Modelling of shear strength of rockfills used for the construction of rockfill dams

Andjelkovic, Vladimir; Pavlović, Nenad D.; Lazarevic, Zarko; Radovanović, Slobodan

doi:10.1016/j.sandf.2018.04.002

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…The SVM achieved a better prediction performance with (R 2 = 0.9655, RMSE = 0.0513 and mean absolute error (MAE) = 0.0184) in comparison to the cubist method (R 2 = 0.9645, RMSE = 0.0975, and MAE = 0.0644) and ANN method (R 2 = 0.9386, RMSE = 0.1320 and MAE = 0.0841) reported by Zhou et al [34] and Kaunda [33], respectively, for the test data. Additionally, the accuracy of modeling determined by the linear regression method reported by Andjelkovic et al [58] between measured and calculated values of shear strength (R 2 = 0.836) was slightly lower than the proposed SVM model. In general, the generalization and reliability of the SVM algorithm perform well, and larger data sets can yield better prediction results.…”

Section: Resultsmentioning

confidence: 64%

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

et al. 2021

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Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R2 together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R2 = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R2 = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R2 = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM.

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Section: Resultsmentioning

confidence: 64%

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

et al. 2021

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“…Considering the respective values of R 2 (0.9334, 0.9411, and 0.9455), r (0.9661, 0.9701, and 0.9724), MAE (0.1395, 0.1092, and 0.1048), RMSE (0.1781, 0.1508, and 0.1443), RAE (29.0963%, 22.783%, and 21.8554%), and RRSE (29.2377%, 24.7641%, and 23.6865%), respectively, for the GPR-RBF, GPR-Poly, and GPR-PUK models, GPR-Poly is the second most precise model, and GPR-PUK has outperformed both GPR-RBF and GPR-Poly. In comparison with the ANN model (R 2 = 0.9386) and linear regression method (R 2 = 0.836) reported by Kaunda [ 27 ] and Andjelkovic et al [ 41 ], respectively, for the test data, the proposed GPR-PUK (R 2 = 0.9455) has better prediction capacity. In general, the generalization and reliability of the GPR-PUK perform well, and larger datasets can yield better prediction results.…”

Section: Resultsmentioning

confidence: 77%

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

et al. 2022

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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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“…The coarse-grained soil samples with 30%, 45%, 60%, and 75% coarse grain contents were conducted to consolidated drained triaxial compression tests using the DJSZ-150 coarse-grained soil triaxial testing machine. The density of the soil samples was 2.12 g/cm 3 , and the natural water content was 4.2%. First, the test soil material was air-dried, and then the particles were sieved.…”

Section: Test Apparatus and Testmentioning

confidence: 99%

“…Unlike common continuum materials, the strength and deformation properties of coarse-grained soils are significantly influenced by their gradation composition [2][3][4]. Due to the different proportions of coarse and fine particles, the structural characteristics of coarse-grained soils are significantly different, and their deformation failure resistance capacities vary.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Effect of Coarse Grain Content on the Dilatancy and Particle Breakage Characteristics of Coarse-Grained Soils

Xiong

Shen

et al. 2023

Geofluids

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The dilatancy deformation and particle breakage effects of coarse-grained soil have a significant influence on the safety and stability of high earth and rock-fill dams. To study the shear deformation and particle breakage characteristics of coarse-grained soil, four groups of soil sample with different coarse grain contents were to conduct consolidated drained triaxial compression tests by DJSZ-150 large-scale triaxial compression test machine. The results showed that the volume of coarse-grained soil changes significantly under deviation stress. The soil samples underwent positive dilatancy under the confining pressures of 200 kPa and 400 kPa but negative dilatancy under the confining pressures of 600 kPa and 800 kPa. When increasing the coarse grain contents in the soil sample under the same confining pressure, the positive dilatancy was more significant. A three-parameter dilatancy equation of coarse-grained soil considering confining pressure was established, in which the parameter L = 4 could serve as a criterion for whether the coarse-grained soil underwent positive dilatancy. Under the load, the coarse-grained soil showed three particle breakage modes: abrasion, attrition, and fracture. The coarse-grained soil particle size distribution before and after breakage conformed to the fractal characteristics, and the particle breakage rate increased significantly with larger soil particle size fractal dimension differences before and after the test. The coarse grain content and the confining pressure are two important factors affecting the mechanical properties of coarse-grained soil, and they control the shear deformation of soil.

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Modelling of shear strength of rockfills used for the construction of rockfill dams

Cited by 12 publications

References 16 publications

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

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

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

Experimental Study on the Effect of Coarse Grain Content on the Dilatancy and Particle Breakage Characteristics of Coarse-Grained Soils

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