Semantic Modeling of Building Construction Emission Knowledge

EasyChair Preprint no. 1353

Abstract

Previous works on estimating construction carbon emissions from energy consumption were primarily capitalizing on the quantity of construction materials, equipment, and an emission inventory. It is envisaged that with the aid of Building Information Modeling (BIM) technologies that are featured by semantic-rich data and information, the present-day practice of energy and emission estimation can be well improved. Despite an ideal BIM model typically encompasses information that ranges across building design, construction and operation
details, a rationale around how to leverage semantic-rich BIM to address building energy consumption and carbon emission topics is still unclear. Under this backdrop, this study is centered on formulating a semantic-rich building energy consumption ontological model that is capable of accurately calibrating the energy consumption and emissions of a building. The formulated model will consider various factors that can affect the calibration and estimation such as materials, fabrication, logistics, processing, and the like.

Keyphrases: Construction Phase, emission estimation, Ontology

@Booklet{EasyChair:1353,
  author = {Wenkai Luo and Guomin Zhang and Lei Hou and Malindu Sandanayake},
  title = {Semantic Modeling of Building Construction Emission Knowledge},
  howpublished = {EasyChair Preprint no. 1353},

  year = {EasyChair, 2019}}