文献类型：期刊文献

英文题名：A New Representative Sampling Method for Series Size Rock Joint Surfaces

作者：Huang, Man[1,2];Hong, Chenjie[1];Ma, Chengrong[1];Luo, Zhanyou[3];Du, Shigui[1];Yang, Fei[1]

机构：[1]Shaoxing Univ, Dept Civil Engn, 508 Huancheng West Rd, Shaoxing 312000, Zhejiang, Peoples R China;[2]Louisiana State Univ, Dept Civil & Environm Engn, 3255 Patrick F Taylor Hall, Baton Rouge, LA 70803 USA;[3]Zhejiang Univ Sci & Technol, Geotech Engn Inst, 318 Liuhe Rd, Hangzhou 310023, Zhejiang, Peoples R China

年份：2020

卷号：10

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：SCI-EXPANDED(收录号：WOS:000540456100001)、、Scopus(收录号：2-s2.0-85085973863)、WOS

基金：The study was funded by the National Natural Science Foundation of China (Nos. 41572299, 41427802), Natural Science Foundation of Zhejiang Province (No. LY18D020003), and Key Research and Development Projects of Zhejiang Province (No. 2019C03104). Their support is gratefully acknowledged.

语种：英文

外文摘要：The greatest variability in both shear strength and roughness exists for joint samples with smaller size, which underscores the necessity of performing representative sampling. This study aims to provide a representative sampling method for series size joint surfaces. The progressive coverage statistical method is introduced to provide the sufficient sample capacity for series sampling sizes by setting different propulsion spaces. The statistical law of the joint surface morphology at different sampling sizes is measured by the 3D roughness parameter with theta max?/(C+1). Through an application in nine natural large-scale rock joints, nine consecutive sampling sizes from 100mm x 100mm to 900mm x 900mm are selected and 121 samples are successfully acquired from each sampling size. According to the frequency distribution of roughness statistics, a new sampling method combining the layering principle and K-medoids clustering algorithm is proposed to screen representative joint samples for each sampling size. The sampling results that meet the test accuracy requirements suggest the possibility of realizing an intelligent sampling method. In addition, the representative of the interlayer cluster center is validated. Finally, the comparison results with the traditional stratified sampling method prove that the proposed method has better stability.