In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by p...In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design landscape.Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization.The neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates.Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly revised.Furthermore,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.展开更多
针对输入与输出之间高度非线性映射的发动机生产作业环境综合评价问题,文章应用误差反向传播(error back propagation,BP)人工神经网络构建综合评价模型。通过分析发动机生产作业环境的特点、主要影响因素及其危害,建立发动机生产作业...针对输入与输出之间高度非线性映射的发动机生产作业环境综合评价问题,文章应用误差反向传播(error back propagation,BP)人工神经网络构建综合评价模型。通过分析发动机生产作业环境的特点、主要影响因素及其危害,建立发动机生产作业环境评价指标体系,并确定每个单项指标的分级标准;将温度、湿度、气流速度、油雾、噪声以及照度6个指标作为模型输入,舒适度等级作为模型输出,建立3层BP神经网络模型;并应用贝叶斯正则化和动量梯度下降法较好地解决了传统BP人工神经网络训练高精度和预测低精度的过拟合现象。实验结果表明,基于该模型的评价结果符合实际情况,对作业环境改善具有指导意义。展开更多
基金Project(2022YFC3004304)supported by the National Key Research and Development Program of ChinaProjects(52078487,U1934207,52178180)supported by the National Natural Science Foundation of China+2 种基金Project(2022TJ-Y10)supported by the Hunan Province Science and Technology Talent Lifting Project,ChinaProject(2023QYJC006)supported by the Frontier Cross Research Project of Central South University,ChinaProject(SKL-IoTSC(UM)-2024-2026/ORP/GA08/2023)supported by the Science and Technology Development Fund and the State Key Laboratory of Internet of Things for Smart City(University of Macao),China。
文摘In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design landscape.Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization.The neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates.Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly revised.Furthermore,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.
文摘针对输入与输出之间高度非线性映射的发动机生产作业环境综合评价问题,文章应用误差反向传播(error back propagation,BP)人工神经网络构建综合评价模型。通过分析发动机生产作业环境的特点、主要影响因素及其危害,建立发动机生产作业环境评价指标体系,并确定每个单项指标的分级标准;将温度、湿度、气流速度、油雾、噪声以及照度6个指标作为模型输入,舒适度等级作为模型输出,建立3层BP神经网络模型;并应用贝叶斯正则化和动量梯度下降法较好地解决了传统BP人工神经网络训练高精度和预测低精度的过拟合现象。实验结果表明,基于该模型的评价结果符合实际情况,对作业环境改善具有指导意义。
