Influence of rock heterogeneity on the correlation between uniaxial compressive strength and Brazilian tensile strength

被引:0
作者
Kong, Fanmeng M. [1 ,2 ]
Han, Mingyi [2 ]
Zhao, Yuting T. [1 ,3 ]
Lu, Haitao [3 ]
Liu, Shian [1 ,3 ]
Luan, Pengyu [1 ,3 ]
Zhuo, Baolong [4 ]
Shi, Gaofei [1 ,3 ]
机构
[1] MNR, Key Lab Geol Safety Coastal Urban Underground Spac, Qingdao 266061, Peoples R China
[2] China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China
[3] Qingdao Geoengn Surveying Inst, Qingdao Geol Explorat Dev Bur, Qingdao 266101, Peoples R China
[4] Qingdao Geol & Mineral Geotech Engn Co Ltd, Qingdao 266101, Peoples R China
来源
SCIENTIFIC REPORTS | 2025年 / 15卷 / 01期
基金
中国国家自然科学基金;
关键词
Grain size; Anisotropy; Correlation; Uniaxial compressive strength; Brazilian tensile strength; ENGINEERING PROPERTIES; GRANITIC-ROCKS; INDEX; PREDICTION; ANISOTROPY; TOOLS; MODEL; SIZE;
D O I
10.1038/s41598-024-84715-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
To offer guidance for using Brazilian tensile strength (BTS) to estimate UCS of heterogeneous rocks, this study uses sandstone (fine or coarse grain) and gneiss (0 degrees, 45 degrees, 90 degrees inclined anisotropy) to investigate the influence of grain size or anisotropy on the correlations of UCS-BTS. According to the regression analysis, there is no significant equation of UCS-BTS for rocks with vertical anisotropy. The grain size variation or multidirectional anisotropy can result in a decrease in the determination coefficient value of correlations. Then, coarse grain size or vertical anisotropy deteriorates the statistical performance of correlations between UCS and BTS, reflected by the Akaike Information Criterion and performance index. For rocks with fine grain size or 45 degrees inclined anisotropy, the data points of estimated UCS are clustered uniformly around the exact estimation line. Finally, the accuracy of predicted UCS via BTS declines obviously following the varying grain size or different anisotropy orientations. Using empirical formulas with different grain sizes or anisotropy properties can generate significant errors in estimated UCS. To predict UCS, BTS should be extracted from rocks with single grain size magnitude or unidirectional anisotropy. Moreover, the Brazilian test parallel to the anisotropy cannot be used to derive the correlation of UCS-BTS.
引用
收藏
页数:17
相关论文
共 50 条
[41]   Electrical resistivity measurement to predict uniaxial compressive and tensile strength of igneous rocks [J].
Kahraman, Sair ;
Yeken, Tekin .
BULLETIN OF MATERIALS SCIENCE, 2010, 33 (06) :731-735
[42]   A comparative assessment of indirect methods for estimating the uniaxial compressive and tensile strength of rocks [J].
Karaman, Kadir ;
Kesimal, Ayhan ;
Ersoy, Hakan .
ARABIAN JOURNAL OF GEOSCIENCES, 2015, 8 (04) :2393-2403
[43]   Study on the relation between uniaxial compressive strength and point load strength [J].
Wang, Ruihong ;
Jiang, Yuzhou ;
Guo, Jinlong ;
Tang, Tiancai .
ADVANCES IN CIVIL AND STRUCTURAL ENGINEERING III, PTS 1-4, 2014, 501-504 :282-+
[44]   Assessment of Scale Effects on Uniaxial Compressive Strength in Rock Salt [J].
Ozkan, I. ;
Ozarslan, A. ;
Genis, M. ;
Ozsen, H. .
ENVIRONMENTAL & ENGINEERING GEOSCIENCE, 2009, 15 (02) :91-100
[45]   Prediction of Uniaxial Compressive Strength of Rock Using Machine Learning [J].
Dadhich S. ;
Sharma J.K. ;
Madhira M. .
Journal of The Institution of Engineers (India): Series A, 2022, 103 (04) :1209-1224
[47]   The effect of rock classes on the relation between uniaxial compressive strength and point load index [J].
S. Kahraman ;
O. Gunaydin .
Bulletin of Engineering Geology and the Environment, 2009, 68 :345-353
[48]   Correlations between Uniaxial Compressive Strength and Dynamic Elastic Properties for Six Rock Types [J].
Rahman, Tabish ;
Sarkar, Kripamoy .
INTERNATIONAL JOURNAL OF GEOMECHANICS, 2023, 23 (06)
[49]   Application of Six Metaheuristic Optimization Algorithms and Random Forest in the uniaxial compressive strength of rock prediction [J].
Li, Jingze ;
Li, Chuanqi ;
Zhang, Shaohe .
APPLIED SOFT COMPUTING, 2022, 131
[50]   Prediction Method of Rock Uniaxial Compressive Strength Based on Feature Optimization and SSA-XGBoost [J].
Xie, Huihui ;
Lin, Peng ;
Kang, Jintao ;
Zhai, Chenyu ;
Du, Yuchao .
SUSTAINABILITY, 2024, 16 (19)