Prediction of Axial Compression Bearing Capacity of RECFST Short Columns Based on Artificial Neural Network
In view of the limitations of relevant design specifications and literature formulas in calculating the axial compression bearing capacity of round-ended concrete-filled steel tubular(RECFST)short columns,a high-precision and wide-ranging axial compression bearing capacity prediction model was developed.Firstly,the axial compression test of RECFST short columns completed at home and abroad is investigated,and the finite element model which is strictly verified by the test data is established.Secondly,the finite element model is generated in batches based on Python scripts,and a data set covering a wide range of input parameters is established.Then,the data set is used to develop a high-precision ANN model and compare it with relevant specifications and formulas.Finally,a graphical user interface(GUI)tool is developed based on the ANN model for practical engineering applications.The average value of the ratio of the predicted value of the ANN model to the experimental result is NANN/Nu=0.98,and the prediction error of the model is much lower than that of the relevant specifications and formulas.The mean square error of the ANN model is KMSE=7.373 4 x 10-7,and the regression value of the total data sample is R=0.999 63,indicating the validity of the ANN model and the accuracy of the prediction results.The ANN model can accurately predict the axial compression bearing capacity of RECFST short columns.The GUI tool developed based on the model is simple and practical,which provides a new idea and method for studying the axial compression bearing capacity of RECFST short columns.
artificial neural networkRECFST short columnaxial bearing capacitygraphical user interface tools
NETL
NSTL
万方数据
