TY - GEN A1 - Saoudi, Adel A1 - Fellah, Mamoun A1 - Hezil, Naouel A1 - Lerari, Djahida A1 - Khamouli, Farida A1 - Atoui, L'hadi A1 - Bachari, Khaldoun A1 - Morozova, Iuliia A1 - Obrosov, Aleksei A1 - Samad, Mohammed Abdul T1 - Prediction of mechanical properties of welded steel X70 pipeline using neural network modelling T2 - International Journal of Pressure Vessels and Piping N2 - An artificial neural network (ANN) model was developed to predict tensile and impact properties of a submerged arc helical welded (SAHW) pipeline steel API X70 based upon its chemical composition. Weight percent of the elements was considered as the input, while the tensile and Charpy impact properties were considered as the outputs. Scatter diagrams and two statistical parameters (absolute fraction of variance and relative error) were used to evaluate the prediction performance of the developed artificial neural network model. The predicted values were found to be in excellent agreement with the experimental data and the current model has a good learning precision and generalization (for training, validation and testing data sets). The results revealed that the developed model is very accurate and has a strong potential for capturing the interaction between the mechanical properties and chemical composition of welded high strength low alloy (HSLA) steels. KW - API X70 KW - Artificial neural network (ANN) KW - Submerged arc welding KW - Modeling KW - Mechanical properties KW - Chemical composition Y1 - 2020 U6 - https://doi.org/10.1016/j.ijpvp.2020.104153 SN - 0308-0161 VL - 186 ER -