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Vol. 169, Issue 4, April 2014, pp. 1-8




Applications of Wavelet Neural Network Model to Building Settlement Prediction:
A Case Study

1, 2 Qulin TAN, 1 Jian WEI, 1, 2 Jiping HU

1 Photogrammetric Engineering & Remote Sensing, School of Civil Engineering, Beijing Jiaotong University, Beijing, 100044, China
2 Beijing Key Laboratory of Track Engineering, Beijing Jiaotong University, Beijing 100044, China
1 Tel.: 086-010-51688207, fax: 086-010-51683764

E-mail: qltanbjtu@163.com


Received: 16 February 2014 /Accepted: 7 March 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems


Abstract: Deformation monitoring is a significant work for engineering safety, which is performed throughout the entire process of engineering design, construction and operation. Based on the theoretic analysis of wavelet and neural network, we applied the improved BP neural network model, auxiliary wavelet neural network model and embedded wavelet neural network model to the settlement prediction in one practical engineering monitoring project with MATLAB software programming. The cumulative and the interval settlement was predicted and compared with measured data. The overall performances of the three models were analyzed and compared. The results show that the accuracies of two kinds of wavelet neural network models are roughly the same, which prediction errors of monitoring points are less than 1mm, obviously superior to the single BP neural network model.


Keywords: Deformation, Settlement prediction, BP neural network, Wavelet analysis, Time-series analysis.


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