Computational intelligence methods for the efficient reliability analysis of complex flood defence structures

With the continual rise of sea levels and deterioration of flood defence structures over time, it is no longer appropriate to define a design level of flood protection, but rather, it is necessary to estimate the reliability of flood defences under varying and uncertain conditions. For complex geotechnical failure mechanisms, it is often necessary to employ computationally expensive finite element methods to analyse defence and soil behaviours; however, methods available for structural reliability analysis are generally not suitable for direct application to such models where the limit state function is only defined implicitly. In this study, an artificial neural network is used as a response surface function to efficiently emulate the complex finite element model within a Monte Carlo simulation. To ensure the successful and robust implementation of this approach, a genetic algorithm adaptive sampling method is designed and applied to focus sampling of the implicit limit state function towards the limit state region in which the accuracy of the estimated response is of the greatest importance to the estimated structural reliability. The accuracy and gains in computational efficiency obtainable using the proposed method are demonstrated when applied to the 17th Street Canal flood wall which catastrophically failed when Hurricane Katrina hit New Orleans in 2005.