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| 基于BP神经网络的单层钢筋混凝土柱工业厂房震害预测 | |
| 引用本文： | 赵艳林,杨军平,黄剑飞,吕海波.基于BP神经网络的单层钢筋混凝土柱工业厂房震害预测[J].桂林工学院学报,2006,26(4):491-496. |
| 作者姓名： | 赵艳林 杨军平 黄剑飞 吕海波 |
| 作者单位： | 1. 桂林工学院,土木工程系,广西,桂林,541004;广西大学,土木建筑工程学院防灾减灾研究所,南宁,530004 2. 广西南宁市建筑设计院,南宁,5300122 3. 广西大学,土木建筑工程学院防灾减灾研究所,南宁,530004 |
| 基金项目： | 广西科技攻关项目（桂科攻0480003）;广西教育厅重点科研项目（桂教科研（2003）22） |
| 摘 要： | 将人工神经网络理论应用于等高单层钢筋混凝土柱工业厂房的震害预测.在分析震害特点的基础上,将震害影响因子分为精确性和规律性两大类,提出以地震反应指标、天窗类型、支撑情况、建筑材料作为主要的影响因子,并给出了相应的量化取值范围,然后将震害等级作为输出结果,构造了震害预测的BP人工神经网络.通过对52个实际震害实例的检验,网络的准确率超过80%.计算结果证明了该人工神经网络的有效性. |
| 关 键 词： | 工业厂房 震害预测 人工神经网络 |
| 文章编号： | 1006-544X(2006)04-0491-06 |
| 收稿时间： | 2005-12-30 |
| 修稿时间： | 2005年12月30 |
Seismic damage prediction for single-story reinforced concrete industrial building based on back propagation neural network |
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| ZHAO Yan-lin,YANG Jun-ping,HUANG Jian-fei,LU Hai-bo.Seismic damage prediction for single-story reinforced concrete industrial building based on back propagation neural network[J].Journal of Guilin University of Technology,2006,26(4):491-496. | |
| Authors: | ZHAO Yan-lin YANG Jun-ping HUANG Jian-fei LU Hai-bo |
| Affiliation: | 1. Department of Civil Engineering, Guilin University of Technology, Guilin 541004, China; 2. Research Institute of Preventing and Mitigating Disasters, College of Civil Engineering, Guaagxi University, Nanning 530004, China; 3. Nanning Institute of Architecture Design , Nanning 530004, China |
| Abstract: | A back propagation artificial neural network is applied to predict seismic damage of single-story reinforced concrete industrial building.Based on the analysis of characteristics of seismic damage,it is found that earthquake response index,type of skylight,bracing system and building material are the main factors affecting seismic damage.The four factors can be classified into two types: precise factors and regular factors.The corresponding spans of factors are suggested and applied to engineering examples.Thus the back propagation artificial neural network is developed,with factors affecting seismic damage as input and seismic damage grade as output.Verified by 52 engineering examples,the percentage of accuracy is above 80%.It is concluded that the back propagation artificial neural network developed in this paper is applicable to predict seismic damage of single-story reinforced concrete industrial building. |
| Keywords: | industrial building seismic damage prediction artificial neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |