Abstract
A rock slope can be characterized by tens of persistent discontinuities. A slope can be massive. The slip surface of the slope is usually easier to expand along with the discontinuities because the shear strength of the discontinuities is substantially lower than that of the rock blocks. Based on this idea, this paper takes a jointed rock slope in Hengqin Island, Zhuhai as an example, and establishes a three-dimensional (3D) model of the studied slope by digital close-range photogrammetry to rapidly interpret 222 fracture parameters. Meanwhile, a new Floyd algorithm for finding the shortest path is developed to realize the critical slip surface identification of the studied slope. Within the 3D fracture network model created using the Monte Carlo method, a sequence of cross-sections is placed. These cross-sections containing fractures are used to search for the shortest paths between the designated shear entrances and exits. For anyone combination of entry point and exit point, the shortest paths corresponding to different cross-sections are different and cluttered. For the sake of safety and convenience, these shortest paths are simplified as a circular arc that is regarded as a potential slip surface. The fracture frequency is used to determine the probability of sliding along a prospective critical slip surface. The potential slip surface through the entrance point (0, 80) and exit point (120, 0) is identified as the final critical slip surface of the slope due to the maximum fracture frequency.
Highlights
IntroductionSlope instability will destroy buildings, block roads, and seriously threaten human life and property safety [1–3]
Slope stability analysis is an important part of geological engineering and geotechnical engineering research to prevent slope destruction
222 fractures are quickly obtained by digital close-range photogrammetry, and a new Floyd
Summary
Slope instability will destroy buildings, block roads, and seriously threaten human life and property safety [1–3]. Slope stability analysis relies heavily on identifying the crucial slip surface and calculating the safety factor [4–6]. The fundamental challenge in slope stability analysis is determining the critical slip surface. Some mathematical programming methods and intelligent algorithms, such as the artificial fish swarms algorithm, the ant colony algorithm, genetic algorithms, the imperialistic competitive algorithm, and the black hole algorithm, have been used in recent years to locate the crucial slip surface [8–12]. These methods produce good results in identifying the critical slip surface of a soil slope, they cannot be Remote Sens.
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