DocumentCode :
3370182
Title :
Foundation pit multi-point displacement RBF monitoring model and application key points
Author :
Ming, Huang ; Jun, Liu ; Ning, Wang
Author_Institution :
Sch. of Civil & Hydraulic Eng., Hefei Univ. of Technol., Hefei, China
fYear :
2010
fDate :
26-28 June 2010
Firstpage :
4562
Lastpage :
4565
Abstract :
To know deep foundation pit state and ensure its safety, multi monitoring points data are used to establish model in a quick and useful way. Considering grading excavation effect, time effect and synchronous multi-point displacement monitoring information, the frame of Radial basis function artificial neural network (RBF) multi-point monitoring model has been structured. The concise effect factors base on Genetic Creep Theory are studied to forecast multi-point displacement. After analyzing the action of RBF centers in this network, preselected centers considering the grading excavation and curve characteristic of displacement have been used in calculation. Monitoring data from a deep foundation pit are used to establish multi-point model base on the multi-point model frame, concise effect factors and preselected centers. Analyses show that the results of instance are very good. Application suggestions are also presented combining RBF characteristics and monitoring practice.
Keywords :
foundations; radial basis function networks; safety; foundation pit multi-point displacement RBF monitoring model; genetic creep theory; grading excavation effect; radial basis function artificial neural network; synchronous multi-point displacement monitoring information; time effect; Artificial neural networks; Civil engineering; Creep; Data engineering; Genetics; Marine safety; Marine technology; Monitoring; Oceans; Project management; RBF; deep foundation pit; key points of application; monitoring model frame; multi-point displacement model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7737-1
Type :
conf
DOI :
10.1109/MACE.2010.5536847
Filename :
5536847
Link To Document :
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