基于BP神经网络模型的多层砖房震害预测方法
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摘要
针对传统的基于地震烈度的建筑物震害预测方法的不足,本文以地震动峰值加速度作为建筑物震害预测的地震动指标,结合几次大地震中多层砖房的震害实例,提出了一种基于BP神经网络模型的建筑物震害预测方法,模型的输入为反映结构抗震性能的各类物理参数,输出为给定地震动峰值加速度下建筑物破坏状态的概率。研究表明:基于BP网络模型的多层砖房的震害预测结果与震害实例的实际情况比较吻合,本文的思路和方法可推广于其他不同类型的建筑结构的震害预测。
The authors analyse the deficiencies of the traditional methods for predicting the seismic damage to multistory masonry buildings based on earthquake intensity and adopt the peak acceleration value as a ground motion index to predict the seismic damage to buildings,so a new predicting model based on BP neural network model is presented.Several seismic damage samples of multistory masonry buildings are induded.In this model,the input data are the structural physical parameters which can express the performance of the earthquake-resistance and the output results are the probabilities of the different failure status under different peak accelerations.The research shows that the prediction results are similar to the actual seismic damage to multistory masonry buildings by the BP neural network model and the analytic method and process discussed in this paper can also be applied to the seismic damage prediction of other structures with different forms.
The authors analyse the deficiencies of the traditional methods for predicting the seismic damage to multistory masonry buildings based on earthquake intensity and adopt the peak acceleration value as a ground motion index to predict the seismic damage to buildings,so a new predicting model based on BP neural network model is presented.Several seismic damage samples of multistory masonry buildings are induded.In this model,the input data are the structural physical parameters which can express the performance of the earthquake-resistance and the output results are the probabilities of the different failure status under different peak accelerations.The research shows that the prediction results are similar to the actual seismic damage to multistory masonry buildings by the BP neural network model and the analytic method and process discussed in this paper can also be applied to the seismic damage prediction of other structures with different forms.
引文
[1]任锡泰,高惠瑛.建筑物震害空间分布模拟G IS方法[J].世界地震工程,2003,19(2):90~95.
[2]尹之潜.地震灾害及损失预测方法[M].北京:地震出版社,1996:67.
[3]陶正如,陶夏新.基于地震动参数的建筑物震害预测[J].地震工程与工程振动,2004,24(2):88~94.
[4]钟德理,冯启民.基于地震动参数的建筑物震害研究[J].地震工程与工程振动,2004,24(5):46~51.
[5]飞思科技产品研发中心.MATLAB6.5辅助神经网络分析与设计[M].合肥:中国科学技术出版社,2001.9~68.
[6]李方明.人工神经网络在砂土液化判别及震陷预估中的应用[D].南京:南京工业大学土木工程学院,2005.
[7]刘本玉,叶燎原,江见鲸.用模糊人工神经网络方法预测多层砖房震害[J].清华大学学报(自然科学版),2002,42(6):843~846.
[8]成小平,胡聿贤,帅向华.基于神经网络模型的房屋震害易损性估计方法[J].自然灾害学报,2000,9(2):68~73.
[9]汤皓,陈国兴.灰关联与人工神经网络在建筑物震害预测中的应用[J].地震工程与工程振动,2006,26(3):57~59.
[10]谭克坚,曹晖,毛沂.建筑体型抗震性能模糊评判专家系统[J].重庆建筑大学学报,1997,19(6):108~112.
[11]袁雪霞,赵艳林.多层砖房震害预测的人工神经网络方法[J].广西大学学报(自然科学版),2000,25(3):234~237.
[12]刘恢先主编.唐山大地震震害(第2册)[M].北京:地震出版社,1986.
[13]缪升,刘本玉,叶燎原.多层砖房震害预测的人工神经网络方法及实践[J].世界地震工程,2000,16(1):21~27.
[2]尹之潜.地震灾害及损失预测方法[M].北京:地震出版社,1996:67.
[3]陶正如,陶夏新.基于地震动参数的建筑物震害预测[J].地震工程与工程振动,2004,24(2):88~94.
[4]钟德理,冯启民.基于地震动参数的建筑物震害研究[J].地震工程与工程振动,2004,24(5):46~51.
[5]飞思科技产品研发中心.MATLAB6.5辅助神经网络分析与设计[M].合肥:中国科学技术出版社,2001.9~68.
[6]李方明.人工神经网络在砂土液化判别及震陷预估中的应用[D].南京:南京工业大学土木工程学院,2005.
[7]刘本玉,叶燎原,江见鲸.用模糊人工神经网络方法预测多层砖房震害[J].清华大学学报(自然科学版),2002,42(6):843~846.
[8]成小平,胡聿贤,帅向华.基于神经网络模型的房屋震害易损性估计方法[J].自然灾害学报,2000,9(2):68~73.
[9]汤皓,陈国兴.灰关联与人工神经网络在建筑物震害预测中的应用[J].地震工程与工程振动,2006,26(3):57~59.
[10]谭克坚,曹晖,毛沂.建筑体型抗震性能模糊评判专家系统[J].重庆建筑大学学报,1997,19(6):108~112.
[11]袁雪霞,赵艳林.多层砖房震害预测的人工神经网络方法[J].广西大学学报(自然科学版),2000,25(3):234~237.
[12]刘恢先主编.唐山大地震震害(第2册)[M].北京:地震出版社,1986.
[13]缪升,刘本玉,叶燎原.多层砖房震害预测的人工神经网络方法及实践[J].世界地震工程,2000,16(1):21~27.
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