Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)-Reference-Cited by-同舟云学术

Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)

Published:1999-04 Issue:3 Volume:24 Page:207-230
ISSN:0266-352X
Container-title:Computers and Geotechnics
language:en
Short-container-title:Computers and Geotechnics

Author:

Penumadu Dayakar,Zhao Rongda

Publisher

Elsevier BV

Subject

Computer Science Applications,Geotechnical Engineering and Engineering Geology

Reference44 articles.

1. Al-Hussaini MM. Investigation of plane strain shear testing. Report 2. Drained plane strain and triaxial compression tests on crushed Napa basalt. Tech. Reports-71-2, Army Engr. Waterways Exp. Sta., Vicksburg, MI, 1971.

2. Al-Hussaini MM. Investigation of plane strain shear testing. Report 3. Plane strain and triaxial compression tests on painted rock dam material. Tech. Reports-71-2, Army Engr. Waterways Exp. Sta., Vicksburg, MI, 1972.

3. Alva-Hurtado, Selig ET. Permanent strain behavior of railroad ballast. In Proceedings of the 10th International Conference on Soil Mech. And Found. Engrg. Vol. Stockholm, Sweden, 1981, p. 543–46.

4. Becker E, Chan CK, Seed HB. Strength and deformation characteristics of rockfill materials in plane strain and triaxial compression tests. Report No. TE72-3, Univ. of California, Berkeley, CA, 1972.

Cited by 127 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites;Computer Methods in Applied Mechanics and Engineering;2024-07

2. Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable;Journal of Rock Mechanics and Geotechnical Engineering;2024-06

3. Integrated database of granular soils under triaxial shear and its application in the prediction of stress–strain relationship;IOP Conference Series: Earth and Environmental Science;2024-05-01

4. Modeling the Mechanical Response of Cement-Admixed Clay Under Different Stress Paths Using Recurrent Neural Networks;International Journal of Geosynthetics and Ground Engineering;2024-03-09

5. Deep transfer learning-aided constitutive modelling of granular soils considering out-of-range particle morphology;Tunnelling and Underground Space Technology;2024-02