A data structure for studying 3D modeling design behavior based on event logs

Abstract

In recent years, with the increase of large-scale and complex architecture projects, the architecture, engineering and construction (AEC) industries are changing rapidly towards intelligent and databased direction. As the by-product, 3D modeling event logs could provide quantitative and traceable data for early design stages that can produce butterfly-effect on the later construction stages. In this study, new data structure of command-object graph retrieved from 3D modeling event logs was proposed. It reflects the cause-and-effect relationship between commands and objects to accommodate 3D modeling process. A case study was conducted on 110 students' event logs generated from solving a well-defined façade model. As result: an average trial-and-error ratio was calculated at 0.3357 based on original and simplified graphs. Four main types of ‘modeling bubble’ were summarized base on unit-tag graphs. Key commands were found by graph centrality. Auto-prediction of same-group objects was developed with mis-group rate less than 0.9%.