Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials

Author:

Ahmad Mahmood12ORCID,Al-Mansob Ramez A.1ORCID,Kashyzadeh Kazem Reza3ORCID,Keawsawasvong Suraparb4ORCID,Sabri Sabri Mohanad Muayad5ORCID,Jamil Irfan6ORCID,Alguno Arnold C.7ORCID

Affiliation:

1. Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak, Selangor 50728, Malaysia

2. Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan

3. Department of Transport, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow 117198, Russia

4. Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand

5. Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia

6. Department of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan

7. Department of Physics, Mindanao State University-Iligan Institute of Technology, Iligan City 9200, Philippines

Abstract

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R2 = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R2 = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R2 = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R2 = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable.

Funder

Federal Target Program

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Reference41 articles.

1. Prediction of shear strength parameter of prototype rockfill material;S. Abbas;IGC-2003, Roorkee,2003

2. Constitative Modelling of Rockfill Materials;A. K. Gupta,2000

3. Prediction of Mechanical Behaviour of Rockfill Materials;K. Venkatachalam,1993

4. Large Scale Testing of Rockfill Materials

5. Strength and deformation, Characteristics of Rockfill Materials;N. D. Marachi,1969

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