Classification of architectural and MEP BIM objects for building performance evaluation

Abstract

Numerous factors in current workflows reduce the completeness, accuracy, and reliability of the information exchanged between Building Information Modelling (BIM) authoring tools and building performance simulation and analysis software. Automated classification of BIM objects could ameliorate most of the interoperability problems. However, architectural, structural, mechanical, electrical, and plumbing (MEP), and other building objects have a wide range of shapes, features, and properties, which makes identifying them reliably and accurately with any single AI tool difficult. This study explored methods and techniques for automated classification of architectural and MEP BIM objects that can significantly reduce or eliminate the manual effort required to correct data exchange and interoperability issues in pre-processing building models for performance evaluation. A series of ensemble models using a stacking mechanism of four classification tools (semantic keyword search, rule-based inferencing, machine learning using object geometry features, and deep learning using visual shape features) were implemented and tested. A BIM object dataset with 3,410 instances belonging to 40 classes was compiled for training and testing the models. The dataset contains diverse architecture and MEP objects with high-quality selected instances and is available for public use. We found that an ensemble learning approach that exploits the various types of information available in instance models may exhibit superior object classification performance than any individual tool. The ensemble model applying all four tools achieved 91.0 % accuracy (F1 score 0.88), suggesting that automated classification using ensemble learning is an effective strategy.