DocumentCode :
518752
Title :
Prediction of seawall foundation settlement based on the improved variable dimension fraction and artificial neural network model
Author :
Peng, Qin ; Zhihai, Qin
Author_Institution :
Dept. of Hydraulic Eng., Zhejiang Water Conservancy & Hydropower Coll., Hangzhou, China
Volume :
4
fYear :
2010
fDate :
27-29 March 2010
Firstpage :
347
Lastpage :
350
Abstract :
Prediction of the seawall foundation settlement is important to the engineering maintenance and disaster prevention. A new method based on the improved variable dimension fraction (IVDF) and artificial neural network (ANN) was presented on the example of the seawall located in Zhejiang Province of China. The settlement displacement analysis for a single point located on the seawall was performed. The analysis consists of three stages: idea of IVDF - ANN model analysis, IVDF-ANN modeling, and deformation forecast. The result proves that IVDF-ANN model makes good use of the self-similarity of fractal theory and the self-learning ability of artificial neural network, and the method has a degree of applicability.
Keywords :
deformation; foundations; neural nets; structural engineering computing; IVDF - ANN model analysis; artificial neural network; deformation forecasting; disaster prevention; engineering maintenance; fractal theory self-similarity; improved variable dimension fraction; seawall foundation settlement; seawall foundation settlement prediction; self-learning ability; settlement displacement analysis; Accuracy; Artificial neural networks; Deformable models; Educational institutions; Fractals; Hydroelectric power generation; Monitoring; Performance analysis; Predictive models; Water conservation; artificial neural network; improved variable dimension fractal; prediction; seawall; settlement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-5845-5
Type :
conf
DOI :
10.1109/ICACC.2010.5486909
Filename :
5486909
Link To Document :
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