# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 2 Related Work
### Geometric Accuracy Evaluation  
Recently, the application of soft computing techniques in the reliability analysis of buildings and structures is ever increasing [43]. [PERSON] et al. [44] integrated a Genetic Algorithm (GA) and Artificial Neural Network (ANN) to predict the risk priority of buildings in highly seismic front regions. ANN was used by [PERSON] et al. [45] and [PERSON] et al. [46] to simulate climate change scenarios to evaluate the reliability of urban drainage systems. The experimental results can ensure the sustainable development of the city. Among them, laser scanning has been widely used in the accuracy inspection of construction components, such as precast elements, construction panels, and steel reinforcement. Some scholars measure the accuracy of concrete surfaces based on methods defined by construction industry standards (e.g., the straightedge method, the F value method, and the corrugation index method). Additionally, existing methods rely on the accuracy inspection of a small number of measurement points on a construction surface, which interferes with a comprehensive and complete accuracy inspection of the surface [46]. Some scholars [21] have used laser scanning technology to obtain the data required for the F value method to calculate the surface flatness, which overcomes the time-consuming sampling and low accuracy disadvantages of traditional methods. However, this method is still unable to accurately describe the geometric details of the entire plane. [PERSON] et al. [47] used 3D laser scanning to evaluate the accuracy of rectangular precast concrete elements. Their method measured the dimensional size characteristics of the elements and evaluated their accuracy by detecting the boundary points of the precast concrete elements. [PERSON] et al. [25] proposed a method to evaluate the flatness quality of building structure surfaces by segmenting the wall point cloud using the RANSAC algorithm and calculating the deviation of the point cloud data from the plane after fitting the point cloud data to the plane to obtain the unit normal vector. [PERSON] et al. [46] proposed a method based on laser scanning to detect the flatness of building surfaces, applying a continuous wavelet transform (CWT) to obtain information such as the period and position of the undulations of the building surface. Geometric accuracy evaluation is different from the quality inspection of individual components and focuses more on the comprehensive evaluation of the construction site and the construction status, involving a variety of building structures and building components. It is thus difficult to form an effective evaluation system by considering only individual parts of the evaluation process (such as registration and inspection). The existing methods are only evaluated for a single object, and no hierarchical evaluation index system is proposed for subway stations to meet the evaluation of different construction objects of facades, slabs, and columns. Geometric accuracy evaluation has been an important topic within the AEC fields [6]. Geometric accuracy evaluation refers to comparing the size, location, and orientation of objects in the point cloud with the design BIM to avoid potential quality problems. In recent years, scholars have started to use 3D laser scanning to evaluate the quality of construction sites. There have been studies combining construction specifications and proposing frameworks for geometric accuracy evaluation. A generic framework for accuracy control integrating sensors, laser scanning, and design models proposed by [PERSON] et al. [48] consists of five steps: creating a design model, identifying inspection objects according to project construction specifications, developing an inspection plan, collecting data, and accuracy control. [PERSON] et al.