为提升chirplet变换(chirplet transform,CT)估算瞬时频率的精度,在CT基础上结合花斑翠鸟优化(pied kingfisher optimizer,PKO)和径向基移动最小二乘(radial basis function moving least squares,RBFMLS)算法提出了一种识别结构瞬时频...为提升chirplet变换(chirplet transform,CT)估算瞬时频率的精度,在CT基础上结合花斑翠鸟优化(pied kingfisher optimizer,PKO)和径向基移动最小二乘(radial basis function moving least squares,RBFMLS)算法提出了一种识别结构瞬时频率的新方法。该方法采用正定紧支径向基函数作为移动最小二乘近似的权函数,对CT的能量脊线进行估算,同时应用PKO对RBFMLS节点支撑半径和CT窗函数宽度进行优化。通过一组解析信号数值算例和一个时变拉索试验验证了所提方法的有效性。研究结果表明,该方法能有效改善信号分析的能量聚集性,提高瞬时频率的识别精度。展开更多
Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to ...Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.展开更多
文摘为提升chirplet变换(chirplet transform,CT)估算瞬时频率的精度,在CT基础上结合花斑翠鸟优化(pied kingfisher optimizer,PKO)和径向基移动最小二乘(radial basis function moving least squares,RBFMLS)算法提出了一种识别结构瞬时频率的新方法。该方法采用正定紧支径向基函数作为移动最小二乘近似的权函数,对CT的能量脊线进行估算,同时应用PKO对RBFMLS节点支撑半径和CT窗函数宽度进行优化。通过一组解析信号数值算例和一个时变拉索试验验证了所提方法的有效性。研究结果表明,该方法能有效改善信号分析的能量聚集性,提高瞬时频率的识别精度。
基金funded by United Arab Emirates University(UAEU)under the UAEU-AUA grant number G00004577(12N145)with the corresponding grant at Universiti Malaya(UM)under grant number IF019-2024.
文摘Concrete-filled steel tubes(CFST)are widely utilized in civil engineering due to their superior load-bearing capacity,ductility,and seismic resistance.However,existing design codes,such as AISC and Eurocode 4,tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core.To address this limitation,this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer(PKO),a nature-inspired algorithm,to enhance the accuracy of shear strength prediction for CFST columns.Additionally,quantile regression is employed to construct prediction intervals for the ultimate shear force,while the Asymmetric Squared Error Loss(ASEL)function is incorporated to mitigate overestimation errors.The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy,achieving a Mean Absolute Percentage Error(MAPE)of 4.431%and R2 of 0.9925 on the test set.Furthermore,the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%,with negligible impact on predictive performance.Additionally,based on the Genetic Algorithm(GA)and existing equation models,a strength equation model is developed,achieving markedly higher accuracy than existing models(R^(2)=0.934).Lastly,web-based Graphical User Interfaces(GUIs)were developed to enable real-time prediction.
