Engineering computation under uncertainty – Capabilities of non-traditional models

Möller, Bernd; Beer, Michael

doi:10.1016/j.compstruc.2007.05.041

Cited by 225 publications

(100 citation statements)

References 54 publications

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“…If the available information for the risk assessment is very limited in similar projects, probabilistic models, interval models, and imprecise probabilities should be considered [43][44][45][46][47]. Specifically, when all parameters are collected as random variables, probabilistic models are proposed; for cases in which all parameters are collected as interval variables, interval models have behaved with some favourable features; when parameters are described by random variables and interval parameters, respectively, imprecise probabilities with components of fuzzy set theory can be quite helpful.…”

Section: Resultsmentioning

confidence: 99%

Risk Assessment of Rockfall Hazards in a Tunnel Portal Section Based on Normal Cloud Model

Wang¹,

Li²,

Ma³

et al. 2017

Pol. J. Environ. Stud.

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Section: Resultsmentioning

confidence: 99%

Risk Assessment of Rockfall Hazards in a Tunnel Portal Section Based on Normal Cloud Model

Wang¹,

Li²,

Ma³

et al. 2017

Pol. J. Environ. Stud.

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“…Beyer and Sendhoff (2007). This paper specifically considers non-cognitive sources of uncertainty or aleatory uncertainty (Haldar and Mahadevan 2000;Möller and Beer 2008). These sources of uncertainty are of physical nature.…”

Section: Introductionmentioning

confidence: 99%

Sequential robust optimization of a V-bending process using numerical simulations

Wiebenga

Boogaard

Klaseboer

2012

Struct Multidisc Optim

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The coupling of finite element simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a generally applicable strategy for modeling and efficiently solving robust optimization problems based on time consuming simulations. Noise variables and their effect on the responses are taken into account explicitly. The robust optimization strategy consists of four main stages: modeling, sensitivity analysis, robust optimization and sequential robust optimization. Use is made of a metamodel-based optimization approach to couple the computationally expensive finite element simulations with the robust optimization procedure. The initial metamodel approximation will only serve to find a first estimate of the robust optimum. Sequential optimization steps are subsequently applied to efficiently increase the accuracy of the response prediction at regions of interest containing the optimal robust design. The applicability of the proposed robust optimization strategy is demonstrated by the sequential robust optimization of an analytical test function and an industrial V-bending process. For the industrial application, several production trial runs have been performed to investigate and validate the robustness of the production process. For both applications, it is shown that the robust optimization strategy accounts for the effect of different sources of uncertainty onto the process responses in a very efficient manner. Moreover, application of the methodology to the industrial V-bending process results in valuable process insights and an improved robust process design.

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Section: Introductionmentioning

confidence: 99%

“…In general, all time-dependent influences of a structure are uncertain processes which lead to uncertain time-varying structural responses. Uncertain processes can be captured with nontraditional uncertainty models, see Möller and Beer (2008).For robust design of structures, numerical methods are required which can be used to identify and predict time-dependent material behavior. In this paper, a novel method for the numerical prediction of time-dependent structural responses under consideration of uncertain action processes is proposed, which combines neural computing (artificial neural networks, see e.g.…”

mentioning

confidence: 99%

Identification and Prediction of Time-Dependent Structural Behavior with Recurrent Neural Networks for Uncertain Data

Freitag¹,

Graf²,

Kaliske³

2010

Proceedings of the 4th International Workshop on Reliable Engineering Computing Robust Design – Coping With Hazards, Risk and U

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The long-term behavior of Civil Engineering structures depends on a variety of environmental influences such as applied loadings, temperature and weathering. In general, all time-dependent influences of a structure are uncertain processes which lead to uncertain time-varying structural responses. Uncertain processes can be captured with nontraditional uncertainty models, see Möller and Beer (2008).For robust design of structures, numerical methods are required which can be used to identify and predict time-dependent material behavior. In this paper, a novel method for the numerical prediction of time-dependent structural responses under consideration of uncertain action processes is proposed, which combines neural computing (artificial neural networks, see e.g. Haykin (1999)) and mapping of fuzzy data (fuzzy analysis, see e.g. Möller et al. (2000)).Different types of mapping fuzzy processes with recurrent neural networks are introduced. Prediction and training algorithms for the mapping of fuzzy input onto fuzzy output values are described. Thereby, fuzzy network parameters can be considered. Beside fuzzy values, also intervals and deterministic numbers may be processed.The developed recurrent neural network approach for fuzzy data is verified with a fractional rheological material model, see Oeser and Freitag (2009). The new approach is applied to the prediction of the long-term behavior of textile reinforced concrete structures.

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Engineering computation under uncertainty – Capabilities of non-traditional models

Cited by 225 publications

References 54 publications

Risk Assessment of Rockfall Hazards in a Tunnel Portal Section Based on Normal Cloud Model

Risk Assessment of Rockfall Hazards in a Tunnel Portal Section Based on Normal Cloud Model

Sequential robust optimization of a V-bending process using numerical simulations

Identification and Prediction of Time-Dependent Structural Behavior with Recurrent Neural Networks for Uncertain Data

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