DocumentCode :
3542359
Title :
Online monitoring system of slope based on the RBF neural network
Author :
Benqiang, Xi ; Bing, Liang ; Qiang, Xue
Author_Institution :
Sch. of Mech. & Eng., Liaoning Tech. Univ., Fuxin, China
fYear :
2009
fDate :
16-19 Aug. 2009
Firstpage :
21245
Lastpage :
21976
Abstract :
Monitoring the deformation and other useful physical parameters of slopes, either natural mountain slopes or those formed in open pit mining or construction, is one of the most effective methods to avoid or reduce the risk caused by landslides. Within this paper, a set of multi-point displacement sensors and anchor rod stress sensors was pre-installed in a slope. The information from the sensors was timely collected by an A/D converse module, and through a RS485 network the data in the collect module was then passed to the host computer, where the data about multi-point displacement, outside deformation and the load of anchors was analyzed. Applying RBF neural network, it can assess the stability of the slope and also can forecast landslides. Since monitoring system is a online one, all those processes can be done automatically, and the data can be taken anytime wanted.
Keywords :
geophysics computing; radial basis function networks; sensors; A/D converse module; RBF neural network; RS485 network; anchor rod stress sensor; multipoint displacement sensor; online monitoring system; slope stability; Computer networks; Computerized monitoring; Condition monitoring; Neural networks; Neurons; Safety; Soil; Stability; Stress; Terrain factors; RBF neural network; forecasting; slope safety monitoring; slope stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-3863-1
Electronic_ISBN :
978-1-4244-3864-8
Type :
conf
DOI :
10.1109/ICEMI.2009.5274251
Filename :
5274251
Link To Document :
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