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

页码：1115 / 1123

页数：9

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