Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications

Slope anchor cable life evolution model and prediction

Authors
Jing Wang, Jiwei Liu, Bing Liu
Corresponding Author
Jing Wang
Available Online May 2016.
DOI
10.2991/wartia-16.2016.101How to use a DOI?
Keywords
Slope anchor, life prediction, reliability theory, SVM
Abstract

After a comprehensive analysis of the characteristics of slope anchor structure, a mature reliability theory model in durable life prediction field is applied to predict the life of this structure, and support vector machine method is used to solve the probability density estimation for predicting factors in the prediction model in case of a small sample; therefore, the availability of reliability theory life prediction method is improved in case of a small sample. Finally, reliability theory and K-S test method are used to predict and test the life based on the sample data from Qinshan Nuclear Power Plant.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
Series
Advances in Engineering Research
Publication Date
May 2016
ISBN
10.2991/wartia-16.2016.101
ISSN
2352-5401
DOI
10.2991/wartia-16.2016.101How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jing Wang
AU  - Jiwei Liu
AU  - Bing Liu
PY  - 2016/05
DA  - 2016/05
TI  - Slope anchor cable life evolution model and prediction
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 500
EP  - 507
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.101
DO  - 10.2991/wartia-16.2016.101
ID  - Wang2016/05
ER  -