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Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry

机译:基于无人机物体斜坡摄影测量的高陡边坡落石不连续性解释与识别

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摘要

Discontinuity extraction and interpretation of fractured masses is of high importance when analyzing rock slope stability. Regarding high-steep slopes, which are areas that are difficult to reach, traditional methods to obtain discontinuities, such as the sample window method (SWM), are unlikely to be implemented, resulting in chal-lenges for the identification of potential rockfalls. With the development of the unmanned ariel vehicle (UAV) technology, discontinuity extraction can overcome by noncontact photogrammetry. However, there is still a lack of comprehensive and practical solutions to fulfill rockfall identification from field investigation to in-door analysis. For this purpose, a practical case study was carried out in Wanzhou, Chongqing, China, where a 400 m vertical rock slope prone to rockfall was collected as a typical example. The centimeter-level 3D Textured Digital Outcrop Model (TDOM) and dense Point Cloud (PC) were established using high-resolution photos ac-quired by nap-of-the-object photogrammetry. The discontinuity of the fractured mass was interpreted by fully taking advantage of both 2D images (texture information-dominated) and 3D PCs (depth information -dominated). Furthermore, a new parameter rock cavity rate (RCR) and the corresponding semiautomatic extraction method based on point clouds are proposed. Subsequently, the possibility of various failure modes and their joint combinations were determined by kinematic analysis. Finally, the rock slope stability was determined using a matrix that considers the slope mass rating (SMR) value and the parameter RCR. The proposed process flow and relevant techniques in this study provide an operable and practical solution for further application regarding discontinuity interpretation and potential rockfall identification on high-steep slopes.
机译:在分析岩质边坡稳定性时,裂隙体块的不连续性提取和解释非常重要。对于难以到达的高陡坡,传统的不连续性获取方法,如采样窗法(SWM),不太可能实施,导致识别潜在落石的难度。随着无人驾驶飞行器(UAV)技术的发展,非接触式摄影测量可以克服不连续性提取问题。然而,目前仍缺乏全面实用的解决方案来实现从野外调查到室内分析的落石识别。为此,以重庆万州市为例,以一个400 m的易发生落石的垂直岩质边坡为例,开展了实际案例分析。利用物体摄影测量法获取的高分辨率照片,建立了厘米级的三维纹理数字露头模型(TDOM)和密集点云(PC)。通过充分利用 2D 图像(纹理信息主导)和 3D PC(深度信息主导)来解释断裂块体的不连续性。此外,提出了一种新的参数岩腔率(RCR)及其基于点云的半自动提取方法。随后,通过运动学分析确定了各种失效模式及其关节组合的可能性。最后,使用考虑边坡质量额定值(SMR)值和参数RCR的矩阵确定岩质边坡稳定性。本研究提出的工艺流程和相关技术为高陡边坡上不连续性解释和潜在落石识别的进一步应用提供了可操作和实用的解决方案。

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