Central Soil and Materials Research Station, India
Online published on 29 March, 2018.
Shear strength and deformation characteristics of rock mass play an important role in stability analysis of structures founded or constructed in rock mass. These design parameters are determined by conducting direct shear and deformability tests in exploratory drifts or in open trenches. CSMRS is involved in in-situ rock mechanics investigations of important hydropower projects of India and its neighbouring countries. It has been seen through several field tests that there is considerable variability in in-situ design parameters of rock-mass. Therefore, due to various limitations and errors associated with these tests, it may not be appropriate to assign or recommend a single value of design parameters. However, BIS codes (IS 7317 and IS 7746) used by practicing engineers involved in interpretation of field data of uniaxial jacking test and in-situ shear test are silent on this issue. In present study, a probabilistic framework is summarized and illustrated to interpret in-situ shear and deformability test data collected from various hydroelectric projects in Himalayan region. Probabilistic interpretation of shear test data leads to a new type of partial factor of safety (PFOS) which is complimentary to conventional PFOS of IS: 6512 to be used in determination of factor of safety against sliding failure and it is recommended that a better preliminary design must satisfy conventional as well as proposed factor of safety against sliding. On the other hand, probabilistic interpretation of in-situ plate load test data indicates that the total deformation occurred during the test is log-normally distributed. Using this finding, values of modulus of deformation of rock mass are obtained with desired level of confidence and an upper and lower bound on modulus values is also evaluated and it is recommended to use these values for risk analysis by conducting parametric studies in numerical modelling. Thus, present study advocates the probabilistic treatment of in-situ test data to improve the quality of judgment taken by designers while making a choice of in-situ design parameters.
