基于SFLA-GRNN模型的基坑地表最大沉降预测
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摘要
为可靠预测基坑周边地表沉降的发展趋势,提出了一种基于混合蛙跳算法和广义回归神经网络模型的基坑地表最大沉降预测模型(SFLA-GRNN模型)。首先,在沉降机制分析并初选输入变量集的基础上,利用灰色相关度分析对模型输入、输出变量的相关性进行量化,并剔除与输出变量相关性明显偏小的输入变量;其次,利用混合蛙跳算法(SFLA)对广义回归神经网络模型(GRNN)的平滑因子进行优化确定,减少人为因素对模型精度和泛化能力的不良影响;最后,利用筛选得到的输入变量集建立基坑地表最大沉降预测的广义回归神经网络模型。实例应用及对比计算结果表明,基于灰色相关度的输入变量筛选和基于混合蛙跳算法的平滑因子优化均能够有效提高广义回归神经网络模型的精度和泛化能力,以上结论可为类似变形预测提供参考。
To predict the development trend of ground settlement around foundation pit accurately, a prediction model for maximum ground settlement of foundation pit was proposed based on shuffled frog leaping algorithm and generalized regression neural network model(SFLA-GRNN model). Firstly, through the settlement mechanism analysis and the initial selection of the input variable set, grey correlation analysis was used to quantify the correlation between model input and output variables. Some of input variables that are significantly less correlated with output variables were eliminated. Secondly, the smoothing factor of the generalized regression neural network model(GRNN) was optimized by using the shuffled frog algorithm(SFLA), so as to reduce the adverse effects of human factors on the accuracy and generalization ability of the model. Finally, a generalized regression neural network model for predicting the maximum settlement of the foundation pit was established by using the selected input variables set. Example application and comparative analysis show that input variables selection based on gray correlation degree and smoothing factor optimization based on shuffled frog leaping algorithm all can effectively improve the accuracy and generalization ability of GRNN model. The above conclusions can provide reference for similar deformation prediction.
To predict the development trend of ground settlement around foundation pit accurately, a prediction model for maximum ground settlement of foundation pit was proposed based on shuffled frog leaping algorithm and generalized regression neural network model(SFLA-GRNN model). Firstly, through the settlement mechanism analysis and the initial selection of the input variable set, grey correlation analysis was used to quantify the correlation between model input and output variables. Some of input variables that are significantly less correlated with output variables were eliminated. Secondly, the smoothing factor of the generalized regression neural network model(GRNN) was optimized by using the shuffled frog algorithm(SFLA), so as to reduce the adverse effects of human factors on the accuracy and generalization ability of the model. Finally, a generalized regression neural network model for predicting the maximum settlement of the foundation pit was established by using the selected input variables set. Example application and comparative analysis show that input variables selection based on gray correlation degree and smoothing factor optimization based on shuffled frog leaping algorithm all can effectively improve the accuracy and generalization ability of GRNN model. The above conclusions can provide reference for similar deformation prediction.
引文
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[15]白永学.软土地铁车站深基坑变形的影响因素及其控制措施[D].成都:西南交通大学,2006.BAI Yong-xue.Effect factor and control method on deformation of deep pit of the subway station in soft soil[D].Chengdu:Southwest Jiaotong University,2006.
[16]金雪莲,樊有维,李春忠,等.带撑式基坑支护结构变形影响因素分析[J].岩石力学与工程学报,2007,26(增刊1):3242-3249.JIN Xue-lian,FAN You-wei,LI Chun-zhong,et al.Analysis of factors affecting support structure deformation of foundation pit with brace[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(Supp.1):3242-3249.
[17]刘思峰,郭天榜,党耀国.灰色系统理论及其应用[M].3版北京:科学出版社,2004:49-85.LIU Si-feng,GUO Tian-bang,DANG Yao-guo.Grey system theory and its application[M].3rd ed.Beijing:Science Press,2004:49-85.
[2]王小川,史峰,郁磊,等.Matlab神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013:67-74.WANG Xiao-chuan,SHI Feng,YU Lei,et al.43 case studies of Matlab neural network[M].Beijing:Beihang University Press,2013:67-74.
[3]孙立超.深基坑围护结构变形的广义神经网络研究[D].北京:中国地质大学(北京),2014.SUN Li-chao.Study on horizontal displacement prediction of retaining structure of deep foundation pit based on generalized regression neural network[D].Beijing:China University of Geosciences(Beijing),2014.
