SDZM: Software for determining shear damage zones of rock joints. (February 2022)

• Record Type: Journal Article
• Authors: Xia, Ding; Ge, Yunfeng; Tang, Huiming; Zhang, Bocheng; Shen, Peiwu
• Is Part Of: Computers & geosciences. Volume 159(2022)
• Publication Date: 2022-02
• Subjects: Rock joints -- Shear damage zones -- SDZM toolbox; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.105021
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Determination of the shear damage zones of rock joints leads to a better understanding of shear mechanical behavior. Here, we propose the SDZM toolbox (Shear Damage Zones Measurement Toolbox for Rock Joint), easy-to-use Python-based software for measuring shear damage zones of post-shearing rock joints using photos collected by digital cameras. Images of post-shearing specimens can be loaded, cropped, and scaled before analysis, and regions of interest (ROIs) can be drawn interactively to mask the shear damage based on its distribution. Then, shear damage analysis is performed based on an image segmentation algorithm to analyze the image according to the ROIs, and a variety of postprocessing tools allow users to view, filter, save and export analysis results visually. Eight specimens of three different lithologies were analyzed using the SDZM toolbox, and two traditional methods to verify the performance. The results show that the SDZM toolbox is more fast, precise and fine in analyzing the shear damage zones, saves 88.80% of the time compared to the manual method, and has higher accuracy and adaptability relative to the Riss method. In addition, suggestions for parameter selection of the postprocessing have been studied and the robustness of the SDZM toolbox has been demonstrated. Highlights: We develop an open-source software named SDZM toolbox to determine the shear damage zones of rock joints. The capability and robustness of the SDZM toolbox have been demonstrated by the analysis of eight different rock specimens. The SDZM toolbox saves 88.80% of time compared to the manual method, and has 73.72% lower error than the Riss method.