Keystone players in collaborative building information modeling — form of contribution in Japanese large-scale projects

Abstract

The stagnation of construction productivity is becoming increasingly serious in Japan with the decreasing construction workforce. Although BIM has attracted attention to overcome this problem, its adoption has not progressed among small organizations. Expanding the BIM use should be driven by the influence of large organizations. This paper stratifies users by cross-analysis using BIM log mining, a newly emerging analytics approach based on Autodesk Revit, combined with recorded software session times of other software to improve the shortcomings of the existing method. The target company, a Japanese general contractor, where external dispatched personnel accounted for most BIM activities, needed to recognize permanent employees who undertake the crucial role in promoting cooperative BIM projects termed the keystone BIM players. The machine learning-based clustering algorithm and visual analytics discovered a group of collaborative users whose intensity of software use was weaker than proficient users but who provided a substantial proportion of the team's workforce, including multiple applications. The semi-structured interviews as a verification process further clarified that they positively perceive collaboration with external BIM operators; while delegating most tasks, they strive to improve their own BIM knowledge to respect equal collaboration. The methodology provides an indispensable dashboard to improve the project BIM communication, which is the pivotal factor in influencing the further utilization of BIM in the whole industry. The contribution of the research is threefold; the extended BIM log mining technique, the discovery of keystone BIM players, and the exclusive focus on the cooperative relationship in the BIM project environment.

3D Modeling of a Virtual Built Environment Using Digital Tools: Kilburun Fortress Case Study

“…Ishizawa [25] applied the machine learning-based clustering algorithm to the project environment. Shaharuddin [26] analyzed digital twin approaches for smart city purposes to provide numerous benefits for enhanced situation assessment, decision-making, coordination, and resource allocation.…”