[4]李钦,左廷英,何卓臣,等.广义回归神经网络在深基坑监测中的应用[J].现代测绘,2012,35(4):8-10.LI Qin,ZUO Ting-ying,HE Zhuo-chen,et al.Application of general regression neural network to monitoring of foundation pit[J].Modern Surveying and Mapping,2012,35(4):8-10.
[5]贾义鹏,吕庆,尚岳全.基于粒子群算法和广义回归神经网络的岩爆预测[J].岩石力学与工程学报,2013,32(2):343-348.JIA Yi-peng,LüQing,SHANG Yue-quan.Rockburst prediction using particle swarm optimization Algorithm and general regression neural network[J].Chinese Journal of Rock Mechanics and Engineering,2013,32(2):343-348.
[6]刘开云,乔春生,刘保国.基于遗传-广义回归神经元算法的坞石隧道三维弹塑性位移反分析研究[J].岩土力学,2009,30(6):1805-1809.LIU Kai-yun,QIAO Chun-sheng,LIU Bao-guo.Research on elastoplastic displacement back analysis method based on GA-GRNN algorithm in three-dimension of Wushi tunnel[J].Rock and Soil Mechanics,2009,30(6):1805-1809.
[7]华瑞平,刘新宇,习剑.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),2001,2(5):67-70.HUA Rui-ping,LIU Xin-yu,XI Jian.Application of neural network to forecasting deformation of bracing of deep excavation pit[J].Journal of PLA University of Science and Technology(Natural Science),2001,2(5):67-70.
[8]曾晖,胡俊,鲍俊安.基于BP人工神经网络的基坑围护结构变形预测方法研究[J].铁道建筑,2011,(1):70-73.ZENG Hui,HU Jun,BAO Jun-an.Research on deformation prediction method of foundation pit retaining structure based on BP artificial neural network[J].Railway Engineering,2011,(1):70-73.
[9]SPRECHT D F.A general regression neural network[J].IEE Transactions on Neural Networks,1991,2(6):568-576.
[10]SPRECHT D F.The general regression neural network rediscovered[J].Neural Networks,1993,6(7):1033-1034.
[11]EUSUFF M M,LANSEY K E.Optimization of water distribution network design using the shuffled frog leaping algorithm[J].Journal of Water Resources Planning Management,2003,129(3):210-225.
[12]刘建航,侯学渊.基坑工程手册[M].北京:中国建筑工业出版社,2009:191-200.LIU Jian-hang,HOU Xue-yuan.Excavation engineering manual[M].Beijing:China Architecture&Building Press,2009:191-200.
[13]祝汉锋,吴盛才.基坑开挖地表沉降影响因素及关联度研究[J].城市勘测,2016,(1):168-171.ZHU Han-feng,WU Sheng-cai.Study of factors affecting the ground settlement and relational grade due to excavation[J].Urban Geotechnical Investigation&Surveying,2016,(1):168-171.
[14]齐干,朱瑞钧.基于BP网络的基坑周围地表沉降影响因素分析[J].地下空间与工程学报,2007,3(5):863-867,871.QI Gan,ZHU Rui-jun.Analysis of factors affecting the ground settlement around deep foundation pit based on BP neural network[J].Chinese Journal of Underground Space and Engineering,2007,3(5):863-867,871.
[15]白永学.软土地铁车站深基坑变形的影响因素及其控制措施[D].成都:西南交通大学,2006.BAI Yong-xue.Effect factor and control method on deformation of deep pit of the subway station in soft soil[D].Chengdu:Southwest Jiaotong University,2006.
[16]金雪莲,樊有维,李春忠,等.带撑式基坑支护结构变形影响因素分析[J].岩石力学与工程学报,2007,26(增刊1):3242-3249.JIN Xue-lian,FAN You-wei,LI Chun-zhong,et al.Analysis of factors affecting support structure deformation of foundation pit with brace[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(Supp.1):3242-3249.
[17]刘思峰,郭天榜,党耀国.灰色系统理论及其应用[M].3版北京:科学出版社,2004:49-85.LIU Si-feng,GUO Tian-bang,DANG Yao-guo.Grey system theory and its application[M].3rd ed.Beijing:Science Press,2004:49-85.
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