A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Digital twins for asset management: Social network analysis-based review
Abstract The civil engineering industry is notorious for being slow in the adoption of digital technology and research has shown that the industry suffers from poor asset management processes as a result. Digital Twin is one of the emerging technologies being adopted to address this challenge. This study conducted a systematic literature review of digital twin for asset management using social network analysis. Salient topics in studies were identified and centrality metrics including degree, betweenness, and eigenvector centralities were used to analyze the importance of topics. The analysis involved global analyses of the combined studies and local analysis within identified clusters. The result showed that some topics such as real-time data and decision making have received more attention from scholars. Topics that had low centrality scores were also identified as less studied in the research space. The study was also able to cross-compare the clusters with the global analyses.
Graphical abstract Display Omitted
Highlights Deeper research insights achieved from a multi-level social network analysis. Facility, infrastructure, and disaster management are digital twin focal areas. Real-time data and decision-making are focal topics across all research areas. Future digital twin research directions identified at local and global levels.
Digital twins for asset management: Social network analysis-based review
Abstract The civil engineering industry is notorious for being slow in the adoption of digital technology and research has shown that the industry suffers from poor asset management processes as a result. Digital Twin is one of the emerging technologies being adopted to address this challenge. This study conducted a systematic literature review of digital twin for asset management using social network analysis. Salient topics in studies were identified and centrality metrics including degree, betweenness, and eigenvector centralities were used to analyze the importance of topics. The analysis involved global analyses of the combined studies and local analysis within identified clusters. The result showed that some topics such as real-time data and decision making have received more attention from scholars. Topics that had low centrality scores were also identified as less studied in the research space. The study was also able to cross-compare the clusters with the global analyses.
Graphical abstract Display Omitted
Highlights Deeper research insights achieved from a multi-level social network analysis. Facility, infrastructure, and disaster management are digital twin focal areas. Real-time data and decision-making are focal topics across all research areas. Future digital twin research directions identified at local and global levels.
Digital twins for asset management: Social network analysis-based review
Arisekola, Kolade (author) / Madson, Katherine (author)
2023-03-11
Article (Journal)
Electronic Resource
English
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