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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Hoffbauer, Ln
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Topics
Publications (9/9 displayed)
- 2018RESEARCH AND VALIDATION OF GLOBAL MPP IN THE RELIABILITY ANALYSIS OF COMPOSITE STRUCTURES
- 2018Bayesian inference in validation of global MPP for the reliability analysis of composite structurescitations
- 2017A RBDO APPROACH FOR THE RELIABILITY ASSESSMENT OF COMPOSITE STRUCTURES
- 2017Reliability-based design optimization and uncertainty quantification for optimal conditions of composite structures with non-linear behaviorcitations
- 2013Uncertainty assessment approach for composite structures based on global sensitivity indicescitations
- 2012A variability study on the response of composite structures based on sensitivity indices
- 2010Reliability Assessment of Composite Structures with Multiple Failure Modes
- 2010Uncertainty propagation in inverse reliability-based design of composite structurescitations
- 2008From local to global importance measures of uncertainty propagation in composite structurescitations
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article
Uncertainty assessment approach for composite structures based on global sensitivity indices
Abstract
The problem of uncertainty propagation in composite laminate structures is studied. An approach based on the optimal design of composite structures to achieve a target reliability level is proposed. Using the Uniform Design Method (UDM), a set of design points is generated over a design domain centred at mean values of random variables, aimed at studying the space variability. The most critical Tsai number, the structural reliability index and the sensitivities are obtained for each UDM design point, using the maximum load obtained from optimal design search. Using the UDM design points as input/output patterns, an Artificial Neural Network (ANN) is developed based on supervised evolutionary learning. Finally, using the developed ANN a Monte Carlo simulation procedure is implemented and the variability of the structural response based on global sensitivity analysis (GSA) is studied. The GSA is based on the first order Sobol indices and relative sensitivities. An appropriate GSA algorithm aiming to obtain Sobol indices is proposed. The most important sources of uncertainty are identified.