Optimization of space structures by neural dynamics
Neural Networks
Learning improvement of neural networks used in structural optimization
Advances in Engineering Software
Design and robust optimal control of smart beams with application on vibrations suppression
Advances in Engineering Software - Selected papers from civil-comp 2003 and AlCivil-comp 2003
Nonparametric multivariate density estimation: a comparative study
IEEE Transactions on Signal Processing
Simulating the seismic response of embankments via artificial neural networks
Advances in Engineering Software
Computationally efficient seismic fragility analysis of geostructures
Computers and Structures
Soil-structure interaction: Parameters identification using particle swarm optimization
Computers and Structures
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The objective of this paper is to investigate the efficiency of soft computing methods, in particular methodologies based on neural networks, when incorporated into the solution of computationally intensive engineering problems. Two types of applications have been considered, namely parameter (flaw) identification and probabilistic seismic analysis of structures. Artificial neural networks (ANNs) based metamodels are used in order to replace the time-consuming repeated structural analyses. The back-propagation algorithm is employed for training the ANN, using data derived from selected analyses. The trained ANN is then used to predict the values of the necessary data. The numerical tests demonstrate the computational advantages of the proposed methodologies.