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A COMPARATIVE ANALYSIS OF ROCK MASS CLASSIFICATION SYSTEMS FOR HYDROPOWER PROJECTS OF PAKISTAN.

Byline: K.Munir, K.Farooq and H.Mujtaba

ABSTRACT: For engineering analysis and design of structures in rock mass, rock mass classification systems often serve as a main tool. Due to the difficulties in retrieving relevant strength parameters of rock mass, classification systems are frequently applied to serve many applications like in assessing Deformation Modulus, Poisson ratio etc. In this study, the authors used four different rock mass classification systems for Diamer Basha Dam and Kohala Hydropower Project sites in order to obtain numerical values which are indicators of qualities of the rock mass. Both sites have different rock characteristics. Diamer Basha Dam Site consists of strong intrusive igneous rocks while Kohala Hydropower Project Site has weak sedimentary rocks. The classification systems used are Rock Mass Rating, Rock Quality Index, Rock Structure Rating, and Geological Strength Index. New correlations have been suggested among these systems after comparing with some existing correlations.

Key words: Rock Mass Classification Systems, Q System, RMR, RSR, GSI

INTRODUCTION

During the feasibility and preliminary design stages of a project, when very little detailed information is available on the rock mass, its stress and hydrologic characteristics, the use of a rock mass classification systems can be of considerable benefit. Rock mass classification systems have been developing for over 100 years since Ritter (1879) attempted to formalise an empirical approach to tunnel design, in particular for determining support requirements (Hoek, 1994).

There are more than ten (10) methods being practiced in the world for rock mass classification. Most popular systems of these have been discussed hereafter; The Geomechanics classification system, most popularly known as Rock Mass Rating (RMR) system proposed by Bieniawski (1973) provides quantitative data for the tunnel reinforcement methods such as rock bolts, shotcrete etc. The system was developed initially for tunnels but has been applied to rock slopes and foundations, ground rippability, and mining problems (Bieniawski, 1983). Rock Quality Index, also known as the Q system was proposed by Barton et al. (1974) and has been developed specifically for tunnels and underground chambers (Wines et al., 2001).

The rock structure rating (RSR) developed by Wickham et al., (1972 and 1974), was the first system featuring classification ratings for weighing the relative importance of rock mass parameters. Geological Strength Index (GSI) introduced by Hoek and Brown in 1995 greatly respects the geological constraints that occur in nature and are reflected in the geological information. These classification systems have been applied to dam foundations, tunnels and underground excavations very successfully.

RMR and the Q system are the major classification systems for estimates of rock support. Both systems use the most important ground features as input parameters such as RQD, condition and spacing of the discontinuities and groundwater etc. Each of these parameters is classified individually and each rating expresses the quality of the rock with respect to stability of underground structure.

The data sets regarding rock mass are not always available in a format that may immediately be applied to a specific engineering problem, the correlations may be very useful to rapidly derive different design tool. Furthermore, the availability of correlation equations between classification systems facilitates a rapid means of verifying resultant rock mass rating values, without necessitating the re-calculation of the values (Dyke, 2006).

Using the rock mass classifications for Basha and Kohala sites, new correlations have been developed in this study which have been compared with the following well known correlations of different authors.

Each of these expressions has arisen from a series of specific data taken from some worksites. Therefore use of these correlations with extreme prudence about the compatibility of the data has been recommended by many researchers. Bieniawski's equation is based on the case histories having Poor to Good quality rocks. Rutledge and Preston (1978) has worked on sedimentary rocks while Tugrul (1998) has developed his correlations working on limestone. The Hoek's relation between GSI and RMR is for good competent rocks with GSI (Greater than) 25.

MATERIAL AND METHODS

Geological and Structural Features of Diamer Basha Dam Site: The prevailing rock type at Diamer Basha dam site is intrusive igneous which is petrologically called Norite, or more precisely Gabbro-Norite (GN), (NEAC, 2004). Another rock formation is Ultramafic Association (UMA) having mafic minerals more than 90 (Percent) . At the site, the rock appears very strong and massive. From the portal of the left bank adit (adit 4), the rock mass is massive but has a very complicated joint pattern. The joint spacing is in the range of 1 - 3 m. Most of the joints are tight and show no infill.

The spacing between open joints with infill is 6 - 7 m. Minor seepage can be observed along the some joint planes (DBC,2008).

The conditions in the access part of right bank adit (adit 5) are favourable with massive GN. The rock is jointed and some of the discontinuities have a persistence of (Greater than) 10 m. The adit intersects three steeply inclined fracture zones, which might be evidence for stress relief (DBC, 2008).

The area of the dam has been investigated by several means of exploration. The main information had been gathered by drilling, water pressure tests and adit excavation. Extensive laboratory testing has been carried out in three batches in Central Material Testing Laboratory, WAPDA from 2006 to 2007.

