Traditionally, the one-to-one interaction between heterogeneous software has become the most commonly used method for multi-disciplinary collaboration in building projects, resulting in numerous data interfaces, different data formats, and inefficient collaboration. As the prevalence of Building Information Modeling (BIM) increases in building projects, it is expected that the exchange of Industry Foundation Classes (IFC)-based data can smoothly take place between heterogeneous BIM software. However, interoperability issues frequently occur during bidirectional data exchanges using IFC. Hence, a data interoperability experiment, including architectural, structural and MEP models from a practical project, was conducted to analyze these issues in the process of data import and re-export between heterogeneous software. According to the results, the fundamental causes of interoperability issues can be concluded as follows: (a) software tools cannot well interpret several objects belonging to other disciplines due to the difference in domain knowledge; (b) software tools have diverse methods to represent the same geometry, properties and relations, leading to inconsistent model data. Furthermore, this paper presents a suggested method for improving the existing bidirectional data sharing and exchange: BIM software tools export models using IFC format, and these IFC models are imported into a common IFC-based BIM platform for data interoperability.

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Among various building information model (BIM) reconstruction methods for existing building, image-based method can identify building components from scanned as-built drawings and has won great attention due to its lower cost, less professional operators and better reconstruction performance. However, this kind of method will cost a great deal of time to design and extract features. Moreover, the manually extracted features have poor robustness and contain less non-geometric information. In order to solve this problem, this paper proposes a deep learning-based method to detect building components from scanned 2D drawings. Taking structural drawings as an example, in this article, 1500 images of structural drawings were firstly collected and preprocessed to guarantee the quality of data. After that, the neural network model—You Only Look Once (YOLO) was trained, verified and tested. In addition, a series of metrics were utilized to evaluate the performance of recognition. The results of test experiments show that the components in structural drawings (e.g., grid reference, column and beam) can be successfully detected, while the average detection accuracy of the whole image is over 80% and the average detection time for each image is 0.71 s. The experimental results demonstrate that the proposed method is robust and timesaving, which provides a good basis for the reconstruction of BIM from 2D drawings.