Reliability based design approaches are usually adopted to explicitly consider uncertainties in the rock mass and make decisions on selecting design parameters for tunnel-support problems. However, it is difficult to accurately characterize the input random variables due to fewer sample availability and costly observations. Thus, it is necessary to focus on the characterization and reduction of epistemic uncertainty of those input variables, which have higher relative contributions towards the variability in output. These input variables are identified using global sensitivity analysis based on Sobol indices conducted for the supported circular tunnel in Hoek–Brown rock mass. Convergent confinement analysis method is applied to obtain the output—radius of yield zone (Rpl\documentclass[12pt]{minimal}
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\begin{document}$$R_{pl}$$\end{document}), tunnel convergence (urD\documentclass[12pt]{minimal}
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\begin{document}$$u_{r}^{D}$$\end{document}) and induced load on installed support (psD\documentclass[12pt]{minimal}
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\begin{document}$$p_{s}^{D}$$\end{document}). The analysis is conducted for both uncorrelated and correlated input parameters. For uncorrelated input, the sensitivity measures are divided into correlated and uncorrelated contributions to provide insights into the mechanism of the tunnel-support system. The results obtained by assuming input variables as uncorrelated suggest that the relative contribution of variation in uniaxial compressive strength (UCS) of rock is highest towards the variation in the outputs followed by geological strength index (GSI) of the rock mass, its Young’s modulus (Ei\documentclass[12pt]{minimal}
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\begin{document}$$E_{i}$$\end{document}) and thickness of the liner support. However, by considering the correlation between input parameters, the sensitivity measures change significantly. The ranking of input parameters in descending orders of their relative contribution also changed due to the presence of high correlated contributions. The strong correlation between UCS and Ei\documentclass[12pt]{minimal}
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\begin{document}$$E_{i}$$\end{document} makes them most sensitive, followed by GSI, while thickness of the liner impacts psD\documentclass[12pt]{minimal}
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\begin{document}$$p_{s}^{D}$$\end{document} the most. It is found that strong correlations do not necessarily mean higher correlated contributions, rather, it depends on the physics of the tunnel-support system. The 95% confidence interval of ground response curve and longitudinal deformation profile changes considerably for correlated rock mass parameters compared to the uncorrelated case resulting in higher variance of urD\documentclass[12pt]{minimal}
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\begin{document}$$u_{r}^{D}$$\end{document} and lower variances of Rpl\documentclass[12pt]{minimal}
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\begin{document}$$R_{pl}$$\end{document} and psD\documentclass[12pt]{minimal}
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\begin{document}$$p_{s}^{D}$$\end{document}. Additionally, the investigation of average error in the variance of output, induced by keeping individual parameters as deterministic, yielded high values for highly sensitive inputs and vice versa.
