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IJSCER 2022 Vol.11(2): 42-45
doi: 10.18178/ijscer.11.2.42-45

The Prediction of Permanent Deformation of Fine-Grained Soils Using Multiple Linear Regression: Dummy Variables

Mohammad Ali Khasawneh 1 and Rabea Saleh AL-Jarazi 2
1. Civil Engineering, Prince Mohammad Bin Fahd University, Al Azeziya, Eastern Province, Kingdom of Saudi Arabia
2. Civil Engineering Department, Jordan University of Science and Technology, Irbid, Jordan

Manuscript received July 2, 2021; revised October 21, 2021; accepted January 25, 2022; issue published April 27, 2022.

Abstract—Under repeated traffic loading, knowledge and understanding of cumulative permanent deformation and failure mechanisms for subgrade soils (fine-grained soils) are crucial for the proper design and maintenance planning of pavement structures. In other words, considering the great contribution of subgrade soils to the overall performance of pavement structures, it is crucial to provide the best prediction of permanent deformation behavior. This paper presents a new predictive equation for the permanent deformation of fine-grained soils (A-4a and A-6a soils) utilizing the dummy-variable multiple linear regression technique. The permanent deformation (PD) results revealed that A-4a at OMC exhibited the least plastic deformation versus the highest plastic deformation assigned to A-6a compacted at 2% wet of OMC. The results obtained could be used to help engineers in characterizing fine-grained materials. As per the statistical analysis carried out in this study, the dummy regression for permanent deformation did not greatly improve the prediction power of the model.  
Index Terms—pavements, fine-grained soils, permanent deformation, dummy regression

Cite: Mohammad Ali Khasawneh and Rabea Saleh AL-Jarazi, "The Prediction of Permanent Deformation of Fine-Grained Soils Using Multiple Linear Regression: Dummy Variables," International Journal of Structural and Civil Engineering Research, Vol. 11, No. 2, pp. 42-45, May 2022. doi: 10.18178/ijscer.11.2.42-45

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.