Journal
GEOMECHANICS AND ENGINEERING
Volume 11, Issue 3, Pages 361-372Publisher
TECHNO-PRESS
DOI: 10.12989/gae.2016.11.3.361
Keywords
soil-concrete interface shear strength; modified direct shear test; LS-SVM; statistical prediction
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Funding
- State Key Laboratory for GeoMechanics and Deep Underground Engineering, CUMT [SKLGDUEK1502]
- National Science Foundation of China [51609072, 11402070]
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The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.
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