# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 5 Evaluation  
The Distance-DNN-based Scan-vs-BIM was applied to actual steel structures to conduct structural integrity evaluation experiments. The target steel structure covered an area of 423 m\({}^{2}\) with a height of 10 m and included 184 structural objects (Figure 8). In the past, it was constructed through As-Planned BIM and used as a factory. Three-dimensional scanning data were collected at the time of completion for the remodeling of the structure, and the point cloud data were collected using a Faro Focus3D X 330 laser scanner (FARO Technologies, Inc., Lake Mary, FL, USA). This scanner has a range of up to 330 m and an accuracy of \(\pm\)2 mm. During data acquisition, the scanner was set to a resolution of 1/5 (corresponding to approximately 122,000 points per second) and a quality setting of 4\(\times\) to balance between data density and scanning time. The total number of points acquired was approximately 10 million. Structural deformation of the objects did not occur during the process used. There were differences between the As-Built and As-Planned data due to the addition of pipes and equipment (Figure 8). The BIM model was created using Autodesk Revit 2022. BIM parameters were accessed programmatically using Dynamo (version 2.17), allowing us to extract information such as nodes, edges, and element geometries. The BIM model was constructed at LOD 200, with structural object details accurately represented. The experimental results for the SIE and SETA networks are listed in Table 3. The integrity evaluation of structural column objects was conducted based on the Distance-DNN framework. It took 42 ms to evaluate the integrity of all 20 structural column objects. The integrity evaluation rate for one column object was calculated to be 2.1 ms and was faster than the traditional Scan-vs-BIM framework. It also used less computational resources. The network predicted that all columns were, on average, 94.68% accurate. Additionally, as a result of the structural error type analysis, the proportion of the \"Error\" label was high. This is attributed to the SETA network's prediction that the probability of \"Error\" among structural error types is highest because equipment and pipes are added to the target structure. However, since only the point cloud around a single object was included in the calculation process, and the structural object of the target steel structure was not deformed, the SIE network predicted no difference between BIM and scan. From the experiments, it was proved that the SIE and SETA networks could derive accurate results for structural integrity evaluation and show faster operation speed than the traditional Scan-vs-BIM framework. \begin{table}
\begin{tabular}{c c c} \hline
**Training Results** & **SIE Network** & **SETA Network** \\ \hline Accuracy rate & 0.9577 & 0.6897 \\ Loss rate & 0.03 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of SIE and SETA network evaluation. Figure 8: (**a**) As-Planned BIM structural model, (**b**) As-Built point cloud, and (**c**) inside view. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Column**} & \multirow{2}{*}{**Integrity Rate**} & \multicolumn{6}{c}{**Error Rate**} \\ \cline{2-7}  & & **Tilt** & **Deflection** & **Rotation** & **Error** & **Total** \\ \hline
1 & 0.97780 & 0.00067 & 0.00109 & 0.00369 & 0.01675 & 0.02220 \\
2 & 0.93720 & 0.00073 & 0.00531  
To validate the computational efficiency of our proposed framework, we conducted experiments comparing the processing times of the traditional Scan-to-BIM method and our method under the same conditions. The traditional method required approximately 8 h to process the entire structural dataset, primarily due to the mesh generation and alignment steps. In contrast, our method completed the same task in approximately 3 min, demonstrating a reduction in processing time by over 95%.