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Building information modeling (BIM) object classification is a key step in supporting the full automation of architecture, engineering, and construction (AEC) domain tasks such as cost estimation and building code compliance checking. A machine learning approach is designated to address any classification task without requiring the domain knowledge to be explicitly or manually specified in detail. The success of machine learning, however, relies on the quality and suitability of input features. In order to support seamless interoperability of BIM applications, the authors have proposed invariant signatures that uniquely define each AEC object and capture their intrinsic properties. In this paper, the authors combine the use of invariant signatures together with machine learning approach to address BIM object classification. The developed invariant signatures include geometric signatures, locational signatures, and metadata signatures. To test the robustness of their use as machine learning features, the authors created a new BIM object data set with 1,900 AEC objects in five major categories of building elements, including beams, columns, footings, slabs, and walls. The data were manually annotated by independent annotators to ensure the quality. Among those AEC objects in the data set, 1,330 objects (70% of the data) were used as training/development data and 570 objects (30% of the data) were used as testing data. The authors extracted the predefined invariant signatures as features and tested the robustness of them in AEC object classification using different machine learning algorithms. The best-performing algorithm achieved 99.6% F1-measure in the testing data, which outperformed the state of the art (94.9% F1-measure). As a demonstration of the value of such object classification, a comparative experiment was conducted to take off quantities of walls from a student apartment complex, both using the state-of-the-art commercial software and using the object classification–based automation. Consistent results were found between these two quantity takeoff methods, whereas using object classification–based automation further saved time and manual efforts significantly (saved 98.1% of the loading and object selecting time). These results showed that the use of proposed invariant signatures and machine learning algorithms in BIM applications is promising. [ABSTRACT FROM AUTHOR]

This study analyzes and evaluates building maintenance costs by applying the loss-distribution approach (LDA) normally used by financial organizations to assess operational risks to 7,267 historical maintenance data items for 1,915 apartment housing complexes in South Korea. These maintenance activities are classified into six categories and the period after construction into four categories in order to create a risk matrix, after which the severity and frequency distribution for each cell is calculated and the results combined to derive the total maintenance cost distribution. Maintenance activities incurred 11 to 20 years post-construction were found to have the greatest impact on cost and highest occurrence, likely because facilities such as fire safety and electrical equipment require replacement during that period and the painting is renewed. Adapting an LDA for maintenance cost evaluation, therefore, allows both the total cost distribution and the specific cost distribution incurred by particular circumstances to be analyzed, making it possible to allocate sufficient maintenance funding to properly maintain the building structure and systems and ensure adequate housing quality for occupants. [ABSTRACT FROM AUTHOR]