Abstract
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.
Highlights
In civil engineering projects, such as rockfill dams, slopes, and embankments, rockfill materials (RFMs) are often used as filling materials
Waikato environment for knowledge analysis (WEKA) is an open-source software which consists of a collection of machine learning algorithms for data mining tasks
For the training results, based on the R2 (0.9224, 0.9430, and 0.9806), r (0.9604, 0.9711, and 0.9903), mean absolute error (MAE) (0.1565, 0.1015, and 0.0646), root mean square error (RMSE) (0.2219, 0.1605, and 0.0965), relative absolute error (RAE) (31.7002%, 20.5572%, and 13.0776%), and root relative squared error (RRSE) (31.7002%, 20.5572%, and 13.0776%), respectively, for Gaussian process regression (GPR)-RBF, GPRPoly, and GPR-Pearson universal kernel (PUK) models, the GPR-PUK outputs are verified to be the most compatible with actual RFM shear strength values
Summary
In civil engineering projects, such as rockfill dams, slopes, and embankments, rockfill materials (RFMs) are often used as filling materials. Quarried materials are angular to sub-angular, whereas riverbed materials are rounded to sub-rounded. Particle size, shape, gradation, individual particle strength, void content, relative density, and surface roughness of the particles all influence the behavior of the RFMs utilized in the construction of rockfill dams. Several studies in geotechnical engineering have been carried out, such as that examining the contact between the soils and concrete used in earth and rockfill dams [1]. Inverse analysis provides an means to better understand dam behavior [2], and offers measures of surface roughness, apparent porosity, apparent density, water absorption, and uniaxial compression strength (UCS), as well as an understanding of the influence of the heating rate and cooling process of gneiss stone [3]
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