New Automated BIM Object Classification Method to Support BIM Interoperability

Abstract

Industry Foundation Classes (IFC) is widely accepted as the future of building information modeling (BIM) to take on the challenge of BIM interoperability and enable its support of various automation tasks. However, it is not uncommon to see misuses of IFC entities during the creation of BIM. Such misuses prevent a successful automation of BIM-supported tasks because misclassification of objects in BIM can lead to significant negative consequences in downstream applications due to incorrect semantic information provided. To address this problem, the authors propose a new data-driven, iterative method that can be used to develop an algorithm to automatically classify each object in an IFC model into predefined categories. The algorithm consists of multiple subalgorithms with each subalgorithm depicting a pattern matching rule that uses inherent features of the geometric representation of an architecture, engineering, and construction (AEC) object. The method was tested in an experiment in which IFC models from three different sources were collected and 1,891 AEC objects were extracted and divided into training and testing data for use. By comparing the classification results of the algorithm developed based on training data and applied to testing data with a manually developed gold standard, 84.45% recall and 85.20% precision were achieved in common building element categories, and 100% recall and precision were achieved in detailed beam categories. The sources of errors were found to be (1) different objects sharing the same geometric shape and (2) uncovered geometric shape representation in the training data. By adding locational information into consideration in addition to geometric information and making sure training data covers all geometric shape representations, 100% precision and recall can be achieved for all categories.