Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles

被引：3
|
作者：
Pijush Samui
Dookie Kim
机构：
[1] VIT University,Centre for Disaster Mitigation and Management
[2] Kunsan National University,Department of Civil Engineering
来源：
Neural Computing and Applications | 2013年 / 23卷
关键词：
Least square support vector machine; Multivariate adaptive regression spline; Soft computing; Prediction; Pile foundation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This article adopts least square support vector machine (LSSVM) and multivariate adaptive regression spline (MARS) for prediction of lateral load capacity (Q) of pile foundation. LSSVM is firmly based on the theory of statistical learning, uses regression technique. MARS is a nonparametric regression technique that models complex relationships. Diameter of pile (D), depth of pile embedment (L), eccentricity of load (e), and undrained shear strength of soil (Su) have been used as input parameters of LSSVM and MARS. Equations have been presented from the developed MARS and LSSVM. This study also presents a comparative study between the developed MARS and LSSVM.
引用
收藏
页码：1123 / 1127
页数：4
相关论文
共 50 条
  • [21] Determination of ultimate capacity of driven piles in cohesionless soil: A Multivariate Adaptive Regression Spline approach
    Samui, Pijush
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, 2012, 36 (11) : 1434 - 1439
  • [22] Short Term Load Forecasting with Least Square Support Vector Regression and PSO
    Zou Min
    Tao Huanqi
    [J]. 2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL V, 2010, : 79 - 82
  • [23] Short Term Load Forecasting with Least Square Support Vector Regression and PSO
    Zou Min
    Tao Huanqi
    [J]. APPLIED INFORMATICS AND COMMUNICATION, PT 5, 2011, 228 : 124 - 132
  • [24] MODELING OF FLEXIBLE STRUCTURES BY MEANS OF LEAST SQUARE SUPPORT VECTOR MACHINE
    Tamimi, Hammam
    Soeffker, Dirk
    [J]. PROCEEDINGS OF THE ASME 8TH ANNUAL DYNAMIC SYSTEMS AND CONTROL CONFERENCE, 2015, VOL 2, 2016,
  • [25] An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators
    Mao, Xuefei
    Wang, Yijun
    Liu, Xiangdong
    Guo, Youguang
    [J]. SENSORS AND ACTUATORS A-PHYSICAL, 2017, 263 : 423 - 429
  • [26] Load prediction of hybrid excavator based on least square support vector machine
    College of Mechanical Science and Engineering, Jilin University, Changchun
    130022, China
    [J]. Jilin Daxue Xuebao (Gongxueban), 1 (133-138):
  • [27] Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR
    Cui, Wentong
    Yan, Xuefeng
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2009, 98 (02) : 130 - 135
  • [28] Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs
    Adnan, Rana Muhammad
    Liang, Zhongmin
    Heddam, Salim
    Zounemat-Kermani, Mohammad
    Kisi, Ozgur
    Li, Binquan
    [J]. JOURNAL OF HYDROLOGY, 2020, 586 (586)
  • [29] Least Square Transduction Support Vector Machine
    Rui Zhang
    Wenjian Wang
    Yichen Ma
    Changqian Men
    [J]. Neural Processing Letters, 2009, 29 : 133 - 142
  • [30] Least Square Transduction Support Vector Machine
    Zhang, Rui
    Wang, Wenjian
    Ma, Yichen
    Men, Changqian
    [J]. NEURAL PROCESSING LETTERS, 2009, 29 (02) : 133 - 142
12345