Bayesian Analysis of Consolidation Parameters of a Tailings Storage Facility

Density reconciliation of tailings deposits is an essential step in the design and operation of a tailings storage facility (TSF) because it provides the required information to forecast the storage capacity and life span of the facility. Density reconciliation is necessary because different stakeholders (e.g. designers, processing plant operators and TSF operators) inform their density predictions from various data sources. For geotechnical designers, density predictions can rely on evaluating the compressibility and hydraulic conductivity properties of mine tailings to predict filling rates and settlements of tailings deposits. This prediction is a challenging task because the conditions present during testing in the laboratory are generally very different from those existing at the field scale. It is customary to adjust tailings parameters and other inputs required to analyse settlements using engineering judgement to reconcile the model predictions with a limited number of observations from site performance. The adjusted inputs can then be used as the design parameters of the deposit. In this study, we discuss a Bayesian approach for the inference of the design parameters of the tailings that combine prior knowledge of the parameters with information from different sources, including laboratory testing data and observations from site performance. The deposit consolidation settlements are calculated with a commercially available one-dimensional large-strain consolidation model (FSConsol). The Bayesian approach is coded in Python, and the consolidation model is incorporated within the code with a response surface (RS). The method is illustrated with an example of an unnamed TSF that serves to highlight its advantages.