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

Mahmood Ahmad, Ramez A Al-Mansob, Irfan Jamil, Mohammad A Al-Zubi, Mohanad Muayad Sabri Sabri, Arnold C Alguno

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Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Mahmood AhmadDepartment of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak 50728, Selangor, Malaysia.ORCID 0000-0002-1139-1831

Ramez A Al-MansobDepartment of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak 50728, Selangor, Malaysia.

Irfan JamilDepartment of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan.ORCID 0000-0003-2942-6614

Mohammad A Al-ZubiDepartment of Mechanical Engineering, Hijjawai Faculty for Engineering, Yarmouk University, Irbid 21163, Jordan.ORCID 0000-0001-8809-5283

Mohanad Muayad Sabri SabriPeter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.ORCID 0000-0003-3154-8207

Arnold C AlgunoDepartment of Physics, Mindanao State University-Iligan Institute of Technology, Iligan City 9200, Philippines.ORCID 0000-0002-6402-6184

Funding

Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program 'Priority 2030' Agreement 075-15-2021-1333 dated 30.09.2021

6 · The paper itself

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 (R

Indexed as

Gaussian functionsPearson universal kernelpolynomial kernelradial basis functionrockfill materialsshear strength

Identifiers

PMID35268965

DOI10.3390/ma15051739

PMCPMC8911239

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.