Semi-automated visualization method for visual inspection of buildings on BIM using 3D point cloud

Regular diagnosis of a building can identify structural safety and latent hazards and establish efficient maintenance strategies. Traditional diagnoses are based on personal perspectives or knowledge of experts; therefore, they are laborious, time-consuming, and subjective. Moreover, the results may be unreliable. In addition, an undefined data format makes its practical use difficult. This study proposes a visualized semi-automated approach to extract building condition data and integrate them with BIM using Dynamo. The proposed framework uses Python code to improve efficiency when extracting coordinates. First, 3D point cloud data on the external walls of buildings obtained using a laser scanner are processed, and the relevant information, such as coordinates, is extracted. Second, the BIM model is implemented based on a point cloud using supplementary modeling tools. Finally, the extracted defects are modeled as new objects, and each BIM object is colored according to the modified criteria. To validate the feasibility of the proposed method, it was applied to actual buildings, improving the efficiency and accuracy of building inspections. This is expected to facilitate a reduction in building maintenance work and the identification of deterioration patterns.