Deformation prediction of foundation pit with PCA-SVM
- https://doi.org/10.2991/icadme-15.2015.415How to use a DOI?
- Support vector machine Principal component analysis Horizontal displacement Prediction model
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.
- © 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 -