The traditional methods for identifying structural planes of rock mass have problems such as reliance on manual experience and low computational efficiency.To solve the problems

an exploration adit in a pumped storage power station in northwestern China is chosen as a study case.A rapid automatic identification method based on multiple clustering algorithms proposed

with the purpose of enhancing both the objectivity and efficiency of structural plane recognition.3D laser point cloud data are used to create combined feature vectors.Both K-means and DBSCAN clustering methods are applied to automatically identify rock mass structural planes.An empirical threshold is set to filter out fractured surfaces.The research results show that this method can efficiently and automatically accomplish the structural plane identification and the statistical analysis of attitude information for rock mass

significantly reducing manual intervention.The calculation speed is better than the traditional method and can meet the practical needs of engineering.