Application of artificial neural networks to predict soil recompression index and recompression ratio
Joint Authors
al-Bayati, Ahmad Falih
al-Tai, Abbas Jawad
Source
Issue
Vol. 9, Issue 4 (31 Oct. 2018), pp.246-257, 12 p.
Publisher
University of Kufa Faculty of Engineering
Publication Date
2018-10-31
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Abstract EN
Overconsolidated soils are widely encountered in practice where settlement calculations are crucial.
The recompression index (Cr) and the recompression ratio (Cr) are considered as one of the most important parameters used in settlement calculations.
To achieve this purpose, expensive and time-consuming laboratory tests are usually conducted using undisturbed specimens to obtain the values of these parameters.
Various equations derived from regression analysis were proposed to predict consolidation parameters from the physical properties of a soil.
In this paper, however, an artificial neural network model (ANN) is proposed to predict Cr and Cr using natural water content, initial void ratio, total unit weight and effective overburden pressure.
The proposed ANN model achieved good agreement with the results of one hundred seventy-nine standard one-dimensional consolidation tests collected from previous geotechnical investigations in Baghdad.
American Psychological Association (APA)
al-Tai, Abbas Jawad& al-Bayati, Ahmad Falih. 2018. Application of artificial neural networks to predict soil recompression index and recompression ratio. Kufa Journal of Engineering،Vol. 9, no. 4, pp.246-257.
https://search.emarefa.net/detail/BIM-901806
Modern Language Association (MLA)
al-Tai, Abbas Jawad& al-Bayati, Ahmad Falih. Application of artificial neural networks to predict soil recompression index and recompression ratio. Kufa Journal of Engineering Vol. 9, no. 4 (Oct. 2018), pp.246-257.
https://search.emarefa.net/detail/BIM-901806
American Medical Association (AMA)
al-Tai, Abbas Jawad& al-Bayati, Ahmad Falih. Application of artificial neural networks to predict soil recompression index and recompression ratio. Kufa Journal of Engineering. 2018. Vol. 9, no. 4, pp.246-257.
https://search.emarefa.net/detail/BIM-901806
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 254-257
Record ID
BIM-901806