Prediction of california bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine

被引：0
|
作者：
Sabat, Akshaya Kumar [1 ]
机构：
[1] Department of Civil Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Khandagiri Square, Bhubaneswar, India
来源：
Electronic Journal of Geotechnical Engineering | 2015年 / 20卷 / 03期
关键词：
Ann models - California bearing ratio - Curing periods - Expansive soils - Paper models - Predictive models - Quarry dust - SVM model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Expansive soil possess low California bearing ratio (CBR) value other than the peculiar swell-shrink behaviour, hence it is stabilized using admixtures, to be suitable for construction of pavements. In this paper models have been developed for prediction of CBR of an expansive soil stabilized with lime and quarry dust at different curing periods, using artificial neural network (ANN) and support vector machine (SVM). The accuracy of the predictive models has been compared. It is found that both the ANN and the SVM models are very accurate in prediction of CBR of stabilized expansive soil and the performances of the SVM models are found to be better than that of the ANN models. © 2015 ejge.
引用
收藏
页码：981 / 991
相关论文
共 50 条
  • [1] Prediction of california bearing ratio of a soil stabilized with lime and quarry dust using artificial neural network
    Sabat, Akshaya Kumar
    Electronic Journal of Geotechnical Engineering, 2013, 18 P : 3261 - 3272
  • [2] Crop Prediction Using Artificial Neural Network and Support Vector Machine
    Fegade, Tanuja K.
    Pawar, B. V.
    DATA MANAGEMENT, ANALYTICS AND INNOVATION, ICDMAI 2019, VOL 2, 2020, 1016 : 311 - 324
  • [3] Diagnosis and Classifications of Bearing Faults Using Artificial Neural Network and Support Vector Machine
    Agrawal P.
    Jayaswal P.
    Agrawal, Pavan (Pavanmits2012@gmail.com), 1600, Springer (101): : 61 - 72
  • [4] Approximating support vector machine with artificial neural network for fast prediction
    Kang, Seokho
    Cho, Sungzoon
    EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (10) : 4989 - 4995
  • [5] Osteoporosis Risk Prediction Using Enhanced Support Vector Machine over Artificial Neural Network
    Jagadeesh, A.
    Kumar, Senthil S.
    JOURNAL OF PHARMACEUTICAL NEGATIVE RESULTS, 2022, 13 : 1602 - 1611
  • [6] Spam Email Detection Using Deep Support Vector Machine, Support Vector Machine and Artificial Neural Network
    Roy, Sanjiban Sekhar
    Sinha, Abhishek
    Roy, Reetika
    Barna, Cornel
    Samui, Pijush
    SOFT COMPUTING APPLICATIONS, SOFA 2016, VOL 2, 2018, 634 : 162 - 174
  • [7] Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction
    Jafarzadeh, A. A.
    Pal, M.
    Servati, M.
    FazeliFard, M. H.
    Ghorbani, M. A.
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 2016, 13 (01) : 87 - 96
  • [8] Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction
    A. A. Jafarzadeh
    M. Pal
    M. Servati
    M. H. FazeliFard
    M. A. Ghorbani
    International Journal of Environmental Science and Technology, 2016, 13 : 87 - 96
  • [9] Prediction of Field Hydraulic Conductivity of Clay Liners Using an Artificial Neural Network and Support Vector Machine
    Das, Sarat Kumar
    Samui, Pijush
    Sabat, Akshaya Kumar
    INTERNATIONAL JOURNAL OF GEOMECHANICS, 2012, 12 (05) : 606 - 611
  • [10] Fault diagnosis of induction machine using artificial neural network and support vector machine
    Fang, Ruiming
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS, 2006, 13 : 658 - 661
12345