Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

Mahmood Ahmad, Suraparb Keawsawasvong, Mohd Rasdan Bin Ibrahim, Muhammad Waseem, Kazem Reza Kashyzadeh and Mohanad Muayad Sabri Sabri
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Mahmood Ahmad: Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
Suraparb Keawsawasvong: Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand
Mohd Rasdan Bin Ibrahim: Center for Transportation Research, Department of Civil Engineering, Engineering Faculty, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Muhammad Waseem: Department of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan
Kazem Reza Kashyzadeh: Department of Transport, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
Mohanad Muayad Sabri Sabri: Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia

Sustainability, 2022, vol. 14, issue 14, 1-15

Abstract: In the design stage of construction projects, determining the soil permeability coefficient is one of the most important steps in assessing groundwater, infiltration, runoff, and drainage. In this study, various kernel-function-based Gaussian process regression models were developed to estimate the soil permeability coefficient, based on six input parameters such as liquid limit, plastic limit, clay content, void ratio, natural water content, and specific density. In this study, a total of 84 soil samples data reported in the literature from the detailed design-stage investigations of the Da Nang–Quang Ngai national road project in Vietnam were used for developing and validating the models. The models’ performance was evaluated and compared using statistical error indicators such as root mean square error and mean absolute error, as well as the determination coefficient and correlation coefficient. The analysis of performance measures demonstrates that the Gaussian process regression model based on Pearson universal kernel achieved comparatively better and reliable results and, thus, should be encouraged in further research.

Keywords: soil permeability coefficient; Gaussian process regression; Pearson universal kernel; radial basis function; polynomial (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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