Refined modeling and identification of complex rock blocks and block-groups based on an enhanced DFN model

详细信息    查看全文

• 作者：Mingchao Li^a ; ^{lmc@tju.edu.cn} ; Ye Zhang^a ; Sibao Zhou^b ; Fugen Yan^c
• 关键词：Rock block ; Block identification ; Monte Carlo simulation ; Discrete fracture network (DFN) ; Stochastic polygonal model ; Underground powerhouse
• 刊名：Tunnelling and Underground Space Technology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：62
• 期：Complete
• 页码：23-34
• 全文大小：3672 K
• 卷排序：62

文摘

Valid modeling and identification of rock blocks are the keys to analyzing rock-mass stability. Based on the Monte Carlo method and 2D Poisson point process, an enhanced polygonal discrete fracture network (DFN) model is proposed firstly. The equal area conversion algorithm and subarea simulation method are used to control fracture size and to determine fracture shape, respectively. Then, coupling the polygonal DFN model with the large-scale geological model, a refined rock mass structure model for identifying rock blocks is established. Subsequently, the spatial representations of polygonal fracture planes, complex geological surfaces and free surfaces are presented. And the major characteristics and the refined modeling method of complex rock blocks and block-groups are analyzed. Finally, a modified and precise topology-based identification method of rock blocks and block-groups is put forward. The application in the underground powerhouse of a hydropower station indicates that the proposed approach and scheme are very efficient and can identify arbitrary-shaped rock blocks. The identified rock blocks and block-groups contain geometric information, geological information as well as physical and mechanical information. This research contributes to further study on the stability analysis of rock blocks and block-groups and rock mass seepage.