# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 4 Results
Subsequently, we manually modeled features of the building such as doors, windows, stairs, and handrails to finalize the model with the desired level of detail. The final model is a mix of elements modeled by the algorithm with automatic and semi-automatic approaches and elements modeled manually (Figure 21). The elements modeled by the algorithm showed a high accuracy rate and few deviations measured from the building's point cloud. The floors and columns showed the highest average accuracy, with 4 mm and 6 mm, respectively (Table 2), while the accuracy was 7 mm for the structural beams, 9 mm for the walls, and, finally, 8 mm for the terrain and topographic surface. To measure the performance of the algorithm in the beams' reconstruction, only the support beams of the main floor and roof were considered; the beams at the base of the building and the ramp were not included in this calculation because they had many problems with occlusion, corrosion, and deformities due to constant contact with sea water. In these cases, it was not possible to accurately calculate the accuracy and performance of the algorithm. After using the algorithmic approaches to create the final model, it was possible to reach an accuracy of 93.3% for accuracy of up to 25 mm and 80.6% for accuracy of up to 12 mm, and the average accuracy of the entire model was 8 mm; excluding the terrain, these values were 94.7%, 82.9%, and 7 mm, respectively. The total time spent modeling these elements was 5 h and 40 min. This time does not include the algorithm development time, as the idea is that the codes are reused in other cases and other situations, not just one-off solutions for this project but as a reusable approach. The rest of the building elements were modeled using the manual approach. The reason why some elements could not be reconstructed with the algorithms varied in each case. Some were due to their geometric characteristics that made it impossible to use the algorithm, others due to occlusion problems or a lack of points in the point cloud in the case of beams and columns, and others due to design decisions such as the slabs and access ramp. In addition, complementary elements such as doors, windows, stairs, and handrails that were not part of the automation studies were also included manually in the final model. Figure 20: Floor plan of the main level of the Old Lifeguard Station of Fuseta building. In order to measure the actual efficiency of the algorithms, the research created, in addition to the model built with the codes, a model built manually in the traditional way. The elements modeled in this new model were the same as those modeled with the algorithms, which allows a comparison between the two approaches in terms of precision, accuracy, and time consumed. These models created with a manual approach performed worse than those created by the algorithm in terms of accuracy and positioning (Figure 22). The overall average accuracy of model elements created with manual modeling was 18 mm, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Approach** & \multicolumn{2}{c}{**Accuracy (\% of Points)**} & **Average** & \\  & & **Up to 12 mm** & **Up to 25 mm** & **Accuracy** & **Time Consumed** \\ \hline Columns & Automatic & 90.4\% & 96.9\% & 6 mm & 1 h 19 min \\ Beams & Automatic & 85.0\% & 94.0\% & 7 mm & 3 h 11 min \\ Walls & Automatic & 73.2\% & 90.6\% & 9 mm & 33 min \\ Floors & Semi-automatic & 92.3\% & 97.6\% & 4 mm & 25 min \\ Topography & Automatic & 84.3\% & 93.7\% & 8 mm & 12 min \\ Building (except terrain) & Auto and Semi & 82.9\% & 94.7\% & 7 mm & 5 h 28 m \\ Total & Auto and Semi & 80.6\% & 93.3\% & 8 mm & 5 h 40 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Level of accuracy and the time consumed in the modeling using algorithms with automatic and semi-automatic approaches. Figure 21: Level of accuracy of elements modeled by the algorithm with automatic and semi-automatic approaches compared to the point cloud obtained from the terrestrial 3D laser scanning of the existing building.