Journal of Shanghai Jiaotong University ›› 2018, Vol. 52 ›› Issue (11): 1508-1515.doi: 10.16183/j.cnki.jsjtu.2018.11.013
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ZHONG Guoqiang,WANG Hao,ZHANG Guohua,QIN Weimin WANG Chengtang,XIONG Junfeng
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ZHONG Guoqiang,WANG Hao,ZHANG Guohua,QIN Weimin WANG Chengtang,XIONG Junfeng. Analysis and Prediction of Factors Affecting Horizontal Displacement of Foundation Pit Based on RS-MIV-ELM Model[J]. Journal of Shanghai Jiaotong University, 2018, 52(11): 1508-1515.
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[1]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 Ruiping, LIU Xinyu, 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. [2]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 Junan. Research on deformation prediction method of foundation pit re-taining structure based on BP artificial neural network[J]. Railway Engineering, 2011(1): 70-73. [3]熊孝波, 桂国庆, 郑明新, 等. 基于免疫RBF神经网络的深基坑施工变形预测[J]. 岩土力学, 2008, 28(S1): 598-602. XIONG Xiaobo, GUI Guoqing, ZHENG Mingxin, et al. Research on deformation prediction for deep foundation pit based on the artificial immune RBF neural network[J]. Rock and Soil Mechanics, 2008, 28(S1): 598-602. [4]徐洪钟, 滕坤, 李雪红. 基于LS-SVM的基坑变形时间序列预测模型[J]. 水电能源科学, 2011, 29(12): 92-94. XU Hongzhong, TENG Kun, LI Xuehong. Time series prediction model of deformation of foundation pit based on least squares support vector machines[J]. Water Resources and Power, 2011, 29(12): 92-94. [5]时红莲, 李大毛, 张涛, 等. 基于时间序列的深基坑支护结构变形预测[J]. 安全与环境工程, 2005, 12(2): 79-82. SHI Honglian, LI Damao, ZHANG Tao, et al. Deformation forecasting for supporting structure of deep pit based on time series[J]. Safety and Environmental Engineering, 2005, 12(2): 79-82. [6]胡冬, 张小平. 基于灰色系统理论的基坑变形预测研究[J]. 地下空间与工程学报, 2009, 5(1): 74-78. HU Dong, ZHANG Xiaoping. Research on predicting deformation of foundation pit based on grey system theory[J]. Chinese Journal of Underground Space and Engineering, 2009, 5(1): 74-78. [7]贾备, 邬亮. 基于灰色BP神经网络组合模型的基坑变形预测研究[J]. 隧道建设, 2009, 29(3): 280-284. JIA Bei, WU Liang. Research of the prediction of foundation deformation based on gray BP neural network combined model[J]. Tunnel Construction, 2009, 29(3): 280-284. [8]PAWLAK Z. Rough sets-theoretical aspects of reasoning about data[M]. Boston, USA: Klumer Academic Publishers, 1991. [9]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: 12-19. WANG Xiaochuan, SHI Feng, YU Lei, et al. Ana-lysis of 43 cases of MATLAB neural network[M]. Beijing: Beihang University Press, 2013: 12-19. [10]HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: A new learning scheme of feed-forward neural networks[C]//Proceedings of the International Joint Conference on Neural Networks. Budapest, Hungary: IEEE, 2004: 985-990. [11]HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications[J]. Neuro Computing, 2006, 70(1/2/3): 489-501. [12]李春祥, 迟恩楠, 李正农. 基于极限学习机的脉动风速快速预测方法[J]. 上海交通大学学报, 2016, 50(11): 1719-1723. LI Chunxiang, CHI Ennan, LI Zhengnong. Fore-casting method for fluctuating wind velocity based on extreme learning machine[J]. Journal of Shanghai Jiao Tong University, 2016, 50(11): 1719-1723. [13]刘学艺, 李平, 郜传厚. 极限学习机的快速留一交叉验证算法[J]. 上海交通大学学报, 2011, 45(8): 1140-1145. LIU Xueyi, LI Ping, GAO Chuanhou. Fast leave-one-out cross-validation algorithm for extreme learning machine[J]. Journal of Shanghai Jiao Tong University, 2011, 45(8): 1140-1145. [14]刘菲菲, 彭荻, 贺彦林, 等. 基于极限学习的过程神经网络研究及化工应用[J]. 上海交通大学学报, 2014, 48(7): 977-981. LIU Feifei, PENG Di, HE Yanlin, et al. Research and chemical application of extreme learning machine[J]. Journal of Shanghai Jiao Tong University, 2014, 48(7): 977-981. [15]DOMBI G W, NANDI J, SAXE P, et al. Prediction of rib fracture injury outcome by an artificial neural network[J]. Journal of Trauma, 1995, 39(5): 915-921. [16]刘建航, 侯学渊. 基坑工程手册[M]. 2版.北京: 中国建筑工业出版社, 1997: 191-200. LIU Jianhang, HOU Xueyuan. Excavation engineering manual[M]. 2nd edition. Beijing: China Construction Industry Press, 1997: 191-200. [17]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: 西南交通大学土木工程学院, 2006. BAI Yongxue. Effect factor and control method on deformation of deep pit of the subway station in soft soil[D]. Chengdu: College of Civil Engineering, Southwest Jiaotong University, 2006. [18]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(S1): 3242-3249. JIN Xuelian, FAN Youwei, LI Chunzhong, et al. Analysis of factors affecting support structure deformation of foundation pit with brace[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(S1): 3242-3249. |
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