Prediction of artificial soil's unconfined compression strength test using statistical analyses and artificial neural networks
Küçük Resim Yok
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCI LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Artificial soil, Unconfined compression strength, Soil index properties, Artificial neural networks, Statistical analyses, Correlation
Kaynak
ADVANCES IN ENGINEERING SOFTWARE
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
41
Sayı
9
