基于ANN的RECFST短柱轴压承载力预测  

Prediction of Axial Compression Bearing Capacity of RECFST Short Columns Based on Artificial Neural Network

作　　者：杜运兴[1] 刁俊杰 DU Yunxing;DIAO Junjie(College of Civil Engineering,Hunan University,Changsha,China,410082)

机构地区：[1]湖南大学土木工程学院,湖南长沙410082

出　　处：《沈阳建筑大学学报（自然科学版）》2024年第3期414-422,共9页Journal of Shenyang Jianzhu University：Natural Science

基　　金：国家自然科学基金项目(52178206)。

摘　　要：目的针对相关设计规范和文献在计算圆端形截面钢管混凝土短柱轴压承载力上的局限性,开发高精高效的轴压承载力预测模型。方法首先,基于国内外已有的RECFST短柱轴压试验研究结果建立有限元模型,并通过验证;其次,基于Python脚本批量生成有限元模型,建立涵盖广泛输入参数的数据集;然后,利用数据集开发高精度的ANN模型并与相关规范和文献结果进行比较;最后,基于ANN模型开发GUI图形用户界面工具。结果ANN模型预测值与试验结果之比的平均值N ANN/N u=0.98,模型预测误差远低于相关规范和文献公式预测误差;ANN模型的均方误差K MSE=7.3734×10-7,总数据样本回归值R=0.99963,表明了ANN模型的有效性以及预测结果的精确性。结论ANN模型可以准确预测RECFST短柱的轴压承载力,基于模型开发的GUI工具简便实用。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 N ANN/N u=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 K MSE=7.3734×10-7,and the regression value of the total data sample is R=0.99963,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.

关 键 词：ANN RECFST短柱 轴压承载力 图形用户界面工具 

分 类 号：TU398.9[建筑科学—结构工程] TP183[自动化与计算机技术—控制理论与控制工程]