Horizontal Displacement Prediction Research of Deep Foundation Pit Based on the Least Square Support Vector Machine
Wei-dong Li, Meng-Hong Wu, Nan Lin
Available Online December 2016.
- https://doi.org/10.2991/icwcsn-16.2017.81How to use a DOI?
- Least square support vector machine; Deep foundation pit; Horizontal displacement; prediction.
- 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.
- 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 BT - 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016) PB - Atlantis Press 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 -