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Building Information Modeling (BIM) and its implementation in Architecture, Engineering and Construction (AEC) industry is rapidly growing all over the US. The growing utilization of BIM application has resulted in accumulation of tremendous volumes of computer-generated design data. Such vast datasets provide practitioners with an opportunity to extract valuable information regarding the process of design. Design log files, in particular, are rich data sources that contain model development data automatically recorded throughout the design process. However, the existing studies are mostly restricted to mining structured data and hence, lack the capabilities to handle unstructured temporal data sources such as design log files. The main objective of this paper extract implicit process information from design log data by implementing a tailored sequential pattern mining approach. For the purpose of this research, we examined the feasibility of utilizing Revit journal log files as a non-intrusive data collection mechanism to capture modelers’ interactions with the software and detect common command execution structures during model development sessions. To this end, user-model interaction data such as modeler characteristics, command type, and command time were extracted from the data sample’s journal files using a text file parser. After careful data cleaning, the final set of temporal data were transformed into sets of multiple character strings. We used an efficient implementation of Generalized Suffix Trees (GST) data structure to identify common command execution sequences among several modelers. The results of the study identified several shared command sequences among five modeler. This study proposes a novel pattern mining methodology to extract useful information from time-stamped design log data and enables project managers to obtain valuable insight into design development processes.

A Sequential Pattern Mining Approach to Extract Information from BIM Design Log Files