NDLI: Application of improved BP neural network in predicting subsidence of buildings

Content Provider	IEEE Xplore Digital Library
Author	Bo Fu Zhenhong Xie
Copyright Year	2012
Description	Author affiliation: School of Surveying and Prospecting Engineering, Jilin Institute of Architecture and Civil Engineering, Changchun, China (Bo Fu; Zhenhong Xie)
Abstract	With the increase of engineering construction, the safety of engineering became a realistic problem of great importance. As a major technical means, prediction of deformation monitoring was extremely important for construction and operation of buildings. In this paper, an improved BP neural network was adopted and a nonlinear coefficient was added in the response function of the neurons to enhance the approximation performance of the network. The cumulative subsidence of the 40th to the 43rd interval was predicted and compared with the measured data through training the subsidence monitoring data of total 35 intervals of 14 monitoring points of presupposition around Changchun Science & Technology Museum as sample data. The 1-5-4 network model based on improved BP network reached error demands by 4428 times training. The result showed that the prediction errors of 11 monitoring points were less than 0.5mm and the model performed well.
Starting Page	389
Ending Page	392
File Size	222011
Page Count	4
File Format	PDF
ISBN	9781457721304
ISSN	21579563
e-ISBN	9781457721335
DOI	10.1109/ICNC.2012.6234641
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	nonlinear coefficient Back-Propagation neural network Buildings Neurons Artificial neural networks Predictive models prediction Mathematical model Monitoring Biological neural networks subsidence monitoring
Content Type	Text
Resource Type	Article

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