Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

Cai Yun Gao; Xi Min Cui; Xue Qian Hong

doi:10.4028/www.scientific.net/AMM.501-504.2149

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Research on Soft Soil Model and Subsidence Coefficient of Coastal Zone of Yingkou
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Study on the Applications of Neural Networks for Processing Deformation Monitoring Data
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 501-504Study on the Applications of Neural Networks for...

Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

Abstract:

Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.

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Periodical:

Applied Mechanics and Materials (Volumes 501-504)

Pages:

2149-2153

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.501-504.2149

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Online since:

January 2014

Authors:

Cai Yun Gao, Xi Min Cui, Xue Qian Hong

Keywords:

Accuracy, Adaptability, Artificial Neural Network (ANN), Deformation, Prediction

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