Research on Prediction Method of Residual Strength Index of CFST Columns at High Temperature Based on Machine Learning
The purpose of this study is improving calculation accuracy and speed,machine learning models were employed to generalize data for predicting the residual strength index of CFST columns under high temperatures.A Generative Adversarial Network was used to generalize and generate 407 datasets from the 110 collected experimental results.These were then used to train machine learning models,with the experimental results used to evaluate their performance and determine the optimal model.The generated data were input into the established model to predict the high-temperature residual strength index of CFST columns,and the results were compared with existing calculation methods.Comparative analysis with available experimental results showed that the Random Forest model had the best performance in terms of metrics,achieving a goodness-of-fit of 0.947 7,a mean squared error of 0.001 8,and an accuracy of 94.7%.The prediction error for 83%of the data was within the±10%range,and for 100%of the data,it was within the±20%range.The main influencing factors for the residual strength index,in order of importance,were temperature,steel yield strength,concrete compressive strength,and cross-sectional area,with steel tube thickness having a minimal impact.The findings demonstrate that the proposed prediction method outperforms existing calculation methods,offering faster computation speed,smaller result errors,and stronger model interpretability.This method can provide a reference for fire-resistant design of CFST columns.
万方数据
维普
