Compression index and compression ratio prediction by artificial neural networks
Other Title(s)
التنبؤ بمؤشر و نسبة الانضغاط بواسطة الشبكات العصبية الاصطناعية
Joint Authors
al-Bayati, Ahmad Falih
Taqi, Zahir Nuri Muhammad
al-Tai, Abbas Jawad
Source
Issue
Vol. 23, Issue 12 (31 Dec. 2017), pp.96-106, 11 p.
Publisher
University of Baghdad College of Engineering
Publication Date
2017-12-31
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Information about soil consolidation is essential in geotechnical design.
Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties.
Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations.
The aim of this study is to investigate the consolidation and physical properties of the cohesive soil.
Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties.
One hundred and ninety five consolidation results for soils tested at different construction sites in Baghdad city were used.
70% of these results were used to train the prediction ANN models and the rest were equally divided to test and validate the ANN models.
The performance of the developed models was examined using the correlation coefficient R.
The final models have demonstrated that the ANN has capability for acceptable prediction of compression index and compression ratio.
Two equations were proposed to estimate compression index using the connecting weights algorithm, and good agreements with test results were achieved.
American Psychological Association (APA)
al-Tai, Abbas Jawad& al-Bayati, Ahmad Falih& Taqi, Zahir Nuri Muhammad. 2017. Compression index and compression ratio prediction by artificial neural networks. Journal of Engineering،Vol. 23, no. 12, pp.96-106.
https://search.emarefa.net/detail/BIM-796420
Modern Language Association (MLA)
al-Tai, Abbas Jawad…[et al.]. Compression index and compression ratio prediction by artificial neural networks. Journal of Engineering Vol. 23, no. 12 (Dec. 2017), pp.96-106.
https://search.emarefa.net/detail/BIM-796420
American Medical Association (AMA)
al-Tai, Abbas Jawad& al-Bayati, Ahmad Falih& Taqi, Zahir Nuri Muhammad. Compression index and compression ratio prediction by artificial neural networks. Journal of Engineering. 2017. Vol. 23, no. 12, pp.96-106.
https://search.emarefa.net/detail/BIM-796420
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 101-106
Record ID
BIM-796420