Entegre güçlendirilmiş yapısal paneller için yapay sinir ağı bazlı burkulma yükleri belirleme aracı

The sudden change in the load carrying capacity under compresive loading, called buckling, may cause catastrophic failures. Therefore, determination of the first buckling and collapse loads of structural elements is essential in preliminary design stages. Finite element (FE) analyses and structural testing are used to determine buckling characteristics of a structural element. However, in early design stages, FE analyses are time consuming and structural testing is costly. In this study, an artificial neural network tool (ANN) is used to reduce computational effort to determine buckling loads of integrally stiffened structural panels in early design stages. Reuslts of FE analyses are employed to train the ANN. Moreover, Latin Hypercube Sampling (LHS) methodology is used to reduce the number of required FE analyses to generate database that artificial neural network is based on. Finally, a Multi-fidelity sampling algorithm that uses FE models with different mesh resolutions is implemented for generation of the ANN database in order to reduce computational time spent for finite element analyses. Mean errors and fit performance model results are compared to determine accuracy of the neural network results.