Compression Index and Compression Ratio Prediction by Artificial Neural Networks

  • Abbas Jawad Al-Taie, Ass. Prof. Dr. College of Engineering – University of Nahrain
  • Ahmed Faleh Al-Bayati, Instructor College of Engineering – University of Nahrain
  • Zahir Noori M. Taki, Ass. Lect. College of Engineering – University of Nahrain
Keywords: compression index, compression ratio, index properties, artificial neural network.

Abstract

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.

 

 

 

Downloads

Download data is not yet available.
Published
2017-11-26
How to Cite
Al-Taie, A., Al-Bayati, A. and Taki, Z. (2017) “Compression Index and Compression Ratio Prediction by Artificial Neural Networks”, Journal of Engineering, 23(12), pp. 96-106. Available at: http://joe.uobaghdad.edu.iq/index.php/main/article/view/406 (Accessed: 18February2020).