Data-driven characterization of the correlation between uniaxial compressive strength and Youngs’ modulus of rock without regression models

The uniaxial compressive strength (UCS) and Youngs’ modulus (E) of rock are important parameters required during design stability analysis mining geotechnical structures. There is correlation between UCS E rock, the proper estimation such for reliable engineering analysis. However, limited quantity data pairs often available most project sites makes it difficult to estimate E. This study addresses difficulty by developing Bayesian approach characterizing site-specific joint probability distribution that data-driven, without use an empirical model. A major novelty proposed over previous studies does not require selection or integration a regression model as input characterize likelihood function in directly constructed from only their prior information inputs. incorporated into Markov Chain Monte Carlo (MCMC) simulation generate samples E, which then analysed marginal statistics, distribution, correlation. Real obtained compression tests on migmatites at Sanandaj-Sirjan zone Iran used demonstrate approach. distributions coefficient consistent with those measured adopted site. indicates effective can be when there need characterization site data. Simulated also results show quality inputs play role efficiency hallmark practitioners do determine select appropriate evaluate rock.