Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/189382
Title: Build2Vec: Building Representation in Vector Space
Authors: Abdelrahman, Mahmoud 
Chong, Adrian 
Miller, Clayton 
Keywords: Graph embeddings
node2vec
STAR
Feature learning
Representation learning
Issue Date: 25-May-2020
Citation: Abdelrahman, Mahmoud, Chong, Adrian, Miller, Clayton (2020-05-25). Build2Vec: Building Representation in Vector Space. Symposium on Simulation for Architecture and Urban Design (SimAUD) 2020. ScholarBank@NUS Repository.
Abstract: In this paper, we represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs obtained from a Building Information Model (BIM). Industrial Foundation Classes (IFC) is a standard schema for BIM, which is utilized to convert the building data into a graph representation. We used node2Vec with biased random walks to extract semantic similarities between different building components and represent them in a multi-dimensional vector space. A case study implementation is conducted on a net-zero-energy building located at the National University of Singapore (SDE4). This approach shows promising machine learning applications in capturing the semantic relations and similarities of different building objects, more specifically, spatial and spatio-temporal data.
Source Title: Symposium on Simulation for Architecture and Urban Design (SimAUD) 2020
URI: https://scholarbank.nus.edu.sg/handle/10635/189382
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