Geological and Structural Features of Kohala Hydropower Project Site: Rocks exposed at the Kohala Hydropower Ptoject site are Sandstone and Shale which belong to Murree formation. Sandstone has been classified into two types i.e. Sandstone-1 (SS-1) and Sandstone-2 (SS-2). SS-1 is the dominant rock unit in the Powerhouse area and along the tunnel route. The rock is generally fresh, fine to medium grained, well cemented and hard. As seen in the core samples, joints are mostly tight, with few joints having filling and coating of calcite. SS-2 is a transitional unit between grey colour SS-1 and Shale. The fine grained rock is generally reddish brown in colour. It is medium hard and comparatively thin bedded. The rock looks highly weathered on surfaces, but in core samples it is generally fresh to slightly weathered. The Shale present at site is mostly interbedded with SS-2. It is reddish brown in colour, fine grained and comparatively less hard. At places it is splintery with well developed laminations.

The geological and geotechnical investigations were accomplished through geological mapping, core drilling at different locations, geophysical surveys, excavation of two adits, in-situ testing in the adits and laboratory testing.

Geomechanical Properties of Rocks: Extensive lab testing has been carried out on the samples obtained from both the sites. These have been summarized in the following table;

Table-1: List of Lab Tests Performed

Test###No. of Samples###No. of Samples

###(Basha)###(Kohala)

Index Property Tests###77###94

Point Load Strength Index Test###96###96

Uniaxial Compressive Strength###106###117

Modulus of Elasticity###79###36

Poisson's Ratio###79###36

Tensile Strength###10###54

The standard average values obtained from the extensive laboratory testing for both the projects including average RQD values are given in Table 2. The results from the laboratory tests were extrapolated to the rock mass with the help of Rocklab software. The program is fed with input data like; UCS, GSI (Geological Strength Index), mi (Material constant), D (Disturbance factor) and Intact Modulus.

Table-2: Summary of Laboratory Test Results - Average Values

Site###Rock Type###RQD###Uniaxial Compressive###Young's###Poisson###Point Load Strength

###Strength (MPa)###Modulus (GPa)###Ratio###Index (MPa)

Basha###Gabbro-Norite###75 - 85###100###50###0.25###5.2

###UMA###75 - 85###80###100###0.26###4.8

###SS-1###35 - 40###80###40###0.20###8

Kohala###SS-2###25 - 30###50###30###0.15###5

###Shale###20 - 25###20###25###0.10###3

Classification Systems Applied: In this study, four most well-known and recognized rock mass classification systems have been used which are, Rock Mass Rating (RMR) Rock Quality Index (Q-System), Rock Structure Rating (RSR) and Geological Strength Index (GSI). All these methods incorporate geological, geometric and design/engineering parameters in arriving at a quantitative value of their rock mass quality. The RMR system considers the orientation of discontinuities and material strength, which are not directly included in the Q system. However, the Q system considers rock stress and the joint set number, which are only indirectly considered in the RMR system. The RSR system considers geological and construction parameters and suitable for the selection of steel supports while RMR and Q systems are suitable for the selection of modern tunnel support (Tugrul, 1998).

For Diamer Basha Dam, laboratory testing data and geological mapping of two adits (total length 1183 m) have been used as the input parameters to classify the rock mass. The rock has been classified in segments of 25 m along the length of both the adits. For Kohala Hydropower Project site, data of fourteen (14) boreholes of dam area, desander, diversion tunnel and powerhouse area have been used as per procedures of ASTM D5878-08, 2008.

RESULTS AND DISCUSSION

Engineering Classification of Rock Mass of Both Sites: Rock mass of both the sites have been classified in four classification systems by standard formula of each system. All the parameters have been evaluated and incorporated carefully. The histograms representing the frequency of numerical values of different classification systems for Basha and Kohala sites are shown below in Fig. 1 and 2 respectively; As can be observed from above figures, generally the data range for all the systems is from 50 - 90 for Basha and from 20 - 60 for Kohala indicating good quality of rock mass for Basha site and from poor to fair for Kohala site. The combination of data of both sites gives a wide range for study which has a considerable advantage for regression analyses. Furthermore, the wide rock mass classes also provide an advantage in the use of the correlations.

Based on the mean values, the classifications of rock mass of the Diamer Basha Dam site show that the rock is from Good to Very Good in all four classification systems while for Kohala, the classifications show Poor quality of rock as described in the following table.

Table-3: Rock Mass Classifications of Basha and Kohala Sites

Site###RMR###Q System###RSR###GSI

###Mean###73###78###75###55

###Rock Quality###Good###Very Good###Good###Fair/Good

Basha###Standard Deviation 7.54###23.51###4.81###5.33

###Min###59###34###65###45

###Max###87###126.17###985###65

###Mean###38###3.47###40###35

###Rock Quality###Poor###Poor###Poor###Poor/Fair

Kohala###Standard Deviation 16.39###12.58###6.99###9.50

###Min###8###0.01###13###13

###Max###69###42.08###68###58

A total number of 143 (48 of Basha and 95 of Kohala) rating value sets in four classification systems were used. Using these numerical values for both the sites, correlations have been developed between classification systems by regression analysis as described in Figures. 3 - 6 below.

