文献类型：期刊文献

英文题名：A Dice Similarity Measure for TBM Penetrability Classification in Hard Rock Condition with the Intuitionistic Fuzzy Information of Rock Mass Properties

作者：Yong, Rui[1,2];Ye, Jun[3];Du, Shi-Gui[1,2];Zhang, He[2];Gu, Leiyu[4];Li, Hang[5]

机构：[1]Shaoxing Univ, Ctr Rock Mech & Geohazards, Shaoxing, Peoples R China;[2]Zhejiang Univ, Ocean Coll, Zhoushan, Zhejiang, Peoples R China;[3]Shaoxing Univ, Dept Informat & Elect Engn, Shaoxing, Peoples R China;[4]Shanghai Inst Disaster Prevent & Relief, Shanghai 200433, Peoples R China;[5]Cent South Univ, Sch Resources & Safety Engn, Changsha, Hunan, Peoples R China

年份：2021

卷号：25

期号：14

起止页码：2658

外文期刊名：EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000479359400001)、、EI(收录号：20193207278206)、Scopus(收录号：2-s2.0-85075473144)、WOS

基金：The study was funded by National Natural Science Foundation of China (No. 41502300), Zhejiang Provincial Natural Science Foundation (No. LQ16D020001).

语种：英文

外文关键词：Intuitionistic fuzzy set; penetrability classification; similarity measure; TBM

外文摘要：Penetration rate is a key parameter for the performance prediction of hard rock tunnel boring machines (TBM), which has a high correlation with rock mass properties. The purpose of this study is to find an appropriate method for estimation of penetrability classification of TBM. To achieve this aim, a similarity measure model and a classification method were proposed based on the similarity measures of intuitionistic fuzzy sets of rock properties. The relationships between actual measured TBM penetration rate and rock properties were discussed by statistical analysis on the published database obtained from Queens's tunnel project in New York. Then the rock mass parameters were classified into three categories and these normalized categories were transformed into intuitionistic fuzzy sets. According to the Dice similarity measures between the vectors of rock mass properties at each tunnel station, the TBM penetrability conditions in hard rocks can be predicted and described into Good, Medium, and Poor classes. Eventually, in comparison with the measured penetrability results, this suggested method was demonstrated to be effective for penetrability classification with accuracy of 77.5%.