径向基函数(RBF)神经网络在桥梁减震设计中的应用仿真

英文篇名：The Application of RBF Neural Network to Seismic Design of Bridges
中文刊名：系统仿真技术
英文刊名：System Simulation Technology
作者：叶爱君
英文作者：YE Aijun (State Key Laboratory for Disaster Reduction in Civil Engineering ; Tongji University ; Shanghai ; 200092)
中文关键词：径向基函数(RBF)神经网络 ; 桥梁结构 ; 流体粘滞阻尼器
英文关键词：RBF neural network ; bridge structure ; fluid viscous damper
出版日期：2006-01-28
机构：同济大学土木工程防灾国家重点实验室上海,200092
年：2006
期：01
出版单位：系统仿真技术

摘要

本文将人工智能理念引入桥梁结构的减震设计中,在对阻尼器参数进行参数分析的基础上,利用径向基函数(RBF)神经网络建立阻尼器参数与桥梁结构地震反应之间的映射关系,以帮助进行阻尼器参数的合理选定。同时,桥梁工程师可利用这一映射关系自行分析实际阻尼器性能参数的误差对结构地震反应的影响。本文最后通过工程实例,验证了人工神经网络算法在桥梁减震设计中应用的可能性和可靠性。
The artificial intelligent algorithm is introduced into seismic design of bridges. Based on the right amount parameter analysis for fluid viscous damper, the mapping relations between the damper parameters and seismic responses of a bridge are established using the artificial intelligent algorithm, and then the reasonable damper parameters can be determined. Further, bridge engineers may analyze the influence of actual damper parameter errors on the seismic responses of bridges by themselves using these mapping relations. Finally, the application possibility and the reliability of the artificial intelligent algorithm to seismic design of bridges have been confirmed through a bridge example.

引文

[1]徐利平,张喜刚等.苏通大桥主桥结构体系研究[A].见:苏通大桥建设指挥部编.苏通大桥论文集(第一辑)[C],中国科学技术出版社,2004.9.
    [2]Wen-Hsiung Lin,Anil K.Chopra.Earthquake response of elastic SDF systems with non-linear fluid viscous dampers[J].E.E.S.D.,2002,31:1623-1642.
    [3]Mahendra P.Singh,Navin.P.Verma,Luis M.Moreschi.Seismic analysis and design with Maxwell dampers[J],Journal of Engineering Mechanics,2003,29(3):273-282.
    [4]樊有维,郑东明.嵌岩桩极限承载力的径向基函数神经网络(RBF)预测[J],江苏建筑,2001,(2):56-58.
    [5]王炜,吴耿锋等.径向基函数(RBF)神经网络及其应用[J],地震,2005,25(2):19-25.
    [6]徐秉铮.神经网络理论与应用[M].广州:华南理工大学出版社,1994,304~322.