Ensemble-based Deep Learning Approach for Performance Improvement of BIM Element Classification

Ensemble learning is explored as an approach to improve the classification of BIM elements using multiple deep learning classifiers. Existing studies have focused on improving the performance of a single classifier, thereby restricting the types of features available for training. Two classifiers, MVCNN and MLP were trained on nine BIM element classes extracted from a 20-story office building. MVCNN used multiple images of these elements, while MLP used adjacency relations between elements. The predictions of the classifiers were then ensembled using a maximum rule-based algebraic combiner. Results showed that the overall classification accuracy improved by +0.05. The Ensemble model, in effect, leveraged the strength of each classifier: MVCNN for elements with distinct features and MLPs for elements with high similarity. The Ensemble model enables the automatic checking of the semantic accuracy of elements in BIM models, thus endorsing the seamless information exchanges required for true interoperability in BIM-centric project collaboration.