| 基于ACCRBF网络的多层砖房震害预测 |
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| 引用本文： | 杨秀萍,程运平,陈克珩,丁大勇.基于ACCRBF网络的多层砖房震害预测[J].震灾防御技术,2013,8(1):90-96. |
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| 作者姓名： | 杨秀萍 程运平 陈克珩 丁大勇 |
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| 作者单位： | 荆门市民防办,荆门,448000 |
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| 摘 要： | 针对传统震害预测方法逐栋抽样计算建筑物抗震性能的不足,本文提出了一种基于蚁群聚类径向基(ACCRBF)网络模型的建筑物震害预测方法。依据不同地震动峰值加速度下多层砖房的实际震害资料,对模型进行训练,在模型的输入和输出之间建立映射关系,并利用这种映射关系对未知样本进行分类,实现对多层砖房的震害分析和预测。模型的输入为反映结构的震害影响因子,输出为给定的地震动峰值加速度下结构震害等级。研究表明,基于ACCRBF网络模型的多层砖房震害预测结果与震害实例基本吻合,具有推广应用价值。
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| 关 键 词： | ACCRBF网络 多层砖房 震害预测 震害因子 破坏等级 |
| 收稿时间： | 2012/10/12 0:00:00 |
Seismic Damage Prediction of Multistory Masonry Buildings Based on ACCRBF Networks |
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| Yang Xiuping,Cheng Yunping,Chen Keheng and Ding Dayong.Seismic Damage Prediction of Multistory Masonry Buildings Based on ACCRBF Networks[J].Technology for Earthquake Disaster Prevention,2013,8(1):90-96. |
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| Authors: | Yang Xiuping Cheng Yunping Chen Keheng and Ding Dayong |
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| Institution: | Jingmen Civil Defense Office, Jingmen 448000, China;Jingmen Civil Defense Office, Jingmen 448000, China;Jingmen Civil Defense Office, Jingmen 448000, China;Jingmen Civil Defense Office, Jingmen 448000, China |
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| Abstract: | To overcome the deficiency in the conventional seismic damage prediction method, in which the anti-earthquake behavior is evaluated by sampling survey, here we present a construction seismic damage prediction method based on ant colony clustering radial basis function (ACCRBF) neural network model. Through training the network on the basis of real seismic damage data, this method sets up the mapping relationship of the parameters between the inputs and outputs. Then The mapping relationship then can be adopted for sample classification, which makes it possible to realize the seismic damage evaluation and hazard prediction. The inputs of the model are the factors that affect the seismic damage, and the output of the model is the seismic damage level under certain peak ground acceleration. Our results show that the seismic damage prediction results from the ACCRBF neural model are in good agreement with the real examples. Therefore, it is worthy of promotion and application in the future. |
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| Keywords: | ACCRBF neural networks Multistory masonry buildings Seismic damage prediction Factors for seismic damage Damage degree |
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