Evaluation of ANFIS and Regression Techniques in Estimating Soil Compression Index for Cohesive soils


  • Yaseen Ahmed Hamaamin College of Engineering - University of Sulaimani, Sulaimani, Iraq
  • Kamal Ahmad Rashed College of Engineering - University of Sulaimani, Sulaimani, Iraq
  • Younis Mustafa Ali College of Engineering - University of Sulaimani, Sulaimani, Iraq
  • Tavga Aram Abdalla Salih College of Engineering - University of Sulaimani, Sulaimani, Iraq




ANFIS, Regression, Cohesive Soils, Compression Index


Generally, direct measurement of soil compression index (Cc) is expensive and time-consuming. To save time and effort, indirect methods to obtain Cc may be an inexpensive option. Usually, the indirect methods are based on a correlation between some easier measuring descriptive variables such as liquid limit, soil density, and natural water content. This study used the ANFIS and regression methods to obtain Cc indirectly. To achieve the aim of this investigation, 177 undisturbed samples were collected from the cohesive soil in Sulaymaniyah Governorate in Iraq. Results of this study indicated that ANFIS models over-performed the Regression method in estimating Cc with R2 of 0.66 and 0.48 for both ANFIS and Regression models, respectively. This work is an effort to practice the advantages of machine learning techniques to build a robust and cost-effective model for Cc estimation by designers, decision makers, and stakeholders.


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How to Cite

Yaseen Ahmed Hamaamin, Kamal Ahmed Rashed, Younis Mustafa Ali and Aram Abdalla, T. (2022) “Evaluation of ANFIS and Regression Techniques in Estimating Soil Compression Index for Cohesive soils”, Journal of Engineering, 28(10), pp. 28–41. doi: 10.31026/j.eng.2022.10.03.