Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

Horizontal Displacement Prediction Research of Deep Foundation Pit Based on the Least Square Support Vector Machine

Authors
Wei-dong Li, Meng-Hong Wu, Nan Lin
Corresponding Author
Wei-dong Li
Available Online December 2016.
DOI
https://doi.org/10.2991/icwcsn-16.2017.81How to use a DOI?
Keywords
Least square support vector machine; Deep foundation pit; Horizontal displacement; prediction.
Abstract
Using of the least square support vector machine to predict the horizontal displacement of deep foundation pit. According to the measured time series data of horizontal displacement of foundation pit, using the least square support vector machine (SVM) to set up the relation model of foundation pit horizontal displacement and time, taking the actual excavation monitoring data as learning and training samples and testing samples, the calculated results and the actual monitoring results were compared and analyzed. The results show that using the least squares support vector machine (SVM) to predict the horizontal displacement of foundation pit, which was with higher prediction accuracy, the method with prediction error is small, fast calculation, less data, etc., precision can satisfy the need of engineering. The method Confirmed that is an effective method to solve the problem of the foundation pit deformation prediction.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-302-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icwcsn-16.2017.81How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wei-dong Li
AU  - Meng-Hong Wu
AU  - Nan Lin
PY  - 2016/12
DA  - 2016/12
TI  - Horizontal Displacement Prediction Research of Deep Foundation Pit Based on the Least Square Support Vector Machine
PB  - Atlantis Press
SP  - 379
EP  - 382
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.81
DO  - https://doi.org/10.2991/icwcsn-16.2017.81
ID  - Li2016/12
ER  -