Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks

被引：43
|
作者：
Gunaydin, Osman [1 ]
Gokoglu, Ali [2 ]
Fener, Mustafa [1 ]
机构：
[1] Nigde Univ, Dept Geol Engn, TR-51200 Nigde, Turkey
[2] Cukurova Univ, Vocat Sch Ceyhan, TR-01960 Ceyhan Adana, Turkey
来源：
ADVANCES IN ENGINEERING SOFTWARE | 2010年 / 41卷 / 09期
关键词：
Artificial soil; Unconfined compression strength; Soil index properties; Artificial neural networks; Statistical analyses; Correlation;
D O I：
10.1016/j.advengsoft.2010.06.008
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Laboratory prediction of the unconfined compression strength (UCS) of cohesive soils is important to determine the shear strength properties. However, this study presents the application of different methods simple-multiple analysis and artificial neural networks for the prediction of the UCS from basic soil properties. Regression analysis and artificial neural networks prediction indicated that there exist acceptable correlations between soil properties and unconfined compression strength. Besides, artificial neural networks showed a higher performance than traditional statistical models for predicting UCS. Regression analysis and artificial neural network prediction indicated strong correlations (R-2 = 0.71-0.97) between basic soil properties and UCS. It has been shown that the correlation equations obtained by regression analyses are found to be reliable in practical situations. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1115 / 1123
页数：9
相关论文
共 50 条
  • [31] Prediction of breakdown strength of cellulosic insulating materials using artificial neural networks
    Singh, Sakshi
    Mohsin, M. M.
    Masood, Aejaz
    JOURNAL OF ADVANCED DIELECTRICS, 2018, 8 (01)
  • [32] Using Artificial Neural Networks for the Prediction of the Compressive Strength of Geopolymer Fly Ash
    Rusna, K. P.
    Kalpana, V. G.
    ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2022, 12 (05) : 9120 - 9125
  • [33] Compressive strength prediction of environmentally friendly concrete using artificial neural networks
    Naderpour, Hosein
    Rafiean, Amir Hossein
    Fakharian, Pouyan
    JOURNAL OF BUILDING ENGINEERING, 2018, 16 : 213 - 219
  • [34] Compressive strength prediction of limestone filler concrete using artificial neural networks
    Ayat, Hocine
    Kellouche, Yasmina
    Ghrici, Mohamed
    Boukhatem, Bakhta
    ADVANCES IN COMPUTATIONAL DESIGN, 2018, 3 (03): : 289 - 302
  • [35] Prediction of self-compacting concrete strength using artificial neural networks
    Asteris, P. G.
    Kolovos, K. G.
    Douvika, M. G.
    Roinos, K.
    EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING, 2016, 20 : s102 - s122
  • [36] Grip strength prediction for Malaysian industrial workers using artificial neural networks
    Taha, Z
    Nazaruddin
    INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS, 2005, 35 (09) : 807 - 816
  • [37] Yield Prediction Using Artificial Neural Networks
    Baral, Seshadri
    Tripathy, Asis Kumar
    Bijayasingh, Pritiranjan
    COMPUTER NETWORKS AND INFORMATION TECHNOLOGIES, 2011, 142 : 315 - +
  • [38] Estimation of compression strength of polypropylene fibre reinforced concrete using artificial neural networks
    Erdem, R. Tugrul
    Kantar, Erkan
    Gucuyen, Engin
    Anil, Ozgur
    COMPUTERS AND CONCRETE, 2013, 12 (05): : 613 - 625
  • [39] An Application of Artificial Neural Networks for Prediction and Comparison with Statistical Methods
    Balli, S.
    Tarimer, I.
    ELEKTRONIKA IR ELEKTROTECHNIKA, 2013, 19 (02) : 101 - 105
  • [40] Compression of Medical Images by Using Artificial Neural Networks
    Dokur, Zuemray
    INTELLIGENT COMPUTING, PART I: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, ICIC 2006, PART I, 2006, 4113 : 337 - 344
12345