Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations-Reference-Cited by-同舟云学术

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Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations

Published:2021-07-14 Issue: Volume: Page:
ISSN:1861-1125
Container-title:Acta Geotechnica
language:en
Short-container-title:Acta Geotech.

Author:

Zhang Pin,Yin Zhen-Yu,Jin Yin-Fu,Liu Xian-Feng

Funder

Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China

Joint research project between SiChuan Province and National Universities funded by Science & Technology Department of Sichuan Province

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s11440-021-01170-4.pdf

Reference90 articles.

1. Badawy MF, Msekh MA, Hamdia KM, Steiner MK, Lahmer T, Rabczuk T (2017) Hybrid nonlinear surrogate models for fracture behavior of polymeric nanocomposites. Probabilist Eng Mech 50:64–75

2. Basheer IA (2000) Selection of methodology for neural network modeling of constitutive hystereses behavior of soils. Comput-Aided Civ Inf 15:440–458

3. Cabalar AF, Cevik A (2011) Triaxial behavior of sand–mica mixtures using genetic programming. Expert Syst Appl 38(8):10358–10367

4. Chang CS, Hicher PY (2005) An elasto-plastic model for granular materials with microstructural consideration. Int J Solids Struct 42(14):4258–4277

5. Chen RP, Zhang P, Kang X, Zhong ZQ, Liu Y, Wu HN (2019) Prediction of maximum surface settlement caused by EPB shield tunneling with ANN methods. Soils Found 59(2):284–295

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