Details of Research Outputs

Automatic interpretation of computer aided design drawings into a Building Information Model (BIM) would reduce the time and labor costs involved in the modeling process for construction parties. However, the offset and height of building objects cannot be automatically extracted by current algorithms and products since they mostly focus on floor plan detection whilst such information conventionally appears on elevation drawings. Manual inspection and input are needed, which is rigid, error prone, and costly. The challenge of elevation recognition is attributed to the irregular and intricate shapes of the objects portrayed in elevation views, which make it difficult to fully cluster the primitives composing a building object. Additionally, none of the existing methods includes floor plan detection and elevation detection to enable a comprehensive reconstruction of a 3D BIM model. In this paper, these issues are tackled by resorting to an automatically layer classification method (ALCM) that identifies the content of hidden layers. An ALCM-based elevation recognition method is developed. It recognizes the orientation of elevation views and levels of each floor. Furthermore, it segments openings (windows and doors) in elevation views and outputs their offset and height dimensions. A façade BIM model is generated with all openings placed at the correct offsets. The experiments take 94 different sample drawings to validate the model's performance. The test results demonstrate that nearly all floor levels are detected. And that 88% of the members that are visible in elevation drawings are measured perfectly. A real-world campus building is automatically modelled as a case study. The results imply that ALCM-EDM (Elevation Detection Method) contributes to the automatic conversion process since manual input of elevation data is avoided. Future directions could address on incorporating section views and detailed drawings into the reconstruction.