# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 5 Discussion
The level of development of the models built with the experiments varied between LOD 200 and LOD 300, according to the definitions of the American Institute of Architects [63], as they correctly represent the three-dimensional geometry of the element, its thicknesses, and dimensions, but without the characteristics of superior LOD data such as the 3D representation of internal elements, join, or connections details. The topography model represents its 3D surface and does not contain site equipment, external structures, and existing foundation elements, classified as LOD 100. Regarding the level of accuracy of the model, all elements fit within the LOA range recommended by the U.S. guide Institute of Building Documentation (USIBD) [28]. The elements ranged between LOA30 and LOA40, where LOA30 represents an accuracy between 15 mm and 5 mm and LOA40 between 5 mm and 1 mm. In all cases, the elements were created in their correct BIM categories and with the possibility of assigning parameters and attributes in the same way as any native element of Revit. It is possible to incorporate the model into a BIM workflow without any interference or incompatibilities, combine its elements with other existing elements, and increase its LOD as the project develops. The automatic approach was 25% faster than the manual approach, and this gain can have a big impact on larger or more complex projects (Table 5). In addition to the comparative analysis between the two models, a quantitative assessment of the algorithms' efficiency in BIM modeling of building elements was also performed (Table 6). The automatic approach demonstrated strong performance in the number of elements it could digitally reconstruct. The automatic method managed to model 90% of the building's columns, 70.9% of the existing beams, and 75.8% of the walls. For the beams, the algorithm was applied to an additional 15%, but due to occlusions, it could not fully reconstruct these elements, managing only to position them and define their inclinations. In these instances, the dimensions had to be measured and set manually because the beams were buried underground and in poor condition, leading to their classification under the semi-automatic method. The algorithms demonstrated robust performance when applied to the Old Lifeguard Station of Fuseta--a small yet complex structure due to its physical characteristics and state of preservation. However, it can be adapted for buildings of various sizes and architectural complexities. The size of the building does not directly affect the outcome; the primary factor is that the algorithm may need to be applied more frequently, extending the project's duration. For more complex geometries, it is essential to analyze the type of element to be modeled. The column algorithms are applicable to any column with rectangular, square, or circular profiles, whether made of concrete, steel, or wood. For columns with other profiles, such as C or H shapes, the algorithms require modifications to be applicable. The same is true for beam algorithms. Wall algorithms can be used on any straight wall that has both faces scanned. It is important to note that regardless of the building's scale, the algorithms likely cannot reconstruct the structure entirely due to common issues like occlusions or non-visible structures, such as hidden walls and columns obscured by furniture. They serve as auxiliary tools for complex tasks, such as detecting tilts and rotations in columns, modeling floor deformities, or optimizing repetitive tasks like modeling simple walls. In a scan-to-BIM project, the quality of the point cloud is essential to ensure that the final product meets the expected level of detail and quality [64]. Similarly, the effectiveness of the algorithm also depends on the quality of the point cloud. It performs better with a clean point cloud in a building where all architectural elements are exposed and free from significant occlusions.