Clustering of designers based on building information modeling event logs
A network-enabled event log mining approach is proposed for a deep understanding of the Building Information Modeling (BIM)-based collaborative design work. It proposes a novel algorithm termed node2vec-GMM combining a graph embedding algorithm named node2vec and a clustering method named Gaussian m...
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sg-ntu-dr.10356-1609062022-08-05T07:47:22Z Clustering of designers based on building information modeling event logs Pan, Yue Zhang, Limao Skibniewski, Miroslaw J. School of Civil and Environmental Engineering Engineering::Civil engineering Social Network Analysis Community Detection A network-enabled event log mining approach is proposed for a deep understanding of the Building Information Modeling (BIM)-based collaborative design work. It proposes a novel algorithm termed node2vec-GMM combining a graph embedding algorithm named node2vec and a clustering method named Gaussian mixture model (GMM) to cluster designers within a network into several subgroups, and then makes cluster analysis. Its superiority lies in the efficient feature learning ability to preserve network structure and the powerful clustering ability to tackle uncertainty and visualize results, which can directly return the cluster embedding. As a case study, a directional network with 68 nodes (designers) and 436 ties (design task transmissions) is constructed based on retrieved data from 4GB real BIM event logs. The node2vec learns and projects the network feature representation into a 128-dimensional vector, which is learned by GMM to discover three possible clusters owning 15, 26, and 27 closely linked designers. Analysis of each cluster is performed from node importance measurement and link prediction to identify information spreading and designers’ roles within clusters. Our new algorithm node2vec-GMM is proven to better improve clustering quality than other state-of-the-art methods by at least 6.0% Adjusted Rand Index and 13.4% Adjusted Mutual Information. Overall, the designer clustering process provides managers with data-driven support in both monitoring the whole course of the BIM-based design and making reliable decisions to increase collaboration opportunities. Ministry of Education (MOE) Nanyang Technological University The Start-Up Grant at Nanyang Technological University, Singapore (No. M4082160.030) and the Ministry of Education Tier 1 Grant, Singapore (No. M4011971.030) are acknowledged for support of this research. 2022-08-05T07:47:22Z 2022-08-05T07:47:22Z 2020 Journal Article Pan, Y., Zhang, L. & Skibniewski, M. J. (2020). Clustering of designers based on building information modeling event logs. Computer-Aided Civil and Infrastructure Engineering, 35(7), 701-718. https://dx.doi.org/10.1111/mice.12551 1093-9687 https://hdl.handle.net/10356/160906 10.1111/mice.12551 2-s2.0-85083789150 7 35 701 718 en M4082160.030 M4011971.030 Computer-Aided Civil and Infrastructure Engineering © 2020 Computer-Aided Civil and Infrastructure Engineering. All rights reserved. |
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Engineering::Civil engineering Social Network Analysis Community Detection Pan, Yue Zhang, Limao Skibniewski, Miroslaw J. Clustering of designers based on building information modeling event logs |
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A network-enabled event log mining approach is proposed for a deep understanding of the Building Information Modeling (BIM)-based collaborative design work. It proposes a novel algorithm termed node2vec-GMM combining a graph embedding algorithm named node2vec and a clustering method named Gaussian mixture model (GMM) to cluster designers within a network into several subgroups, and then makes cluster analysis. Its superiority lies in the efficient feature learning ability to preserve network structure and the powerful clustering ability to tackle uncertainty and visualize results, which can directly return the cluster embedding. As a case study, a directional network with 68 nodes (designers) and 436 ties (design task transmissions) is constructed based on retrieved data from 4GB real BIM event logs. The node2vec learns and projects the network feature representation into a 128-dimensional vector, which is learned by GMM to discover three possible clusters owning 15, 26, and 27 closely linked designers. Analysis of each cluster is performed from node importance measurement and link prediction to identify information spreading and designers’ roles within clusters. Our new algorithm node2vec-GMM is proven to better improve clustering quality than other state-of-the-art methods by at least 6.0% Adjusted Rand Index and 13.4% Adjusted Mutual Information. Overall, the designer clustering process provides managers with data-driven support in both monitoring the whole course of the BIM-based design and making reliable decisions to increase collaboration opportunities. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Pan, Yue Zhang, Limao Skibniewski, Miroslaw J. |
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Article |
author |
Pan, Yue Zhang, Limao Skibniewski, Miroslaw J. |
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Pan, Yue |
title |
Clustering of designers based on building information modeling event logs |
title_short |
Clustering of designers based on building information modeling event logs |
title_full |
Clustering of designers based on building information modeling event logs |
title_fullStr |
Clustering of designers based on building information modeling event logs |
title_full_unstemmed |
Clustering of designers based on building information modeling event logs |
title_sort |
clustering of designers based on building information modeling event logs |
publishDate |
2022 |
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https://hdl.handle.net/10356/160906 |
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1743119607481237504 |