钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术，融合随机抽样一致（random sample consensus, RANSAC）算法对传统的具有噪声的基于密度的聚类方法（density-based spatial clustering of applications with noise, DBSCAN）算法进行改进，对钢拱桥拱肋线形进行提取。三维激光点云数据具有全面性和细节体现的优势，能够完整地呈现桥梁结构的形状和变形信息，融合RANSAC的改进DBSCAN算法根据钢拱桥结构特征对聚类结果进行约束，能够很好地实现删除离散点及桥面、横撑、横联和腹杆部分的点云这一目的。根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合，与人工提取结果进行对比，拱肋关键点提取误差均在毫米级，最大误差为9.2 mm，最小误差为0.1 mm，此提取方法能够更加准确有效地完成钢拱桥线形提取，使线形提取精度达到毫米级，大大降低了人力成本和时间成本，对钢拱桥的复杂结构有更好的鲁棒性，能很好地适应实际生产需求。

The alignment monitoring of steel arch bridges constitutes an essential component of bridge health monitoring systems. Three-dimensional laser scanning technology was utilized, and the traditional density-based spatial clustering of applications with noise(DBSCAN) algorithm was improved by integrating the random sample consensus(RANSAC) algorithm to extract the alignment of steel arch bridge ribs. Three-dimensional laser point cloud data, characterized by its comprehensiveness and detailed representation, is capable of fully presenting the structural shape and deformation information of the bridge. The RANSAC-integrated improved DBSCAN algorithm, constrained by the structural features of the steel arch bridge, effectively achieves the removal of discrete points as well as point clouds from the bridge deck, cross bracing, lateral connections, and web members. Point clouds extracted using the RANSAC-integrated improved DBSCAN algorithm are fitted to identify key points, and a comparison is made with results obtained manually. The extraction errors for the key points of the arch ribs are all within the millimeter range, with the maximum error being 9.2 mm and the minimum error being 0.1 mm. This extraction method is demonstrated to more accurately and effectively accomplish the alignment extraction of steel arch bridges, achieving millimeter-level precision in alignment extraction. It significantly reduces labor and time costs, provides better robustness for the complex structures of steel arch bridges, and adapts well to practical production demands.