Exploring Collaborative Networks in BIM Design Based on Event Logs

摘要

Social network mining is a typical application of the network intelligence that can facilitate investigating social context of work processes and provide insight into increasing the efficiency and effectively of processes and organizations. This research develops a systematic methodology to deeply mine tremendous volumes of design logs, which are generated by using Autodesk Revit in building information model (BIM) design process, in order to discover social networks in BIM-based collaborative design practices. A large dataset of design logs that involves 51 designers working on 82 projects with 620,492 lines of valid commands, provided by a major international design firm, is used as a case study. From a macro-level SNA, there are 514 links among 51 nodes in the discovered social network, the density, mean distance, and average degree of the network is 0.202, 1.984, and 10.078, respectively. This indicates the collaboration is finely intensive and one designer can connect with another in a few steps in the BIM-based collaborative network. From a micro-level SNA, degree, closeness, and betweenness centralities of each node in the discovered social network are calculated out to measure the embeddedness of each node and his position in the whole network, and it is found that designers located in the center of the interaction map (with highest degree centralities), such as "#2" and "#24," are basically those who provide shortest communication channels (with highest betweenness centralities) and are most reachable for others. This research provides insight into (1) capturing collaborations among actors from objective event logs to discover social networks, and (2) a better understanding of network characteristics of designers for enhancing the likelihood of project success within organizations.