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Bruno Sudret  - - - 
Top co-authors See all
Irena Hajnsek

170 shared publications

Fabrice Mortessagne

138 shared publications

O. Picon

120 shared publications

A. Younes

110 shared publications

Joe Wiart

105 shared publications

124
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Publication Record
Distribution of Articles published per year 
(1970 - 2018)
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Publications See all
Article 0 Reads 0 Citations Development of Probabilistic Dam Breach Model Using Bayesian Inference S. J. Peter, Annunziato Siviglia, Joseph Benjamin Nagel, Ste... Published: 04 July 2018
Water Resources Research, doi: 10.1029/2017wr021176
DOI See at publisher website
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Dam breach models are commonly used to predict outflow hydrographs of potentially failing dams and are key ingredients for evaluating flood risk. In this paper a new dam breach modeling framework is introduced that shall improve the reliability of hydrograph predictions of homogeneous earthen embankment dams. Striving for a small number of parameters, the simplified physics‐based model describes the processes of failing embankment dams by breach enlargement, driven by progressive surface erosion. Therein the erosion rate of dam material is modeled by empirical sediment transport formulations. Embedding the model into a Bayesian multilevel framework allows for quantitative analysis of different categories of uncertainties. To this end, data available in literature of observed peak discharge and final breach width of historical dam failures was used to perform model inversion by applying Markov Chain Monte Carlo simulation. Prior knowledge is mainly based on non‐informative distribution functions. The resulting posterior distribution shows that the main source of uncertainty is a correlated subset of parameters, consisting of the residual error term and the epistemic term quantifying the breach erosion rate. The prediction intervals of peak discharge and final breach width are congruent with values known from literature. To finally predict the outflow hydrograph for real case applications, an alternative residual model was formulated that assumes perfect data and a perfect model. The fully probabilistic fashion of hydrograph prediction has the potential to improve the adequate risk management of downstream flooding.
Article 0 Reads 2 Citations Analyzing natural convection in porous enclosure with polynomial chaos expansions: Effect of thermal dispersion, anisotr... Noura Fajraoui, Marwan Fahs, Anis Younes, Bruno Sudret Published: 01 December 2017
International Journal of Heat and Mass Transfer, doi: 10.1016/j.ijheatmasstransfer.2017.07.003
DOI See at publisher website
PREPRINT 0 Reads 0 Citations Uncertainty quantification in urban drainage simulation: fast surrogates for sensitivity analysis and model calibration Joseph B. Nagel, Jörg Rieckermann, Bruno Sudret Published: 11 September 2017
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This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sensitivity measures such as the Sobol indices are obtained directly from the expansion coefficients. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.
PREPRINT 0 Reads 0 Citations An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability a... Stefano Marelli, Bruno Sudret Published: 05 September 2017
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Polynomial chaos expansions (PCE) have seen widespread use in the context of uncertainty quantification. However, their application to structural reliability problems has been hindered by the limited performance of PCE in the tails of the model response and due to the lack of local metamodel error estimates. We propose a new method to provide local metamodel error estimates based on bootstrap resampling and sparse PCE. An initial experimental design is iteratively updated based on the current estimation of the limit-state surface in an active learning algorithm. The greedy algorithm uses the bootstrap-based local error estimates for the polynomial chaos predictor to identify the best candidate set of points to enrich the experimental design. We demonstrate the effectiveness of this approach on a well-known analytical benchmark representing a series system, on a truss structure and on a complex realistic slope-stability problem.
Article 0 Reads 0 Citations Editorial: Special Issue of ESREL 2015 Luca Podofillini, Bruno Sudret, Božidar Stojadinović, Enrico... Published: 01 August 2017
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, doi: 10.1177/1748006X17724236
DOI See at publisher website
Article 0 Reads 4 Citations Rare Event Estimation Using Polynomial-Chaos Kriging R. Schöbi, B. Sudret, Stefano Marelli Published: 01 June 2017
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, doi: 10.1061/ajrua6.0000870
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Structural reliability analysis aims at computing the probability of failure of systems whose performance may be assessed by using complex computational models (e.g., expensive-to-run finite-element models). A direct use of Monte Carlo simulation is not feasible in practice, unless a surrogate model (such as kriging, also known as Gaussian process modeling) is used. Such metamodels are often used in conjunction with adaptive experimental designs (i.e., design enrichment strategies), which allows one to iteratively increase the accuracy of the surrogate for the estimation of the failure probability while keeping low the overall number of runs of the costly original model. This paper develops a new structural reliability method based on the recently developed polynomial-chaos kriging (PC-kriging) approach coupled with an active learning algorithm known as adaptive kriging Monte Carlo simulation (AK-MCS). The problem is formulated in such a way that the computation of both small probabilities of failure and extreme quantiles is unified. Different convergence criteria for both types of analyses are discussed, and in particular the original AK-MCS stopping criterion is shown to be overconservative. A multipoint enrichment algorithm is elaborated, which allows the addition of several points in each iteration, thus fully exploiting high-performance computing architectures. The proposed method is illustrated on three examples, namely a two-dimensional case that allows underlining of the advantages of this approach compared to standard AK-MCS. Then the quantiles of the eight-dimensional borehole function are estimated. Finally the reliability of a truss structure (10 random variables) is addressed. In all cases, accurate results are obtained with approximately 100 runs of the original model.