Abstract3D Indoor space topology, mainly referred as floor layouts and navigation routes, is the foundation of indoor Location-based Services (LBS), such as navigation in large shopping malls or interchange in large transport stations. Manually generating this topology is highly time-consuming, less cost effective, and prone to errors. This research adopts 3-D GIS technology and proposes a novel workflow to automatically generate the indoor space topology based on 3D building models in a format of Industry Foundation Classes (IFC). Floor layouts and paths including stairs will be identified and generated in the environment of ESRI ArcScene . Several categories of navigation routes are defined and constructed by a collective algorithm called i-GIT. This paper demonstrates the entire workflow and concepts to produce floor layouts and navigation routes using a 3-story commercial building model. More details related to i-GIT as well as their validation based on real buildings will be revealed in an upcoming article.
Construction projects are usually designed by different professional teams, where design clashes may inevitably occur. With the clash detection tools provided by Building Information Modeling (BIM) software, these clashes can be discovered at an early stage. However, the number of clashes detected by BIM software is often huge. The literature states that the majority of those clashes are found to be irrelevant, i.e., harmless to the building and its construction. How to filter out these irrelevant clashes from the detection report is one of the issues to be resolved urgently in the construction industry. This study develops a method that automatically screens for irrelevant clashes by combining the two techniques of rule-based reasoning and supervised machine learning. First, we acquire experts’ knowledge through interviews to compile rules for the preliminary classification of clash types. Subsequently, the results of the initial classification inferred by the rules are added into the training dataset to improve the predictive performance of the classifiers implemented by supervised machine learning. The average predictive performance obtained by using the hybrid method is up to 0.96, which has been improved from the traditional machine learning process only using individual or ensemble learning classifiers by 6%–17%.
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