# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 6 Conclusions  
In this paper, an automatic geometric accuracy evaluation method based on laser scanning is proposed and applied to the geometric accuracy evaluation of the Hongqi Road subway station. The method uses coarse-to-fine point cloud and BIM coordinate registration, and the problem of inconsistent coordinate reference between the design BIM and point cloud is resolved. A hierarchical index system is constituted to assess the geometric accuracy evaluation of various subway objects. Evaluation indexes are used to  
Figure 27: Measured point cloud of structure column A-2. A red curtain was wrapped around the perimeter of structural column A-2.  
achieve the surface accuracy evaluation and structural column accuracy evaluation. The over-limit areas can be located automatically; using statistical analysis and visualization, structural column deviations are also quantitatively analyzed. The accuracy of the coarse-to-fine registration was 17.8 \(\pm\) 0.94 mm. Centimeter-level accuracy evaluation is realized in large-scale sites such as subway stations that have floor surface areas that are larger than 10,000 m\({}^{2}\). The successful application in the Hongqi Road subway station demonstrates the potential of this proposed method. It can be applied to other comparable applications.  
However, the building objects do not fit closely with the grid for subway stations with special form structures. The grid-based coarse matching cannot be used, and manually coarse matching is needed. In future work, we will extend to special form underground structures to make the method more general.  
Conceptualization, Q.W. and F.Y.; methodology, Q.W. and F.Y.; software, Q.W. and F.Y.; validation, Q.W., P.Q. and F.Y.; formal analysis, F.Y.; investigation, L.Y.; resources, F.Y.; data curation, Y.L. and T.L.; writing--original draft preparation, Q.W.; writing--review and editing, Q.W.; visualization, Q.W.; supervision, P.Q. and F.Y.; project administration, P.Q. and F.Y.; funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript.  
The project was supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (no. KF-2021-06-022). This research was funded by the National Natural Science Foundation of China (nos. 42001322, 41871015, and 41671003).  
Not applicable.  
The authors would like to gratefully acknowledge China Railway 19 th Bureau Group Co. for their help in accessing and processing the data.  
The authors declare no conflict of interest.  
A description of the nomenclature used in this article.  
Abbreviation Nomenclature  
3D Three-dimensional  
BIM Building Information Modeling  
AEC Architecture, Engineering, and Construction  
2D Two-dimensional  
GNSS Global Navigation Satellite System  
ICP Iterative Closest Point  
RANSAC Random Sample Consistency  
PCA Principal Component Analysis  
E Genetic Algorithm  
ANN Artificial Neural Network  
CWT Continuous Wavelet Transform  
PL-ICP Point-to-line Iterative Closest Point  
KD tree K-Dimensional tree  
_PDM_ Plane Discrepancy Metric  
_SCDM_ Structural Column Discrepancy Metric  
_ADD_ Angular Distance Deviation