利用GRNN方法分析地下管道抗震性能影响因素的敏感性
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摘要
通过对地震动作用下地下管道最大轴向应力和多种影响因素的分析,基于广义回归神经网络(GRNN)建立了地下管道地震反应预测模型。在此基础之上,将正交表试验设计理论、效用函数理论与神经网络结合起来对地下管道抗震性能影响因素进行敏感性分析,并给出了敏感性分析的具体算法。最后,通过实例分析,将分析结果与用传统理论分析所得结果进行比较,结果显示,该方法不仅可靠,而且简单方便。
Through the analysis of the maximum axial stress under the action of seismic ground motion and the affecting factors,the prediction model of underground pipeline response under earthquakes was constructed by using GRNN.On the basis of the model,combining the orthogonal array with the theory of utility function and ANN,the assessment model was set up to make sensibility analysis.The authors made a comparison between the results obtained from the traditional limit balance analysis and the results from the proposed method.It is shown that the results from the proposed method are valid and convenient.
Through the analysis of the maximum axial stress under the action of seismic ground motion and the affecting factors,the prediction model of underground pipeline response under earthquakes was constructed by using GRNN.On the basis of the model,combining the orthogonal array with the theory of utility function and ANN,the assessment model was set up to make sensibility analysis.The authors made a comparison between the results obtained from the traditional limit balance analysis and the results from the proposed method.It is shown that the results from the proposed method are valid and convenient.
引文
[1]郭恩栋,余世舟,吴伟.地下管道工程震害分析[J].地震工程与工程振动,2006,26(3):180-183.
[2]吴少艳,吴德军,陈东.地震对市政管道的破坏及管道的抗震处理[J].地震工程与工程振动,2006,26(3):200-202.
[3]张艳红,杜修力.结构可靠度对系统参数敏感性分析的简便算法[J].同济大学学报,1996,24(4):475-480.
[4]邓民宪.埋地管道震害的多因素灰色关联分析[J].灾害学,1998,13(2):22-27.
[5]侯忠良.地下管线抗震[M].北京:学术书刊出版社,1990.
[6]黄强兵,彭建兵,杨天亮.埋地管道在地震波作用下的抗震性能分析[J].工程勘察,2004(3):64-67.
[7]Kamel EI Hmadi,et al.Soil spring for buried pipeline axial motion[J].Journal of Geotechnical Engineering,1998,11:1 335-1 339.
[8]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005.
[9]Specht D F.The general regression neural network-rediscovered[J].Neural Networks,1993,4:1033-1034.
[10]Molas G L,Yamazaki F.Neural networks for quick earthquake damage estimation[J].Earthquake Engineering and Structural Dynamics,1995,24:505-516.
[11]李昕,周晶,陈健云.基于神经网络方法的地下管道系统地震可靠性分析[J].世界地震工程,2001,17(4):107-111.
[12]夏元友,熊海丰.边坡稳定性影响因素敏感性人工神经网络分析[J].岩石力学与工程学报,2004,23(16):2703-2707.
[13]周锡元,马东辉,苏经宇,等.厦门市供水系统抗震易损性研究[R].北京:北京工业大学抗震减灾研究所,2005.
[2]吴少艳,吴德军,陈东.地震对市政管道的破坏及管道的抗震处理[J].地震工程与工程振动,2006,26(3):200-202.
[3]张艳红,杜修力.结构可靠度对系统参数敏感性分析的简便算法[J].同济大学学报,1996,24(4):475-480.
[4]邓民宪.埋地管道震害的多因素灰色关联分析[J].灾害学,1998,13(2):22-27.
[5]侯忠良.地下管线抗震[M].北京:学术书刊出版社,1990.
[6]黄强兵,彭建兵,杨天亮.埋地管道在地震波作用下的抗震性能分析[J].工程勘察,2004(3):64-67.
[7]Kamel EI Hmadi,et al.Soil spring for buried pipeline axial motion[J].Journal of Geotechnical Engineering,1998,11:1 335-1 339.
[8]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:电子工业出版社,2005.
[9]Specht D F.The general regression neural network-rediscovered[J].Neural Networks,1993,4:1033-1034.
[10]Molas G L,Yamazaki F.Neural networks for quick earthquake damage estimation[J].Earthquake Engineering and Structural Dynamics,1995,24:505-516.
[11]李昕,周晶,陈健云.基于神经网络方法的地下管道系统地震可靠性分析[J].世界地震工程,2001,17(4):107-111.
[12]夏元友,熊海丰.边坡稳定性影响因素敏感性人工神经网络分析[J].岩石力学与工程学报,2004,23(16):2703-2707.
[13]周锡元,马东辉,苏经宇,等.厦门市供水系统抗震易损性研究[R].北京:北京工业大学抗震减灾研究所,2005.
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