There are clear advantages in the establishment of reliable, direct cone penetration test (CPT) based methods for assessment of the axial capacity of driven piles. These advantages motivated the formation of a joint industry project (JIP) under the management of the Norwegian Geotechnical Institute (NGI), which initially led to the creation of a unified database of high-quality pile load tests in sand and clay. The unified database has the consensus approval of representatives of the profession and personnel in multiple companies from the offshore energy sector. This paper presents a component of the research from the second phase of the JIP, which had the objective of developing a new CPT-based method for driven piles in clay to unify several CPT-based methods that are in use today. First, a rational basis for the CPT-based formulation is described, using trends from instrumented pile tests; the description facilitates an understanding of the approach and illustrates its empirical nature and limitations. The unified database was used to calibrate the formulation and it led to good predictions for an independent database of pile load tests and for measured distributions of shaft friction.
With the introduction and revisions of geotechnical limit states design (LSD) standards such as Eurocode 7, rock engineering design is moving towards reliability-based design, a method for which statistical characterisation of design parameters is essential. However, the often limited project-specific data in rock engineering do not allow straightforward application of classical statistical analyses, and thus alternative approaches are required. In this paper, hierarchical Bayesian modelling is first introduced as a means of logically combining data from different sources to augment limited project-specific data. A Bayesian hierarchical non-linear regression model for the analysis of rock strength data is then developed and implemented; it is applied to 40 strength data sets of granite retrieved from the literature. In the context of these data, the advantages of the hierarchical model and the improvements in strength parameter estimations brought about by its application are discussed. Also discussed is the goodness-of-fit of the hierarchical model in comparison with more conventional statistical models. The paper concludes with suggestions for further development of the proposed hierarchical model, and the potential of hierarchical modelling as a general approach to statistical modelling of geotechnical data.
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