Analysis of Neural Network Models in Prediction of Ground Surface Settlement around Deep Foundation Pit
- DOI
- 10.2991/icache-15.2015.81How to use a DOI?
- Keywords
- prediction of ground surfacesettlement; deep foundation pit; PSO-BP neural network; GA-BP neural network;GRNN neural network
- Abstract
During the foundation pit excavation,the prediction of ground surface settlement around deep foundation pit is directly related to the safety of the foundation pit excavation, surrounding buildings and pipelines, but the ground surface settlement of foundation pit has the characteristics of nonlinear and fuzzy. So it is necessary to monitor and predict the excavation settlement according to the excavation conditions, the surrounding environment, security level and other buildings around. Neural networkcan simulate any unknown system of complex polygene conveniently and high precision. GRNN and two improved BP neural network prediction models are established to predictsettlement in this paper. The ground surfacesettlement around a deep foundation pit is predicted with all main influential factors being taken into account properly. The three neural network prediction models—GRNN, PSO-BP and GA-BPpredictionmodel are analyzed in principle and network architecture design.And they are used to predict ground surface settlement for an engineering example in Beijing. The prediction results show that neural network have high feasibility and reliabilityin predicting ground surface settlement around deep foundation pit, and neural network will have better application prospect in the field of geotechnical in-situ testing & monitoring.
- 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 - Fuzhang Zhao AU - Chen Chen AU - Fang Qian PY - 2015/11 DA - 2015/11 TI - Analysis of Neural Network Models in Prediction of Ground Surface Settlement around Deep Foundation Pit BT - Proceedings of the 2015 International Conference on Architectural, Civil and Hydraulics Engineering PB - Atlantis Press SP - 418 EP - 424 SN - 2352-5401 UR - https://doi.org/10.2991/icache-15.2015.81 DO - 10.2991/icache-15.2015.81 ID - Zhao2015/11 ER -