Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering

Deformation prediction of foundation pit with PCA-SVM

Authors
Nan Lin, Menghong Nan, Weidong Li
Corresponding Author
Nan Lin
Available Online October 2015.
DOI
https://doi.org/10.2991/icadme-15.2015.415How to use a DOI?
Keywords
Support vector machine Principal component analysis Horizontal displacement Prediction model
Abstract

Using the principal component (PCA) strong ability of extracting effective features of foundation pit horizontal displacement monitoring data (monitoring, monitoring temperature, relative humidity, excavation depth) characteristics analysis .extracting the effective principal component, constructing the PCA SVM regression prediction model, and the analysis result through comparing with the measured values show that: the displacement data of prediction model based on PCA and SVM was more higher accuracy than a model using SVM, higher reliability, which means has certain applicability in engineering application.

Copyright
© 2015, 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 5th International Conference on Advanced Design and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2015
ISBN
978-94-6252-113-1
ISSN
2352-5401
DOI
https://doi.org/10.2991/icadme-15.2015.415How to use a DOI?
Copyright
© 2015, 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  - Nan Lin
AU  - Menghong Nan
AU  - Weidong Li
PY  - 2015/10
DA  - 2015/10
TI  - Deformation prediction of foundation pit with PCA-SVM
BT  - Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering
PB  - Atlantis Press
SP  - 2226
EP  - 2230
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-15.2015.415
DO  - https://doi.org/10.2991/icadme-15.2015.415
ID  - Lin2015/10
ER  -