Bài báo khoa học

This study develops an artificial neural network (ANN) to estimate the critical buckling load (CBL) of corroded web-tapered steel I-section (WTSI) columns in pre-engineered steel buildings. A total of 387 datasets are employed to develop the ANN model. The datasets are generated from the proposed analytical model and Newton–Raphson method. The input parameters of the developed ANN model contain the cross-sectional dimensions of the steel column (i.e., the top and bottom flange width, top and bottom flange thickness, maximum section height, minimum section height, and web thickness), elastic modulus of material, and the column height. Meanwhile, the CBL is the output parameter of the ANN model. A predictive process for the CBL of the corroded WTSI columns has been proposed based on the ANN model and previous corrosion model. Results reveal that the ANN model showed an excellent performance