Lower values in the graphs demonstrate the relatively weak rock mass of Kohala while higher values correspond to the strong rocks of Basha as shown in the above figures. All the correlations have shown good regression coefficients in the range of 0.83 to 0.90. Although, while classifying the rock mass systems, the numerous variations that occur in rock masses and the uncertainties involved in observing and recording the different parameters can lead to very low regression coefficients. Moreover, different classification systems place different emphases on the various parameters. For example stress is not used specifically in RMR, whereas Q system uses a stress reduction factor. Also material strength is an integral part in RMR but not in Q system (Milne et al., 1998). All the rock mass classification systems have some limitations, but if applied appropriately and with care they are valuable tools and can generate very useful correlations to use.

Based on the study, following new correlations are proposed to be used; The comparisons of correlations developed in this study with the most renowned existing correlations are presented in graphical form in the following figures (7 - 10); While comparing such correlations with each other, it should be kept in mind that mostly the correlations are based on site specific data. So some variations may always be expected. The correlations developed in this study are in comparison with the other existing correlations as the slopes of the graphs show. There is slight variation in the correlation of Q system with RSR. The reason may be that Rutledge (1978) has developed his correlation by using the classifications of weak sedimentary rocks. Also due to fact that Q system is relatively difficult to familiarize having large variations in parameters, some deviation can be expected in correlations involving Q system. However the correlation of Tugrul (1998) is very much similar to the correlation of this study.

Conclusions

1. Based on the geological data of Basha and Kohala hydropower project sites, it can be inferred that Basha dam site mainly comprises two types of rocks mass namely Gabbro-Norite (GN) and Ultramafic Association (UMA). The high values of RQD for these rocks (75 to 85 (Percent) indicate that the Bahsa dam site rocks are classified from good to very good. At Kohala hydropower project site, three types of rock units exist, i.e., SS-1, SS-2 and Shale. The RQD values for Kohala rocks ranges between 20 - 45 (Percent) indicating poor to fair quality rocks.

2. The rock mass rating as determined by Q system for the Basha dam site varies in the narrow range of 40 to 120. Such a narrow range of Q values depicts that rock mass at Basha dam site is fairly homogenous, where as for Kohala site, the Q values vary between 0.01 and 40 indicating variable and relatively poor quality rocks as compared with Basha dam site.

3. The correlations between various rock mass classification systems have been developed and are presented in Eq. 9 through Eq. 12. The equations developed among various rock mass parameters have very good regression coefficients (from 0.835 to 0.901) indicating strong correlations between various rock mass classification systems.

4. The correlations developed through present study are generally in comparison with the other existing correlations being used across the world. However, as evident from Fig. 7 through Fig. 10, some of the existing correlations do not match with those developed by this study indicating that such correlations are quite empirical and can only be applied to similar rock type and conditions. Further, due to fact that Q system is relatively difficult to use having large variations in the input parameters, some deviation can be expected in correlations involving Q system.

REFERENCES

ASTM D5878 - 08, Standard Guides for Using Rock- Mass Classification Systems for Engineering Purposes, (2008).

Bieniawski Z. T, Classification of Rock Masses for Engineering: The RMR system and Future Trends, (1983).

David R. Wines, Peter A. Lilly, A Comparative Analysis of Rock Mass Classification Schemes in part of the Fimistone Open Pit Operation in Kalgoorlie Western Australia. Australian Geomechanics:59-72 (2001).

DBC (Diamer Basha Consultants J.V.):. Bidding Documents of Lot 1, Vol (3), Section VI, Technical Specifications and Drawings, Annex 2.1: Factual Report Geology, (2008).

DBC (Diamer Basha Consultants J.V.):. Tender Design Report, Geology and Engineering Geology, (2008).

Dyke, G. P. A quantitative correlation between the mining rock mass rating and in-situ rock mass rating classification systems, M.Sc. thesis report, (2006).

Hoek, E. Strength of rock and rock masses, ISRM News Journal 2(2): 4-16 (1994).

Kohala HPP Consultants, Kohala Hydropower Project, Detailed Engineering Design Report, Vol (3), (2009).

Milne. D, Hadjigeorgiou. J, R. Pakalnis, Rock Mass Characterization for Underground Hard Rock Mines, Tunnelling and Underground Space Technology, Vol 13(4): 383-391 (1998).

NEAC Consultants, Feasibility Study of Diamer Basha Dam Project, Water and Power Development Authority, Lahore, (2004).

Rutledge, J.C., Preston, R.L., Experience with engineering classifications of rock for the prediction of tunnel support. In. Proceedings of the International Tunnelling Syposium, Tokyo: A-3-1. 7. (1978).

Tugrul. A, , The application of rock mass classification systems to underground excavation in weak limestone, Ataturk dam, Turkey, Journal of Engineering Geology, Vol 50: 337-345 (1998).

Department of Civil Engineering, University of Engineering and Technology, Lahore Corresponding author email: engineerkhawar@yahoo.com
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Publication:Pakistan Journal of Science
Article Type:Report
Date:Mar 31, 2013
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