DocumentCode :
510053
Title :
Artificial Neural Network´s Application in Intelligent Prediction of Enclosure Structure Deformation Induced by Foundation Pit Excavation
Author :
Chen Haiming ; Zhou Shengquan ; Yang Zhao ; Liu Cheng
Author_Institution :
Sch. of Civil Eng. & Archit., Anhui Univ. of Sci. & Technol., Huainan, China
Volume :
2
fYear :
2009
fDate :
7-8 Nov. 2009
Firstpage :
507
Lastpage :
510
Abstract :
Enclosure structure deformation induced by foundation pit excavation is one of important problems in foundation pit engineering. The factors influencing the enclosure structure deformation are complex and have stochastic and fuzzy properties, which have brought difficulties to the enclosure structure deformation prediction. The traditional methods are obviously inadequate while being used to predict the enclosure structure deformation. In this paper, the intelligent prediction method based on BP artificial neural network (ANN) and multi-step circulation is established and can solve these problems. Practices show that the accuracy of the method can meet the application requirements; the method has a few features, such as, with high efficiency, use of convenience, suitable for engineering applications, and so on, and has certain engineering and technical value.
Keywords :
backpropagation; deformation; foundations; neural nets; structural engineering computing; BP artificial neural network; enclosure structure deformation prediction; foundation pit engineering; foundation pit excavation; fuzzy properties; intelligent prediction method; stochastic properties; Artificial intelligence; Artificial neural networks; Competitive intelligence; Design engineering; Electronic mail; Intelligent networks; Intelligent structures; Intelligent systems; Monitoring; Prediction methods; ANN; foundation pit; intelligent predication; multi-step circulation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
Type :
conf
DOI :
10.1109/AICI.2009.375
Filename :
5375890
Link To Document :
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