Behavior and Performance of BIM Users in a Collaborative Work Environment

Featured Application This research has specific applications in architecture firms developing Building Information Modeling (BIM) projects where coordinators and managers are willing to analyze behavioral patterns and efficiency of their modelers and to identify further training needs and opportunities to increase their performance. Abstract Collaborative work in Building Information Modeling (BIM) projects is frequently understood as the interaction of modelers in an asynchronous way through modification requests or via e-mail/telephone. However, alternative work methodologies based on creating a common and synchronous environment allow solving issues instantaneously during the design process. This study aimed to analyze the behavior and performance of BIM users with different specialties who were subjected to an experimental exercise in a collaborative environment. For this purpose, a process was devised to collect, sort, and select the data from the log files generated by the BIM software. A timeline of the experiment was populated with data on the intensity and types of commands used by each specialist, which allowed determining behavioral patterns, preferred commands, indicators of their experience, further training needs, and possible strategies for improving the team's performance. In the experiment, the mechanical designer's performance was 49% and the rest approximately 64%, with respect to that of the architect. An average rate of 1.66 necessary or auxiliary commands for each contributory command was detected. The average performance was 200-400 commands per hour, which intensified by the end of the experiment. Further training needs were detected for the plumbing designer to reduce the use of backwards commands. Conversely, the electrical designer showed a positive evolution regarding this aspect during the experiment. The analysis methods here described become useful for the aforementioned purposes. Nevertheless, combinations with methods from existing research might improve the outcomes and therefore the specificity of recommendations.