Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling-Reference-Cited by-同舟云学术

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Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling

Published:2016-01 Issue: Volume:51 Page:372-386
ISSN:0886-7798
Container-title:Tunnelling and Underground Space Technology
language:en
Short-container-title:Tunnelling and Underground Space Technology

Author:

Chen Dong-Fang,Feng Xia-Ting,Xu Ding-Ping,Jiang Quan,Yang Cheng-Xiang,Yao Pin-Pin

Funder

National Natural Science Foundation of China

Special Fund for Basic Scientific Research of Central Colleges, China

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Building and Construction

Reference31 articles.

1. Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses;Abbas;Int. J. Rock Mech. Min. Sci.,2010

2. Modeling soil collapse by artificial neural networks;Adnan;Geotech. Geol. Eng.,2004

3. Modeling mechanical layering effects on stability of underground openings in jointed sedimentary rocks;Bakun-Mazor;Int. J. Rock Mech. Min. Sci.,2009

4. Deformability of layered or jointed rock masses: analysis and comparison of different types of test;Bayly;Int. J. Rock Mech. Min. Sci.,1982

5. A new failure criterion for transversely isotropic rocks;Cazacu;Int. J. Rock Mech. Min. Sci.,1998

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