Quantification of elastic wave velocity in jointed rock mass and its suggestion to rock mass classification

Abstract

High degrees of urbanization, coupled with the Korean peninsula's mountainous terrain, make the efficient utilization of underground space in rock mass a vital necessity. Underground construction in rock masses is often full of uncertainty as rocks are innately anisotropic and heterogeneous at different scales. Proper ground investigation and estimation of rock mass behavior before the construction of underground structures are necessary for safe and economic construction. Rock masses are innately anisotropic and heterogeneous, with discontinuities at different scales ranging from microcracks to faults. Hence, the estimation of rock mass behavior is often difficult and affected by various factors. One method to reduce the uncertainty of rock masses is through quantitative rock mass classification systems. Rock mass classification allows the grading and grouping of rock masses with similar characteristics based on a defined guideline to better predict their behavior during construction. The rock mass rating (RMR) and Q-system are the most widely used rock classification methods. Another method for estimating rock mass behavior is geophysical wave-based methods. Numerous studies have noted correlations between rock classification value and parameters and wave velocity. However, most previous studies are heavily reliant on site-specific field data or empirical relationships, and a proper theoretical or general framework is needed.This dissertation aims to suggest a wave velocity-based rock mass classification system that can be used to estimate rock mass properties. Elastic wave propagation characteristics in jointed rock masses for different joint conditions are investigated using experimental and numerical studies. The results are quantified and correlated with the rock mass classification parameters of the RMR and Q-system. The relationships between classification system parameters and wave velocity are normalized based on a predefined reference jointed rock condition and used as factors for calculating the classification rating. The deduced relationships are improved through probabilistic analysis using a field dataset to remove low probability cases. Field verification is conducted between the estimated rock mass classification values onsite measurements to verify and analyze the applicability, reliability, accuracy, and limitations. The proposed methods can aid engineers in engineering design and provide additional insights into rock mass classification in the field.