Most of the standards available for assessment failure pressure corroded pipelines are limited in their ability to assess complex loadings, and estimations conservative. To overcome this research gap, study employed an artificial neural network (ANN) model trained with data obtained using finite element method (FEM) develop equation predict a pipeline single corrosion defect. A analysis (FEA) m...

Conventional pipeline failure pressure assessment codes do not allow for prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis the API 5L X52, X65, and X80 corroded pipes with a longitudinal corrosion defect loadings is p...

abstract in this research, a radial basis function artificial neural network (rbf-ann) model was developed to predict the hot deformation flow curves of api x65 pipeline steel. the results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of zener-hollomon parameter and a third order polynomial functio...

Abstract In this research, a radial basis function artificial neural network (RBF-ANN) model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial functio...