Ground anchors are crucial components in various construction and engineering applications. They play a critical role in retaining structures and, therefore, design guidelines have established the necessity of comprehensive testing campaigns to derive the anchors characteristic resistance. The latter is a specified percentile within a presumed statistical distribution. In principle, a limited number of investigation tests cannot be used to estimate the characteristic values. To overcome this limitation, in a simplified way, the design codes suggest reducing the resistance found in experimental results by a factor to estimate the anchor characteristic resistance to be used in the design. In this paper, the authors propose a new approach for interpreting ground anchor test results and determining the statistical distribution of ground anchor resistance. The approach is based on the use of Bayesian updating, formulated as a structural reliability problem, and on the definition of a simplified phenomenological model relating the imposed load and the measured anchor (creep) displacements. This distribution can be used to determine a “proven” anchor characteristic resistance, which can then be used to update the anchor design.

@en

Bayesian inference poses as a means for characterizing the uncertainty in geotechnical parameters based on limited site investigation data. In this study, a Hierarchical Bayesian analysis framework is used to analyse a site investigation database in order to derive geotechnical soil parameters for two widely applied strength models. The first one focuses on calibrating the relationship between in-situ CPT measurements and undrained shear strength. The second one is the SHANSEP soil strength model, which is used forevaluating the undrained shear strength using OCR information. The framework operates in a hierarchical fashion, performing inference on separate project sites and at the same time drawing conclusions on a global level. The result is site characterization on a probabilistic level and the derivation of geotechnical parameters together with their probability distributions. The results are assessed by evaluating their influence in the failure probability of a geotechnical structure, demonstrating that the proposed hierarchical approach provides a more complete description of uncertainty than standard practice methods.@en