This paper investigates automated approaches for the recognition, classification, and parametric modeling of architectural elements within BIM environments. The research focuses on the integration of image-based and point cloud data with algorithmic and AI-driven methods to support the transition from manual Scan-to-BIM workflows to automated digital reconstruction processes.
The proposed methodology combines visual programming, scripting techniques, and machine learning algorithms to segment, classify, and reconstruct architectural components such as columns, wood trusses, and walls. The workflow includes data acquisition, algorithm definition, training and testing phases, and the generation of parametric BIM objects through API-based implementation. Case studies demonstrate how geometric rules derived from architectural treatises and historiographic knowledge can be encoded into parametric systems, enabling both reconstruction and validation of architectural elements. Results highlight the potential of automated classification and recognition techniques to improve efficiency and reproducibility in BIM modeling, while emphasizing the ongoing need for interpretation and refinement due to the complexity and variability of historical architecture.