基于代理模型的输电塔半刚性节点弯矩-转角曲线预测方法OA北大核心CSTPCD
A surrogate model-based prediction method of moment-rotation curve of semi-rigid joints in transmission towers
为了准确、高效地评估输电塔半刚性节点的力学特性,提出了一种基于代理模型的输电塔半刚性节点弯矩-转角曲线预测方法,通过引入代理模型方法近似半刚性节点几何尺寸与极限抗弯承载力、初始转动刚度之间的函数关系,建立具有较高精度的预测模型,进而结合Kish-Chen幂函数模型拟合输电塔半刚性节点的弯矩-转角曲线.结果表明,提出的基于代理模型的输电塔半刚性节点弯矩-转角曲线预测方法能减少实验和数值模拟的成本,较好地模拟输电塔半刚性节点实际受力-变形情况,为输电塔半刚性节点的工程设计和理论研究提供了参考.
In order to accurately and efficiently evaluate the mechanical properties of semi-rigid joints in transmission towers,a method based on surrogate model is proposed to predict the moment-rotation relationship of the semi-rigid joints.By introducing the surrogate model method to approximate the functional relationship between the geometric dimensions,ultimate flexural capacity,and initial rotational stiffness of semi-rigid joints,a prediction model with high accuracy is established.Furthermore,the moment-rotation curves of semi-rigid joints in transmission towers are fitted using the Kish-Chen power function model.The results show that the proposed surrogate model-based prediction method for moment-rotation curves of semi-rigid joints can reduce the cost of experiments and numerical simulations,while accruately approximating the actual force-deformation relationship of semi-rigid joints in transmission towers.This method also provides valuable insights for the engineering design and theoretical research of semi-rigid joints in transmission towers.
刘泉;李正良;彭思思;王涛
国网经济技术研究院有限公司,北京 102209重庆大学 土木工程学院,重庆 400045重庆大学 土木工程学院,重庆 400045||哈尔滨工业大学 交通科学与工程学院,哈尔滨 150090
动力与电气工程
代理模型输电塔半刚性节点弯矩-转角曲线
surrogate modeltransmission towerssemi-rigid jointsmoment-rotation curves
《重庆大学学报》 2024 (004)
86-93 / 8
国家自然科学基金国际(地区)合作与交流项目(51611140123);国家重点研发计划项目(2017YFC0703901,2018YFC0809406);重庆市博士后研究项目特别资助(2022CQBSHBT3009).Supported by International(Regional)Cooperation and Exchange Program of the National Natural Science Founda-tion of China(51611140123),National Key R&D Plan Project(2017YFC0703901,2018YFC0809406)and Special Support of Chongqing Postdoctoral Research Project(2022CQBSHBT3009).
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