基于神经网络的基坑变形预测及安全控制研究
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摘要
研究目的:基坑变形影响到自身安全及周边建(构)筑物的安全,通过神经网络分析预判基坑变形和预警方法的应用,提供一种新的风险控制措施。研究结论:神经网络可以有效地进行基坑变形预测,起到预判基坑变形稳定性的作用;本工程把时间序列作为输入层的预测结果更接近于实测值,说明把时间序列作为输入层元素更能体现出基坑围护结构变形与时间的内在非线性关系;从实际施工来看施工工序的转变,基坑围护结构的变形实测值出现突变,神经网络预测结果误差偏大,这可能与神经网络样本少而导致训练效果不佳有关。
Research purposes: The foundation pit deformation threats the safety of the foundation pit and surrounding buildings and structures.Application of neural networks in prediction of the foundation pit deformation in advance and giving alarm is a new way to control the risk.Research conclusions:The neural networks can effectively predict the deformation and stability of the deep foundation pit in advance.and the stability based on which a judgement can be made on the stability of the deep excavation.The prediction value is much more closed to the measured value by using time series as an input layer.This shows the time series method can reflect the internal non-linear relation between the deformation of bracing structure of foundation pit and the time.In practice,the transfer of construction processing sequence can induce a sudden change of the measured value of the deformation.However,the big deviation of the predicted value from the mesaured value may result from ineffective training due to the small sample range of neural networks.
Research purposes: The foundation pit deformation threats the safety of the foundation pit and surrounding buildings and structures.Application of neural networks in prediction of the foundation pit deformation in advance and giving alarm is a new way to control the risk.Research conclusions:The neural networks can effectively predict the deformation and stability of the deep foundation pit in advance.and the stability based on which a judgement can be made on the stability of the deep excavation.The prediction value is much more closed to the measured value by using time series as an input layer.This shows the time series method can reflect the internal non-linear relation between the deformation of bracing structure of foundation pit and the time.In practice,the transfer of construction processing sequence can induce a sudden change of the measured value of the deformation.However,the big deviation of the predicted value from the mesaured value may result from ineffective training due to the small sample range of neural networks.
引文
[1]王锦山.基于神经网络深基坑排桩支护及地表沉降研究[J].沈阳工业大学学报,2001(1):79-82.Wang Jinsan.Study on Structure Design and GroundSettlement in Deep Foundation Pit Based on ArtificialNeural Networks[J].Shenyang:Journal of ShenyangUniversity,2001(1):79-82.
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[9]葛长峰,等.人工神经网络在预测深基坑周边地表沉降变形中的应用研究[J].防灾减灾工程学报,2008(4):519-523.Ge Changfeng,et al.A Study on Application ofArtificial Neural Network in Prediction of GroundSurface Settlement around Deep Foundation Pit[J].Journal of Disaster Prevention and MitigationEngineering,2008(4):519-523.
[10]郑美田,等.建筑基坑信息化施工和安全监测技术[J].工程建设,2007(5):41-46.Zheng Meitian,et al.Safety Monitoring Technologyand Information-based Construction of BuildingFoundation Pit[J].Engineering Construction,2007(5):41-46.
[11]刘招伟,赵运程.城市地下工程施工监测与信息反馈技术[M].北京:科学出版社,2006.Liu Zhaowei,Zhao Yuncheng.Construction Monitoringand Information Feedback Technique of UrbanUnderground Works[M].Beijing:Science andTechnology Press,2006.
[2]麻凤海,等.BP神经网络在桩基支护方式中的应用[J].中国地震灾害与防治学报,2002(13):97-99.Ma Fenghai,et al.Application of BP Neural NetworkModel in Supporting Pattern of Pile Foundation[J].The Chinese Journal of Geological Hazard and Control,2002(13):97-99.
[3]韦立德,等.基坑支护结构水平变形预测的遗传神经网络方法[J].工程地质学报,2003(3):297-301.Wei Lide,et al.Application of Genetic Neural NetworkMethod to Forecasting Horizontal Deformation ofSupport Structure at Foundation Pit[J].Journal ofEngineering Geology,2003(3):297-301.
[4]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005.Zhou Kaili,Kang Yaohong.Neural Networks Model andProgram Design by MATLAB Simulate[M].Beijing:Tsinghua University Press,2005.
[5]C.G.Chua,Anthony T.C.Goh.Estimating walldeflections in deep excavations using Bayesian neuralnetworks[J].Tunnelling and Underground SpaceTechnology,2005(20):400-409.
[6]Hashash,Y.M.A.&A.J.Whittle.Ground MovementPrediction for Deep Excavations in soft clay[J].Journal of Geotechnical Engineering,1996,122(6):474-486.
[7]华瑞平,等.神经网络在深基坑支护变形预测中的应用[J].解放军理工大学学报(自然科学版),2002(5):67-70.Hua Ruiping,et al.Application of Neural Network toForecasting Deformation of Bracing of Deep ExcavationPit[J].Journal of PLA University of Science andTechnology,2002(5):67-70.
[8]吴贤国,等.深基坑支护变形的模糊神经网络预测[J].基建优化,2005(5):83-87.Wu Xianguo,et al.Deformation Predication of Bracingof Deep Excavation Pit Based on Fuzzy Neural Network[J].Optimization of Capital Construction,2005(5):83-87.
[9]葛长峰,等.人工神经网络在预测深基坑周边地表沉降变形中的应用研究[J].防灾减灾工程学报,2008(4):519-523.Ge Changfeng,et al.A Study on Application ofArtificial Neural Network in Prediction of GroundSurface Settlement around Deep Foundation Pit[J].Journal of Disaster Prevention and MitigationEngineering,2008(4):519-523.
[10]郑美田,等.建筑基坑信息化施工和安全监测技术[J].工程建设,2007(5):41-46.Zheng Meitian,et al.Safety Monitoring Technologyand Information-based Construction of BuildingFoundation Pit[J].Engineering Construction,2007(5):41-46.
[11]刘招伟,赵运程.城市地下工程施工监测与信息反馈技术[M].北京:科学出版社,2006.Liu Zhaowei,Zhao Yuncheng.Construction Monitoringand Information Feedback Technique of UrbanUnderground Works[M].Beijing:Science andTechnology Press,2006.
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