Mingshan tunnel construction period settlement prediction based on DE-SVM

被引:0
|
作者
Lu, Zhongle [1 ]
Wu, Li [1 ]
Zhang, Xuewen [1 ]
Zhou, Ruifeng [1 ]
机构
[1] Faculty of Engineering, China University of Geosciences(Wuhan), China
关键词
In tunnel construction period; there exists a complex; nonlinear relation between time and settlement; as a research branch of tunnel time-space effects. The paper proposes the combination of differential evolution and support vector machine to form the DE-SVM model applied to accurately predict tunnel arc top settlement based on the site survey. Through introduced and analysis of the SVM function and its system structure; and DE optimal process; the DE-SVM can be suitably applied to tunnel settlement prediction to achieve ideal effects. By compare on the same sample data regressions by DE-SVM and GA-SVM; the error of prediction by DE-SVM is obviously less than that of the other model. From the case of Mingshan High Speed Railway Tunnel; the advantages of DE-SVM are expounded that it owns the characters of higher accuracy; faster convergence; and stronger adjustability; therefore DE-SVM settlement prediction model can be widely used to the similar construction required the high precision and the simple approach. © 2013; EJGE;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:5525 / 5536
相关论文
共 50 条
  • [41] Settlement Prediction Using Support Vector Machine (SVM)-Based Compressibility Models: A Case Study
    Scott Kirts
    Boo Hyun Nam
    Orestis P. Panagopoulos
    Petros Xanthopoulos
    International Journal of Civil Engineering, 2019, 17 : 1547 - 1557
  • [42] Study on tunnel settlement prediction method based on parallel grey neural network model
    Zhu, Lei
    Huang, Teng
    Shen, Yue-qian
    Zeng, Xian-min
    INTERNATIONAL CONFERENCE ON INTELLIGENT EARTH OBSERVING AND APPLICATIONS 2015, 2015, 9808
  • [43] Developing an SVM based risk hedging prediction model for construction material suppliers
    Chen, Jieh-Haur
    Lin, Jia-Zheng
    AUTOMATION IN CONSTRUCTION, 2010, 19 (06) : 702 - 708
  • [44] Probabilistic reliability assessment method for max ground settlement prediction of subway tunnel under uncertain construction information
    Chen, Yangyang
    Liu, Wen
    Ai, Demi
    Zhu, Hongping
    Du, Yanliang
    COMPUTERS AND GEOTECHNICS, 2025, 177
  • [45] A deep learning-based method for predicting surface settlement induced by shield tunnel construction
    Yin Q.
    Zhou Y.
    Rao J.
    Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), 2024, 55 (02): : 607 - 617
  • [46] Settlement prediction model of shield tunnel under-crossing existing tunnel based on GA-Bi-LSTM
    Zhou Z.
    Zhang J.
    Ding H.
    Li F.
    Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, 2023, 42 (01): : 224 - 234
  • [47] Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM) Based on Measured Data
    Yu, Huiling
    Shangguan, Yunlong
    INTERNATIONAL SYMPOSIUM ON MATERIALS APPLICATION AND ENGINEERING (SMAE 2016), 2016, 67
  • [48] GIS-SVM prediction of surrounding rock stability in mountain tunnel based on numerical experiment
    Wen H.
    Huang J.
    Yuan X.
    Xie P.
    Xue J.
    1600, Academia Sinica (39): : 2920 - 2929
  • [49] Prediction and Analysis of the Tunnel Arch Top Settlement Based on the Fuzzy Support Vector Regression Machine
    Wu Weidong
    Geng Shuai
    PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING, 2010, : 337 - 341
  • [50] Prediction of soil settlement induced by double-line shield tunnel based on Peck formula
    Chen, Chun-Lai
    Zhao, Cheng-Li
    Wei, Gang
    Ding, Zhi
    Yantu Lixue/Rock and Soil Mechanics, 2014, 35 (08): : 2212 - 2218