Decoding lithological variability: alternating calcareous marls and their role in geomechanical rock mass properties

Finely interbedded rock formations are common within the argillaceous soft rock spectrum, yet they are often treated as homogeneous materials in rock engineering and underground construction. This assumption can lead to widely scattered and inaccurate geomechanical parameters, such as strength or stiffness. This study investigates the statistical distribution of marlstone sublayers in the argillaceous Unterangerberg Formation (Tyrol, Austria) and its correlation with geotechnical properties derived from laboratory tests. We analyze uniaxial compressive strength, splitting tensile strength, elasticity modulus, as well as cohesion and friction angle. The testing program encompasses multistage uniaxial and triaxial compression tests, as well as splitting tensile tests. A comprehensive dataset from core logging and rock mechanical laboratory testing is analyzed to assess whether variations in sublayer thickness contribute to the observed dispersion in these geomechanical properties. We also examine how alternative statistical summaries influence the perceived variability of parameters. The results show that the finely interbedded calcareous marlstone layers within the lithology exhibit distinctive geotechnical characteristics, with clay-rich layers generally showing tendencies towards lower strength. Variability in sublayer thickness influences the spread of laboratory test results, and layered specimens tend to yield smeared responses that reflect both constituents. These findings suggest that lithological heterogeneity plays an essential role in soft rock behavior. These results show the need to account for small-scale sedimentary layering and its statistical distribution when predicting the geomechanical behavior of argillaceous soft rock and interpreting laboratory test results for rock engineering applications. Incorporating probabilistic characterization of sublayer heterogeneity can improve predictive reliability and inform design decisions.