Integral Probabilistic Reliability Assessment for Flood Defences

The Impact of Correlations

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Since 2017, Dutch flood defences are assessed according to
new safety standards. These standards are based on flooding probabilities and
rely on several assumptions and approximations. There are concerns that the
combination of these assumptions leads to conservative results. Recently
computed probabilities of failure are often much higher than expected by dike
managers and the outcomes of former assessment methods. This conservative bias
results in a large and expensive reinforcement task in the coming years which
can be reduced by improving the current assessment procedure.  One of the reasons for the current
conservatism is the assumption of mutual independence of dike sections and
failure mechanisms. Currently, the different elements are assessed
independently, while failure mechanisms and failure at different dike sections
are likely to occur during the same extreme load event. Furthermore, correlations
in space and between different parameters are present within the subsoil
characteristics. Neglecting these correlations results in rather high
estimations of the failure probabilities. 
The aim of this thesis is to investigate how correlations affect the
reliability assessment of a dike trajectory. To achieve this, an integral, full
probabilistic model is developed that enables simultaneous assessment of dike
sections and failure mechanisms while accounting for uncertainties and
(spatial) correlations within the model input. The model is based on Monte
Carlo simulation. The failure probability of a dike trajectory is computed by
counting failure if one or more limit state function 푍푗,푘 for failure mechanism 푗 of dike
section 푘
returns a negative realisation. Correlations between the model input parameters
are provided by means of a Gaussian copula. A particular aspect of the model is
the implementation of metamodeling for the assessment of macrostability. This
failure mechanism cannot be described by an analytical limit state function
that is easily implemented in the Monte Carlo framework. Therefore, metamodels
are created by means of Gaussian process regression. This method makes it
possible to assess macrostability within an integral, full-probabilistic framework
that is able to include interdependencies between e.g. macrostability and
piping, within acceptable computation costs. The model is applied to a case
study of dike trajectory 43-4, which is located along the Waal between Sprok
and Sterreschans, in the east of the Netherlands.  The effects of different plausible
correlations have been studied. This research shows that including certain
correlations can significantly reduce the assessed failure probabilities, by a
factor ten or more in some situations. However, the impact of correlation
strongly depends on the situation. The most significant reduction can be
achieved for cases in which (1) parameters that play a dominant role in failure
of the corresponding mechanism are correlated; (2) the failure probabilities of
the different elements are similar, i.e. for a flood defence where failure is
not dominated by one dike section or one failure mechanism; and (3) the failure
probabilities of the corresponding elements are smaller than approximately
10−3. The model forms a solid, flexible basis that can easily be adapted or
extended to improve the understanding about interactions between failure
mechanisms, even though some aspects are simplified or neglected. All in all,
the conservatism in current safety assessments can be partly solved by
considering the interdependencies between mechanisms and dike sections and by
approaching a dike trajectory as an integral system.