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

Kingston, G.; Rajabalinejad, Mohammad; Gouldby, Ben; Gelder, Pieter van

doi:10.1016/j.strusafe.2010.08.002

Cited by 30 publications

(22 citation statements)

References 22 publications

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“…This yields a random set of erosion profiles from which the failure probabilities in each location for both the road and grass covered dike are derived for each storm condition (q m ). Note that the use of emulators allowed us to reduce the computational times so that crude Monte Carlo simulations could be used that captured the high level of detail of the hydrodynamics instead of using approximate reliability methods such as first order reliability method (FORM) or second order reliability method (SORM) [31].…”

Section: Methodsmentioning

confidence: 99%

“…Hydrodynamic model emulation has proven to be a powerful tool for water level predictions while improving the calculation speed significantly [29,30]. Also, for the reliability assessment of flood defences, emulation has already been implemented for other failure mechanisms, such as backward piping erosion [31][32][33]. For wave overtopping, [34] used the results of 10,000 physical model tests of different types of coastal defences to train a neural network.…”

Section: Introductionmentioning

confidence: 99%

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Failure of Grass Covered Flood Defences with Roads on Top Due to Wave Overtopping: A Probabilistic Assessment Method

Lopez

Warmink²,

Bomers³

et al. 2018

JMSE

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Abstract:Hard structures, i.e., roads, are commonly found over flood defences, such as dikes, in order to ensure access and connectivity between flood protected areas. Several climate change future scenario studies have concluded that flood defences will be required to withstand more severe storms than the ones used for their original design. Therefore, this paper presents a probabilistic methodology to assess the effect of a road on top of a dike: it gives the failure probability of the grass cover due to wave overtopping over a wide range of design storms. The methodology was developed by building two different dike configurations in computational fluid dynamics Navier-Stokes solution software; one with a road on top and one without a road. Both models were validated with experimental data collected from field-scale experiments. Later, both models were used to produce data sets for training simpler and faster emulators. These emulators were coupled to a simplified erosion model which allowed testing storm scenarios which resulted in local scouring conditioned statistical failure probabilities. From these results it was estimated that the dike with a road has higher probabilities (5 × 10 −5 > P f >1 × 10 −4 ) of failure than a dike without a road (P f < 1 × 10 −6 ) if realistic grass quality spatial distributions were assumed. The coupled emulator-erosion model was able to yield realistic probabilities, given all the uncertainties in the modelling process and it seems to be a promising tool for quantifying grass cover erosion failure.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Failure of Grass Covered Flood Defences with Roads on Top Due to Wave Overtopping: A Probabilistic Assessment Method

Lopez

Warmink²,

Bomers³

et al. 2018

JMSE

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show abstract

“…Response Surface methods can then be employed [9] to reduce the computational burden and implement the analysis. An example of this type of implementation in the context of flood defense is described in [11].…”

Section: Dike Reliability Analysismentioning

confidence: 99%

Flood early warning system: design, implementation and computational modules

Krzhizhanovskaya¹,

Shirshov²,

Melnikova³

et al. 2011

Procedia Computer Science

137

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We present a prototype of the flood early warning system (EWS) developed within the UrbanFlood FP7 project. The system monitors sensor networks installed in flood defenses (dikes, dams, embankments, etc.), detects sensor signal abnormalities, calculates dike failure probability, and simulates possible scenarios of dike breaching and flood propagation. All the relevant information and simulation results are fed into an interactive decision support system that helps dike managers and city authorities to make informed decisions in case of emergency and in routine dike quality assessment. In addition to that, a Virtual Dike computational module has been developed for advanced research into dike stability and failure mechanisms, and for training the artificial intelligence module on signal parameters induced by dike instabilities. This paper describes the UrbanFlood EWS generic design and functionality, the computational workflow, the individual modules, their integration via the Common Information Space middleware, and the first results of EWS monitoring and performance benchmarks.

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“…Despite the significant reduction of the computational time, the method is still time consuming and, for this reason, several variance reduction techniques, such as importance sampling or directional simulation, have been widely applied. An extensive description, as well as, numerical examples of the use of ANN-based MCS in structural reliability can be found in ( [31], [32], [27], [33], [34]). …”

Section: Ann-based Methods Of Failure Probability Computationmentioning

confidence: 99%

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

et al. 2015

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

Cited by 30 publications

References 22 publications

Failure of Grass Covered Flood Defences with Roads on Top Due to Wave Overtopping: A Probabilistic Assessment Method

Failure of Grass Covered Flood Defences with Roads on Top Due to Wave Overtopping: A Probabilistic Assessment Method

Flood early warning system: design, implementation and computational modules

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

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