## 1 Introduction
If components are not specified as the same type in the modeling software, shared geometry information may not be recorded. Additionally, when an IFC file is imported, manipulated by other software, and then exported again, or when multiple IFC files are merged, shared geometry information may be lost, undermining redundancy removal effectiveness. The method proposed in this study addresses these issues by providing a more robust solution, ensuring that geometric redundancy is effectively removed regardless of the data export and import processes. Another approach involves the iterative closest point (ICP) method based on point cloud matching, as demonstrated by [PERSON] et al. [17] in their cross-platform online visualization system for open BIM. The ICP method generates point clouds from BIM models to determine matching and identify redundancy. However, traditional ICP methods face limitations such as dependency on initial model positions and efficiency issues due to the multiple iterations required to address measurement errors. The enhanced ICP method offers higher stability and efficiency, making it better suited for geometric redundancy removal in BIM models. Common mesh simplification algorithms, such as vertex deletion, edge collapse, triangle collapse, region merging, and vertex clustering, often present issues like uncontrollable simplification processes and non-intuitive thresholds. These algorithms typically aim to preserve mesh details in generic meshes, which can be problematic when applied to BIM models for steel structures. In these models, small details like bolts are sharp, and considering curvature, larger flat components are more likely to be removed than smaller parts, distorting the original structural characteristics. The method proposed in this study uses the length of the collapsed edge as an error measure, making the mesh simplification process more understandable for users and more suitable for representing the steel structure model. To overcome these limitations, this study aims to develop a lightweight BIM system and optimize the system's performance according to the specific characteristics of steel structural models. To achieve this, (i) BIM data needs to be extracted into the Web system and remain intact, and (ii) the visualization of BIM models should be smooth. AlthoughIFC is a universal BIM data standard, its structure is complex and coupled. In order to achieve a BIM system for the construction management of steel structures, it is necessary to design and optimize the storage of BIM data. Furthermore, the lightweight visualization of BIM models is also an essential part of BIM application. The rendering performance of BIM visualization is closely related to both the project type and information quantity of the BIM model. Therefore, it is an important work to optimize the rendering performance of BIM models with graphics technology. Using BIM technology to solve specific engineering problems is still a hot issue in the field of civil engineering [18; 19]. However, few studies have focused on the BIM system for the construction and management of steel structures. Because of the distinct characteristics of steel structures, the performance optimization of the BIM system is urgently required and needs to be studied further. This study proposed a solution for information extraction and storage for the BIM platform, optimized the system performance through several techniques, including dynamic level of detail (LOD) model loading and redundant instance removal, and finally, realized the key functions of the Web-oriented information management system, which is mainly used in the construction stages for steel structures. During this process, the edge collapse algorithm, principal component analysis (PCA) algorithm, and Hausdorff-based comparison algorithm were employed. Several techniques such as static rendering and cache mechanism were also implemented to improve the speed of data transmission and the efficiency of model display.