Correlation between Strength and Durability Indices of Rocks- Soft Computing Approach

被引：22

作者：

Ahmad, M. ^{[1
]}

Ansari, M. K. ^{[1
]}

Sharma, L. K. ^{[1
]}

Singh, Rajesh ^{[2
]}

Singh, T. N. ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Earth Sci, Bombay 400076, Maharashtra, India

[2] Univ Lucknow, Dept Geol, Lucknow 226007, Uttar Pradesh, India

来源：

ISRM EUROPEAN ROCK MECHANICS SYMPOSIUM EUROCK 2017 | 2017年 / 191卷

关键词：

Strength; Durability; Correlation; Regression; Neural Netrwok; SLAKE DURABILITY;

D O I：

10.1016/j.proeng.2017.05.204

中图分类号：

TD [矿业工程];

学科分类号：

0819 ;

摘要：

Strength and durability are the most important and critical parameter of rock and these two are the key point to evaluate the rock for different purposes. Several classifications have been given for rock on the basis of their strength and durability and are used world-wide. Strength indices such as point load index, impact strength index, aggregate impact value, aggregate crushing value and durability indices such as slake durability index, aggregate abrasion value are the furthermost parameters being evaluated and applied for engineering structures, building stones and construction aggregates. Present study shows the correlation between these important parameters of the rock and tries to propose an empirical equation so that one property can be determine from the others by applying this empirical equation. To estimate the good correlation between the properties of rocks, different categories of igneous, metamorphic and sedimentary rocks were used. Imperial equations and prediction models were determined using regression analysis and neural networking, respectively. Finally, using the proposed equation, obtained values were classified along with the different classifications for rock strength and durability. (C) 2017 The Authors. Published by Elsevier Ltd.

引用

页码：458 / 466

页数：9

共 18 条

[11] EVALUATION OF EMPIRICAL-METHODS FOR MEASURING THE UNIAXIAL COMPRESSIVE STRENGTH OF ROCK [J].

CARGILL, JS ;

SHAKOOR, A .

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES & GEOMECHANICS ABSTRACTS, 1990, 27 (06) :495-503

[12]

FRANKLIN JA, 1979, INT J ROCK MECH MIN, V16, P143

[13] Predicting the Los Angeles abrasion loss of rock aggregates from the uniaxial compressive strength [J].

Kahraman, S. ;

Fener, M. .

MATERIALS LETTERS, 2007, 61 (26) :4861-4865

[14]

McCulloch WS, 2016, EMBODIMENTS OF MIND, P19

[15] Neural network design for engineering applications [J].

Rafiq, MY ;

Bugmann, G ;

Easterbrook, DJ .

COMPUTERS & STRUCTURES, 2001, 79 (17) :1541-1552

[16] A correlation between Schmidt hammer rebound numbers with impact strength index, slake durability index and P-wave velocity [J].

Sharma, P. K. ;

Khandelwal, Manoj ;

Singh, T. N. .

INTERNATIONAL JOURNAL OF EARTH SCIENCES, 2011, 100 (01) :189-195

[17] Prediction of geomechanical parameters using soft computing and multiple regression approach [J].

Singh, Rajesh ;

Umrao, Ravi Kumar ;

Ahmad, M. ;

Ansari, M. K. ;

Sharma, L. K. ;

Singh, T. N. .

MEASUREMENT, 2017, 99 :108-119

[18] Slake durability study of shaly rock and its predictions [J].

Singh, TN ;

Verma, A ;

Singh, V ;

Sahu, A .

ENVIRONMENTAL GEOLOGY, 2005, 47 (02) :246-253

← 1 2 →