Juan Pablo Aguilar Lopez, Jord Warmink, Ralph Schielen, Suzanne Hulscher
Monday 29 june 2015
14:20 - 14:35h at Asia (level 0)
Themes: (T) Special session, (ST) Flood defences and flood risk: hydraulic engineering aspects
Parallel session: 2E. Special session: Flood Risk and Flood defences
Reliability of flood defences is one of the main concerns of water managers in low land countries such as the Netherlands. Population growth, economic development and climate change are main drivers for the development of solutions such as multifunctional flood defences (MFFD). This type of structure combines the primary flood defence function with additional ones such as commercial, residential and recreational. Failure mechanisms of mono functional flood defenses have been studied for a long time in the Netherlands, in order to estimate more accurately the reliability of their flood defense system. MFFD’s will probably be exposed to these same failure mechanisms but their occurrence might also be triggered by the effect of complementary functions embedded in the defence body. The present study aims to develop a methodology which allows to consider the effect of structure embedment in the occurrence of piping erosion failure mechanism. In particular for the eventual embedment of a sewer pipe underneath a flood defence. The method consisted in modelling via finite element a flood defence with and without a sewer pipe embedded underneath. In order to consider the erosion progression for different water level and hydraulic conductivities, a simplified method for solving the aquifer flow was implemented. From the results obtained, two artificial neural network emulators where trained and validated. As a final step the emulators where used for failure estimation by several Monte Carlo runs. The main results from the study show that the embedment of and additional structure will change the failure probability significantly (by a factor of 8 in the present study) and that emulators are capable of representing the highly nonlinear behavior of complex models without significantly compromising the calculation accuracy.