Investigation into engineering parameters of marls from Seydoon dam in Iran

Abstract The quality of designed structures embedded in rocks is strongly related to rock strength parameters of intact rock. Measuring different parameters from tests could be very expensive in designing phase of projects. Estimating some parameters from other ones can reduce costs and time of project procedure. In this paper, the relationships between static and dynamic parameters of marls are studied by using the single and multiple linear regressions. For this purpose, several marl core samples from Seydoon region, Khoozestan Province in Iran are collected and tested. Some equations with sufficient correlation have been obtained to predict the engineering parameters of marls, especially the uniaxial compressive strength (UCS).

[1]  M. Heidari,et al.  New weathering classifications for granitic rocks based on geomechanical parameters , 2013 .

[2]  Abdolazim Azimian,et al.  An Empirical Correlation of Uniaxial Compressive Strength with P-wave Velocity and Point Load Strength Index on Marly Rocks Using Statistical Method , 2014, Geotechnical and Geological Engineering.

[3]  E. Yaşar,et al.  Correlating sound velocity with the density, compressive strength and Young's modulus of carbonate rocks , 2004 .

[4]  Kadir Karaman,et al.  Utilizing the strength conversion factor in the estimation of uniaxial compressive strength from the point load index , 2016, Bulletin of Engineering Geology and the Environment.

[5]  T. Singh,et al.  Prediction of compressive strength and elastic modulus of carbonate rocks , 2016 .

[6]  Akbar Cheshomi,et al.  Evaluation of single particle loading test to estimate the uniaxial compressive strength of sandstone , 2015 .

[7]  K. Diamantis,et al.  Study on uniaxial compressive strength, point load strength index, dynamic and physical properties of serpentinites from Central Greece: Test results and empirical relations , 2009 .

[8]  H. Yoon,et al.  Characterization of rock weathering using elastic waves: A Laboratory-scale experimental study , 2017 .

[9]  Z. T. Bieniawski,et al.  Suggested methods for determining the uniaxial compressive strength and deformability of rock materials: Part 1. Suggested method for determining deformability of rock materials in uniaxial compression , 1979 .

[10]  M. Zoback,et al.  Empirical relations between rock strength and physical properties in sedimentary rocks , 2006 .

[11]  S. Chehreh Chelgani,et al.  Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks , 2010 .

[12]  G. Tsiambaos,et al.  Considerations on strength of intact sedimentary rocks , 2004 .

[13]  S. Kahraman.  Evaluation of simple methods for assessing the uniaxial compressive strength of rock , 2001 .

[14]  S. Yagiz.  Correlation between slake durability and rock properties for some carbonate rocks , 2011 .

[15]  R. Tomás,et al.  Proposal of a New Parameter for the Weathering Characterization of Carbonate Flysch-Like Rock Masses: The Potential Degradation Index (PDI) , 2016, Rock Mechanics and Rock Engineering.

[16]  T. N. Singh,et al.  A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks , 2012, Neural Computing and Applications.

[17]  G. Lagaly,et al.  Layer charge determination by alkylammonium ions , 1994 .

[18]  T. N. Singh,et al.  Indirect estimation of compressive and shear strength from simple index tests , 2016, Engineering with Computers.

[19]  I. Yılmaz,et al.  An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters , 2008 .

[20]  Jian-Fu Shao,et al.  Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine , 2015 .

[21]  M. Giambastiani.  Soft rocks in Argentina , 2014 .

[22]  Z. Moradian,et al.  Predicting the Uniaxial Compressive Strength and Static Young's Modulus of Intact Sedimentary Rocks Using the Ultrasonic Test , 2009 .

[23]  E. T. Brown.  Rock characterization, testing & monitoring: ISRM suggested methods , 1981 .

[24]  Ayhan Kesimal,et al.  A comparative assessment of indirect methods for estimating the uniaxial compressive and tensile strength of rocks , 2015, Arabian Journal of Geosciences.

[25]  R. Holt,et al.  Laboratory Observation and Micromechanics-Based Modelling of Sandstone on Different Scales , 2015, Rock Mechanics and Rock Engineering.

[26]  Lianyang Zhang,et al.  Engineering properties of rocks , 2005 .

[27]  D. Mishra,et al.  Rock failure modes under uniaxial compression, Brazilian, and point load tests , 2013, Bulletin of Engineering Geology and the Environment.

[28]  Kripamoy Sarkar,et al.  Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach , 2015, Geotechnical and Geological Engineering.

[29]  Saffet Yagiz,et al.  Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer , 2009 .

[30]  Danial Jahed Armaghani,et al.  Prediction of the uniaxial compressive strength of sandstone using various modeling techniques , 2016 .

[31]  Z. Moradian,et al.  Predicting slake durability index of soft sandstone using indirect tests , 2010 .

[32]  A. Moradzadeh,et al.  Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties , 2012 .

[33]  Y. Géraud,et al.  Prediction of Porosity and Density of Calcarenite Rocks from P-Wave Velocity Measurements , 2013 .

[34]  M. Karakus,et al.  Fuzzy and Multiple Regression Modelling for Evaluation of Intact Rock Strength Based on Point Load, Schmidt Hammer and Sonic Velocity , 2006 .

[35]  Giovanna Vessia,et al.  Statistical Method for Assessing the Uniaxial Compressive Strength of Carbonate Rock by Schmidt Hammer Tests Performed on Core Samples , 2012, Rock Mechanics and Rock Engineering.

[36]  Behnam Yazdani Bejarbaneh,et al.  Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network , 2014 .

[37]  Goran Vlastelica,et al.  Impact of weathering on slope stability in soft rock mass , 2014 .

[38]  Akshay Venkatesh,et al.  Correlation Between Point Load Index and Uniaxial Compressive Strength for Different Rock Types , 2012, Rock Mechanics and Rock Engineering.

[39]  İ. Çobanoğlu,et al.  Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity , 2008 .

[40]  Z. T. Bieniawski,et al.  The point-load test in geotechnical practice , 1975 .

[41]  R. Tomás,et al.  An approach for characterising the weathering behaviour of Flysch slopes applied to the carbonatic Flysch of Alicante (Spain) , 2015, Bulletin of Engineering Geology and the Environment.

[42]  Danial Jahed Armaghani,et al.  Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks , 2015 .

[43]  Per Horsrud,et al.  Estimating Mechanical Properties of Shale From Empirical Correlations , 2001 .

[44]  D. Mishra,et al.  Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system , 2013 .

[45]  Kaveh Ahangari,et al.  Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method , 2013, Arabian Journal of Geosciences.

[46]  Mostafa Sharifzadeh,et al.  Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics , 2012 .

[47]  Bulent Tiryaki,et al.  Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees , 2008 .

[48]  D. Bennett,et al.  The effect of specimen size and stress rate for the Brazilian test—A statistical analysis , 1990 .