BP neural network method for damage recognition of steel beams in corrosive environment

Abstract

Steel beam is a kind of basic component widely used in machinery and civil engineering industry and its application has been widely studied home and abroad. In this paper, the neural network toolbox in MATLAB software was used to predict and analyze damage identification based on the changes of yield strength, elongation and tensile strength of steel beams with different thickness in accelerated corrosion experiments. The results show that, on the premise of selecting appropriate training samples, the BP neural network method had a great effect on the damage identification of steel beams, and its average error was about 3%, which could meet the requirements of the damage identification of steel beams in adverse environment.