannot be fully automated and still requires users input. This is due to the fact that the IFC data model used for data extraction lacks a representation of certain domains of data.
|Title of host publication||Working Conference on Virtual Enterprises|
|Publication status||Published - 25 Aug 2018|
|Name||Collaborative Networks of Cognitive Systems|
Kasim, T., Li, H., Rezgui, Y., & Beach, T. (2018). Integrated Framework to Manage Building’s Sustainability Efficiency, Design Features and Building Envelope. In Working Conference on Virtual Enterprises (pp. 650-660). (Collaborative Networks of Cognitive Systems; Vol. 534). Springer. https://doi.org/10.1007/978-3-319-99127-6_56