Proceedings of the 2017 International Conference on Advanced Materials Science and Civil Engineering (AMSCE 2017)

Quantile Regression Analysis on Short-Term Measured Data of Gravity-Arch Dam Based on POD

Authors
Weihua Fang, Yunping Chen, Hui Zhang, Lin Li
Corresponding Author
Weihua Fang
Available Online April 2017.
DOI
10.2991/amsce-17.2017.33How to use a DOI?
Keywords
POD; short-time series; quantile regression; gravity-arch dam
Abstract

To solve the problems of little sample, multi-collinearity and bad robust ability of normal model remaining in measured dam data in process of analysis, This paper analyzed the monitoring data of measured dam crest crown cantilever and both sides of 1/4 arch of a gravity dam in 2013 using circannual monitoring data. The research shows that quantile regression analysis method based on POD can conquer the problems above when analyzing measured dam data and excavate more safety dam information.

Copyright
© 2017, 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 2017 International Conference on Advanced Materials Science and Civil Engineering (AMSCE 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-337-1
ISSN
2352-5401
DOI
10.2991/amsce-17.2017.33How to use a DOI?
Copyright
© 2017, 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  - Weihua Fang
AU  - Yunping Chen
AU  - Hui Zhang
AU  - Lin Li
PY  - 2017/04
DA  - 2017/04
TI  - Quantile Regression Analysis on Short-Term Measured Data of Gravity-Arch Dam Based on POD
BT  - Proceedings of the 2017 International Conference on Advanced Materials Science and Civil Engineering (AMSCE 2017)
PB  - Atlantis Press
SP  - 142
EP  - 149
SN  - 2352-5401
UR  - https://doi.org/10.2991/amsce-17.2017.33
DO  - 10.2991/amsce-17.2017.33
ID  - Fang2017/04
ER  -