Abstract Reliability-based structural optimization using

paper examines the application of neural networks (NN) to reliability-based structural optimization of largescale structural systems. The failure of the structural system is associated with the plastic collapse. The optimization part is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation (MCS) method incorporating the importance sampling technique for the reduction of the sample size. In this study two methodologies are examined. In the first one an NN is trained to perform both the deterministic and probabilistic constraints check. In the second one only the elasto-plastic analysis phase, required by the MCS, is replaced by a neural network prediction of the structural behaviour up to collapse. The use of NN is motivated by the approximate concepts inherent in reliability analysis and the time consuming repeated analyses required by MCS. Ó 2002 Elsevier Science B.V. All rights reserved

Elastic Properties of Nonstoichiometric Reacted PDMS Networks

The influence of stoichiometry on the elastic modulus of eight-functional end-linked poly(dimethylsiloxane) (PDMS) networks was investigated by extensional rheometry with extensions up to more than 100%, and the stress-strain relation was found to be almost linear-a characteristic property for a network structure with an eight-functional cross-linker. The experimental data were compared to a stochastic model taking into account entanglements and to Monte Carlo simulations. The Mooney-Rivlin model was furthermore used to fit the data, and the dependency of C-1 and C-2 parameters on the stoichiometric ratio was investigated in order to clarify especially the influence of trapped entanglements acting either as chemical cross-links or as sliding links. It was found that including a locking factor dividing trapped entanglements into locked entanglements and slip-links could explain our data obtained for the Mooney-Rivlin constants. It was furthermore found that trapped entanglements dominate when there is an excess of cross-linker, ensuring that all long difunctional DMS chains are bound to the infinite network in both ends