| ABC BP模型在混凝土双曲拱坝变形监控中的应用 |
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| 引用本文： | 陈〓晨,邵晨飞,魏〓玮,江潜成,李经纬,杨〓孟.ABC BP模型在混凝土双曲拱坝变形监控中的应用[J].水电能源科学,2013,31(8):112-114. |
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| 作者姓名： | 陈〓晨 邵晨飞 魏〓玮 江潜成 李经纬 杨〓孟 |
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| 作者单位： | 河海大学 水利水电学院， 江苏 南京 210098； 河海大学 水文水资源与水利工程科学国家重点实验室，江苏 南京 210098;河海大学 水利水电学院， 江苏 南京 210098； 河海大学 水文水资源与水利工程科学国家重点实验室，江苏 南京 210098;河海大学 水利水电学院， 江苏 南京 210098； 河海大学 水文水资源与水利工程科学国家重点实验室，江苏 南京 210098;南昌工程学院 水利与生态工程学院, 江西 南昌 330099;河海大学 水利水电学院， 江苏 南京 210098； 河海大学 水文水资源与水利工程科学国家重点实验室，江苏 南京 210098;河海大学 水利水电学院， 江苏 南京 210098； 河海大学 水文水资源与水利工程科学国家重点实验室，江苏 南京 210098 |
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| 基金项目： | 中国电力投资集团公司科技基金资助项目（2011 042 HHS KJ X）；国家自然科学基金资助项目（51139001，51279052，51209077） |
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| 摘 要： | 因大坝变形具有很强的非线性、随机性，致使预测困难，将人工蜂群算法(ABC)与BP神经网络相结合，利用人工蜂群算法具有强全局优化能力、强鲁棒性等优点，克服BP神经网络收敛速度慢、易陷入局部极小点等缺点，建立ABC BP、BP神经网络大坝变形预测模型对小湾大坝变形监测数据进行预测。结果表明，与单纯的BP神经网络预测模型相比，ABC BP算法提高了大坝变形预报的精度，加快了网络的收敛速度，能更高效准确地进行大坝变形监控预报。
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| 关 键 词： | 大坝变形 小湾大坝 人工蜂群算法 BP神经网络 权值 |
Application of ABC-BP Model in Deformation Monitoring of Concrete Double curvature Arch Dam |
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| CHEN Chen,SHAO Chenfei,WEI Wei,JIANG Qiancheng,LI Jingwei and YANG Meng.Application of ABC-BP Model in Deformation Monitoring of Concrete Double curvature Arch Dam[J].International Journal Hydroelectric Energy,2013,31(8):112-114. |
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| Authors: | CHEN Chen SHAO Chenfei WEI Wei JIANG Qiancheng LI Jingwei and YANG Meng |
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| Affiliation: | College of Water Conservancy and Hydropower, Hohai University Nanjing 210098, China; State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;College of Water Conservancy and Hydropower, Hohai University Nanjing 210098, China; State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;College of Water Conservancy and Hydropower, Hohai University Nanjing 210098, China; State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;College of Water Conservancy and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China;College of Water Conservancy and Hydropower, Hohai University Nanjing 210098, China; State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;College of Water Conservancy and Hydropower, Hohai University Nanjing 210098, China; State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China |
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| Abstract: | Dam deformation has characteristics of strong nonlinearity and randomness so that it is difficult for prediction of the dam deformation. Artificial bee colony (ABC) has advantages of global optimization ability and strong robustness and it can overcome the disadvantages of slow convergence and easily trapping into local minimal value points for BP neural networks. ABC BP and BP neural network models for forecasting dam deformation are established by combining BP neural network with the artificial bee colony algorithm. ABC BP and BP neural network models are applied to predict Xiaowan dam deformation monitoring data. Compared with BP neural network prediction method, the results show that ABC BP algorithm enhances the precision of dam deformation prediction and accelerates the convergence rate of the network. Thus, ABC BP model can be more efficient and accurate for prediction of dam deformation monitoring. |
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| Keywords: | dam deformation Xiaowan dam artificial bee colony BP neural network weight |
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