Atmospheric Corrosion Prediction Method for Steels Based on Batched Graph Neural Network
The accuracy of the atmospheric corrosion prediction model for carbon steel in our current standards is low,and it is urgent to propose a more accurate prediction model.Aiming at various types of steel in typical environmental regions of China,the corrosion data obtained from 10 atmospheric exposure test stations are collected.The atmospheric corrosion dataset is constructed,and an atmospheric corrosion prediction method for steel structures based on the batched graph neural network(BGNN)is proposed.The method associates data points with similar features to form a structured dataset,generates a structured sampling dataset and a residual feature dataset based on the structured dataset through random sampling.Trains a pre-constructed basic graph neural network(GNN)regressor,and integrates a number of regressors to obtain a BGNN model.Different methods are used to predict the atmospheric corrosion dataset of steel in China and compared.The results show that,the formula in current standards has the largest prediction error.SVM and LSTM have better prediction effect.BGNN has the lowest prediction error and the best prediction effect.Compared with single GNN,BGNN has smaller errors and is relatively insensitive to the selection of hyperparameters.The BGNN model is capable of sufficiently considering the similarity between different regions,different steels and different climatic conditions,and enhancing the ability to analyze the correlated data,which results in a clearer attribution analysis of the new prediction targets and a higher prediction accuracy.
万方数据
维普
