Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques

Ahmad, Mahmood; Kamiński, Paweł; Olczak, Piotr; Alam, Muhammad; Iqbal, Muhammad Junaid; Ahmad, Feezan; Sasui, Sasui; Khan, Beenish Jehan

doi:10.3390/app11136167

Cited by 32 publications

(22 citation statements)

References 57 publications

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“…The sensitivity results of the GPR model were examined using Yang and Zang's approach for determining the impact of input variables on D H . This strategy, which has been used in a number of research [44][45][46][47], is as follows:…”

Section: Sensitivity Analysismentioning

confidence: 99%

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

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During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement.

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Section: Sensitivity Analysismentioning

confidence: 99%

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

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“…It was accomplished through the use of a trial-and-error strategy. Previous studies show that the shear strength (

) of RFM is a function of D 10 , D 30 , D 60 , and D 90 , which correspond to the 10%, 30%, 60%, and 90% passing sieve sizes, while UCS min and UCS max (MPa) indicate the minimum and maximum uniaxial compressive strengths (MPa), the FM and GM parameters describe fineness modulus and gradation modulus, respectively, γ is the dry unit weight (kN/m 3 ), σ n is the normal stress (MPa), and R shows the International Society of Rock Mechanics (ISRM) hardness rating [ 27 , 29 ]. As a result, the current study’s GPR models are constructed using these input variables.…”

Section: Data Catalog and Correlation Analysismentioning

confidence: 99%

“…Zhou et al [ 28 ] has recently shown that cubist and random forest regression algorithms are better at predicting RFM shear strength results than ANN and traditional regression models. To predict RFMs’ shear strength, Ahmad et al [ 29 ] used support vector machine, random forest, adaptive boosting, and k-nearest neighbor algorithms. This field, on the other hand, is still being researched and further explored.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

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The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R2) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R2 = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.

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“…A recently developed approach based on data mining techniques has been increasingly employed to resolve real-world problems for the past half-decade, particularly in the field of civil engineering [18][19][20][21][22][23][24][25][26][27][28]. Several practical problems have already been effectively performed using machine learning algorithms, paving the way for new prospects in the construction industry.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

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The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate predictions however the XGBoost algorithm surpasses others, followed by AdaBoost, RF, DT, and SVM. The sensitivity analysis result shows that the SPT blow count along the pile shaft has the greatest effect on the Pu.

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Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques

Cited by 32 publications

References 57 publications

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

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