Estimating the deformation modulus of rock masses: a comparative study

Kayabaşı, Ali; Gökçeoğlu, Candan; Ercanoğlu, Murat

doi:10.1016/s1365-1609(02)00112-0

Cited by 150 publications

(37 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One of the potential application areas of fuzzy systems is the rock engineering classification systems (Bellman and Zadeh 1970;Nguyen 1985;Nguyen and Ashworth 1985;Juang and Lee 1990;Habibagahi and Katebi 1996;Gokay 1998;Sonmez et al 2003). It has also been used for the construction of models for predicting rock mass and material properties (Grima and Babuska 1999;Finol et al 2001;Kayabasi et al 2003;Lee et al 2003). Other researchers have used fuzzy set and systems for predicting machine performance depending on the properties of rock mass and material (den Hartog et al 1997;Deketh et al 1998;Alvarez Grima and Verhoef 1999;Alvarez Grima et al 2000;Basarir et al 2007;Kucuk et al 2011).…”

Section: Fuzzy Logicmentioning

confidence: 99%

Computational monitoring in real time: review of methods and applications

Dyskin

Başarır

Doherty

et al. 2018

Geomech. Geophys. Geo-energ. Geo-resour.

View full text Add to dashboard Cite

Continuous monitoring of mechanical parameters determining the state of natural and manmade systems is essential in a wide range of engineering disciplines from mechanical to civil and geotechnical engineering. To be effective, the monitoring response time needs to be commensurate with the characteristic time of variation of the processes being monitored e.g. from seconds as for example in some machinery, to weeks and months for mining excavations and years in the case of structures. The methods of measurement can therefore differ significantly for different applications. In spite of this, the methods of information processing-the computational monitoring-have considerable commonality. This review focuses on the methods of computational monitoring in solid and structural mechanics problems in different engineering applications. The traditional methods of monitoring are reviewed along with the corresponding computational and measurement methods. The basic principles of the computational monitoring in real time are established.

show abstract

Section: Fuzzy Logicmentioning

confidence: 99%

Computational monitoring in real time: review of methods and applications

Dyskin

Başarır

Doherty

et al. 2018

Geomech. Geophys. Geo-energ. Geo-resour.

View full text Add to dashboard Cite

show abstract

“…Kayabasy et al [12] derived the following relation form a database of 57 tests showing the influences of weathering degree of the discontinuities on the rock mass deformation modulus:…”

Section: (1a) (1b)mentioning

confidence: 99%

“…Applying multiple regression analysis, and considering the same independent variables, the following equation was obtained [12]:…”

Section: (1a) (1b)mentioning

confidence: 99%

See 1 more Smart Citation

Empirical methods of calculating the mechanical parameters of the rock mass

Vásárhelyi

Kovács²

2016

Period. Polytech. Civil Eng.

View full text Add to dashboard Cite

The knowledge of the main mechanical constants of a rock mass (such as strength, deformability and the Poisson's ratio) is one of the most important for rock engineering design on or in rock mass. Until now, several empirical relationships were determined for calculating these material constants based on both the quality of the studied rock mass (ie. RMR or GSI values) IntroductionLarge scale rock mass characterization introduces material parameters related to mechanical properties. The most important properties are the deformation modulus; the unconfined strength and the Poisson's ratio value of the rock mass in interest. These material parameters are frequently related to laboratory data characteristics of intact rock samples and to the classical rock mass classification systems (e.g. RQD, Q, RMR or GSI). These rock mass quality measures quantify the relation between the rock mass and the intact rock.The established empirical relations between the mechanical parameters of rock masses (unconfined compressive strength, deformation modulus) and the rock mass classification systems (RMR or GSI values) show exponential increasing deformation modulus and compressive strength with the increasing quality of the rock mass.The paper summarizes the observed correlations published between the mechanical properties of rock masses and one of the rock mass classification systems. It was found, that the deformation modulus and the strength of a rock mass data may reflect a simple exponential relationship of the observed quantities. According to analysis of different proposed equations a new formula is suggested and the modification ratio (i.e. ratio of the deformation modulus and the strength of rock mass) is also determined.However, it is important to note, that there are huge differences between the published date and the empirical formulas. The reason for this variance is the difference in situ testing methods, that may give different values of mechanical parameters even for the same rock mass. According to Bieniawski [1], even a single testing method, such as flat jack test, can lead to a widely scattering results even where the rock mass is very uniform. The other reason for the discrepancy in the different calculation methods is the directional effect. Most rock masses are anisotropic and do not have single deformation modulus [2].There is no mechanical (physical) interpretation of the above empirical equations but these were analysed by e.g. [3]. Recently, Ván and Vásárhelyi [4] suggested a damage mechanical approach to analyse them. In this paper their method will be also presented.

show abstract

“…The regression analysis is a widespread statistical method used for the derivation of empirical equations (Bieniawski 1978;Hoek and Brown 1997;Kayabasi et al 2003;Yagiz and Gokceoglu 2010;Kayabaşı 2012;Chen-Chang et al 2011;AlHomadhi Emad 2014), and such analyses require comparable data. In this study, the SPT blow count and parameters derived from the laboratory tests on the disturbed samples are compared.…”

Section: Simple Regression Analysismentioning

confidence: 99%

Some emprical equations for predicting standard penetration test blow counts in clayey soils: a case study in Mersin, Turkey

Kayabaşı

2014

Arab J Geosci

View full text Add to dashboard Cite

The standard penetration test (SPT) is a widespread test used in situ. Soil mechanical properties such as grain-size content, moisture content, atterberg limits, activity of clay, and consistency of clay determine the SPT blow count. In addition, the SPT blow count reflects the soil mechanical and compressibility parameters. The purpose of this study is to determine the clayey soil states that determine the SPT blow count number; 220 data points derived from a foundation investigation of a sewerage project in Mersin City were used in the study. The Mersin sewerage project includes the sewerage station, central pumping station, and west pumping station in Mersin city, Turkey. The corrected SPT blow counts (N 60 ) and corresponding parameters (fine-grain size percent, liquid limit, plastic limit, plasticity index, moisture content, activity, and consistency) of the clayey soil from these locations were used in a series of simple and nonlinear multiple regression analyses. Based on the analyses, the acceptable results have been determined by a simple regression analyses between the corrected SPT blow counts and the following input parameters: activity, moisture content (in percent), finegrained percent (in percent). The simple regression analyses for N 60 -LL (in percent), N 60 -PL (in percent), and N 60 -PI (in percent) are considered unacceptable so these parameters are not evaluated as input parameters in the multiple regression analyses. Multiple regression analyses with two independent input parameters produces acceptable results. The optimal equation is determined with multiple regression analyses using activity and moisture content as the input parameters; this equation has a coefficient of determination of R 2 =0.78. The prediction capability of the developed equations is also acceptable. However, the prediction equations proposed in this study are developed with a limited number of data points. For this reason, a cross-check should be performed before using these empirical equations for design purposes.

show abstract

Estimating the deformation modulus of rock masses: a comparative study

Cited by 150 publications

References 9 publications

Computational monitoring in real time: review of methods and applications

Computational monitoring in real time: review of methods and applications

Empirical methods of calculating the mechanical parameters of the rock mass

Some emprical equations for predicting standard penetration test blow counts in clayey soils: a case study in Mersin, Turkey

Contact Info

Product

Resources

About