The characterisation of geotechnical materials for the design of mine and civil slopes requires the collection of data through site and laboratory investigations. The information provided by data contributes to the reduction of the knowledge uncertainty of design parameters. However, the amount of data collected at different stages of project development is normally limited and subjected to budget and time constraints. Proper assessment of data sufficiency at each stage is, therefore, a key aspect of the slope design process.
The paper discusses the common technique used to relate the number of data points with safe values of the design parameters based on the concept of confidence interval (CI) from classical statistics (i.e. the frequentist approach). This conventional approach is then contrasted with a technique based on the highest density interval (HDI) from Bayesian statistics, which offers a simpler and more intuitive way to judge the sufficiency of data. The discussion is illustrated with examples of analysis of uniaxial compressive strength (UCS) data, and the intact rock strength parameters σci and mi of the Hoek–Brown strength criterion.
