# Automatic Bridge Design Parameter Extraction for Scan-to-BIM
## 3 BIM Model-Based Validation
### BIM Model-Based Validation Result and Discussion  
Case 1 was conducted assuming the noise level and data loss level that simulates the environment of LiDAR measurement (5%) for the beam girder bridge. The predicted design parameters for Case 1 is shown in Figure 18 and Table 1. When 5% noise + data loss were applied, the average error rate was about 1.95%. \(H_{1}\), which represents the height of the bridge, resulted an error rate of approximately 1.86%. \(H_{2}\), which represents the height of the beam and showed an error rate of about 1.82%, and \(H_{3}\), the thickness of the slab, showed an error rate of 4.2%. \(L_{1}\), representing the span length, showed an error rate of about 0.09%. \(W_{1}\) represents the width of the bridge and showed an error rate of about 0.19%. \(W_{2}\) represents the width of all beam girders and showed an error rate of about 0.65%. \(W_{3}\) indicates the width of the flange and showed an error rate of about 1.23%. \(W_{4}\) indicates the width between the beams and showed an error rate of about 2.95%. \(W_{5}\) indicates the web of the beam. There was an error rate of about 4.57%. When comparing the error rates of the parameters, it is shown that the error rate of \(L_{  
Figure 19 shows the result of applying design parameter extraction with noise + data loss for different bridge types. Types of parameters were different for each bridge type, and thus the results cannot be directly compared. However, the result showed how the noise level and data loss level can influence the accuracy of the parameter extraction. When the noise ratio was above a certain value (e.g., about 30% for beam girder bridges), the error rate did not increase and showed a tendency to converge. This is due to the kNN algorithm, which was used for the noise reduction. The kNN algorithm can reduce the noise by removing the outliers when noise level is low enough. However, when noise exceeds certain value, noise points were not considered as outliers anymore; the points were rather considered as inliers. In the case of data loss, there was no significant change in the error rate. Because we have used more than 300,000 data points, the result was reasonably accurate even with 50% data loss. However, when noise + data loss are applied together, the error rate tends to converge faster because the density of PCD affects the consideration of noise as an outlier.  
Figure 19: BIM Model-based validation test result for different type of bridges.  
There are two main sources of the error. The first source is an error due to noise + data loss. When there was no noise, the average error rate was about 0.06% as shown in Table 1, but the error rate increased to 1.95% as the noise + data loss increased to 5%. The second source of the error is due to the plane prediction procedure using the MSAC algorithm. In this study, the MSAC algorithm is applied to find the most probable plane based on probability. The MSAC algorithm is an effective parameter estimation method, but it does not use all the data; The MSAC estimates the parameters by randomly selecting sample data. Therefore, the result of the most probable plane varies depending on the selected sample data. The accuracy tends to increase (while the deviation tends to decrease) with more numbers of data points in PCD. For example, \(L_{1}\), which has relatively large numbers of data points, resulted in high accuracy with low deviation. However, \(W_{5}\), which has relatively small numbers of data points, resulted in lower accuracy with higher deviation. In other words, the error caused by the MSAC algorithm highly depends on how dense the PCD is, and it is expected that the error can be reduced by obtaining denser PCD.