In this paper we define a computational method for measuring walking distances within buildings based on a length-weighted graph structure for a given building model. We name it the universal circulation network (UCN) and it has been implemented as plug-in software in Solibri Model Checker using building information modeling technologies. It provides a new explicitly defined method for representing circulation paths on top of building models supporting further circulation-related analysis as a network application. We define the computing algorithms and how we realize them. We focus not only on the implementation issues, but also on other intrinsic aspects that need to be considered while dealing with pedestrian circulation within buildings. The UCN is determined mainly by the spatial topology and geometry of a given building, and it returns consistent and accurate scalar quantities. It takes into consideration people-movement patterns, reflecting that people tend to walk along the shortest, easiest, and most visible paths. In several actual-design review projects, the UCN has proved that it is of practical benefit, not only to the distance measurement but also the visualization of pedestrian circulation, especially in reviewing building circulation.

Optimizing quality trade-offs in an end-to-end big data science process is challenging, as not only do we need to deal with different types of software components, but also the domain knowledge has to be incorporated along the process. This paper focuses on methods for tackling quality trade-offs in a common data science process for classifying Building Information Modeling (BIM) elements, an important task in the architecture, engineering, and construction industry. Due to the diversity and richness of building elements, machine learning (ML) techniques have been increasingly investigated for classification tasks. However, ML-based classification faces many issues, w.r.t. vast amount of data with heterogeneous data quality, diverse underlying computing configurations, and complex integration with industrial BIM tools, in an end-to-end BIM data analysis. In this paper, we develop an end-to-end ML classification system in which quality of analytics is considered as the first-class feature across different phases, from data collection, feature processing, training to ML model serving. We present our method for studying the quality of analytics trade-offs and carry out experiments with BIM data extracted from Solibri to demonstrate the automation of several tasks in the end-to-end ML classification. Our results have demonstrated that the quality of data, data extraction techniques, and computing configurations must be carefully designed when applying ML classifications for BIM in order to balance constraints of time, cost, and prediction accuracy. Our quality of analytics methods presents generic steps and considerations for dealing with such designs, given the time, cost, and accuracy trade-offs required in specific contexts. Thus, the methods could be applied to the design of end-to-end BIM classification systems using other ML techniques and cloud services.