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Conventional approval requirements exclusively ask for minimum strength values, which have to be met. The probabilistic approach estimates how likely none of the comparatively manufactured units fails during operation.
Both questions are juxtaposed and compared here with respect to the load cycle tests. The influence of the sample sizes is discussed additionally.
Measuring means knowing. The structural engineer’s Knowledge about structures is vitally important for the assessment of their structural safety. This contribution shows, how non-destructive testing methods can be used to collect valuable Information about existing structures. This value is expressed in this paper by the usability in probabilistic assessments and thus by the reliability of the information. The development of non-destructive testing methods in civil-engineering allows the realistic measurement and visualization of inner constructions of concrete components with a minimum of destructive interventions.
The evaluation of the quality of measurement data is of fundamental importance for quantitative measurements in order to ensure the objectivity of testing and evaluation and to assess the reliability of the knowledge acquired. Both systematic and random deviations must be identified, quantified and taken into account to obtain statistically sound data. The Focus of this contribution is on the methodical path, how displayed measurement data can be processed into reliable knowledge.
It is not about developing assessment methods but about providing
necessary knowledge to increase their operational usability.
In this presentation, we discuss the potential of probabilistic approaches to the design and assessment of offshore foundations. The potential is demonstrated in a numerical example considering a laterally loaded monopile. As an outlook, we present a concept for managing the risk associated with installing large monopiles.
The aim of the study is to infer the soil stratification from the provided CPT and borehole data. We infer the soil type at any location within the domain of interest from the SBT index Ic (Robertson, 2009). This index can be directly related to the CPT data through an empirical correlation model. In addition, the soil classes contained in the borehole logs can be expressed as bounds on Ic. A log-transformation was applied to Ic, Y = ln(Ic), and Y was modelled by a 3D Random Field, with a fully Bayesian hierarchical Gaussian Process model to explicitly capture uncertainties.
This presentation discusses the potentials of probabilistic methods in offshore foundation installation, from the perspective of probabilistic ground models and data-driven site characterization. We discuss about methodologies for utilizing site-specific geotechnical (CPT) and geological data, aiming to construct an integrated ground model that can predict stratigraphic profiles and useful for geotechnical design parameters at any location within a 3D domain. The predicted parameters and stratigraphy are then used to predict the probability of potential pile tip damage, upon collision with a boulder.