Probabilistic characterisation of uniaxial compressive strength of rock using test results from multiple types of punch tests

The determination of uniaxial compressive strength (UCS) is central to most mining and geotechnical engineering analyses and designs in rock engineering. Direct measurement of UCS through uniaxial compression tests are costly and time-consuming. This is a challenge in mining engineering practice. However, there is a possibility to have results of other tests (e.g. punch tests) which are simple to conduct, particularly at the early stage of many mining engineering projects. The information contained in the results of multiple punch tests can be combined under a Bayesian approach to characterise UCS. This study proposes a Bayesian approach for probabilistic characterisation of UCS based on information from multiple sources, including the results of multiple punch tests available at a specific rock site. The proposed Bayesian approach is formulated to sequentially incorporate data from the parameters of three punch tests, namely Schmidt rebound hardness (SRH), block punch index (BPI) and point load strength () to update statistics and probability distribution of UCS. The approach is illustrated using real SRH, BPI and data at a sandstone site. The proposed Bayesian approach is shown to perform satisfactorily for the probabilistic characterisation of UCS as results of additional type of punch tests are incorporated.