Full Text:   <2304>

CLC number: TV3

On-line Access: 2012-08-30

Received: 2012-05-07

Revision Accepted: 2012-08-13

Crosschecked: 2012-08-17

Cited: 4

Clicked: 5376

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2012 Vol.13 No.9 P.687-699


Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

Author(s):  Xing Liu, Zhong-ru Wu, Yang Yang, Jiang Hu, Bo Xu

Affiliation(s):  State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; more

Corresponding email(s):   zrwu@hhu.edu.cn

Key Words:  Dam monitoring, Diagnosis, Early-warning, Multi-source information fusion, Information entropy

Xing Liu, Zhong-ru Wu, Yang Yang, Jiang Hu, Bo Xu. Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams[J]. Journal of Zhejiang University Science A, 2012, 13(9): 687-699.

@article{title="Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams",
author="Xing Liu, Zhong-ru Wu, Yang Yang, Jiang Hu, Bo Xu",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams
%A Xing Liu
%A Zhong-ru Wu
%A Yang Yang
%A Jiang Hu
%A Bo Xu
%J Journal of Zhejiang University SCIENCE A
%V 13
%N 9
%P 687-699
%@ 1673-565X
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1200122

T1 - Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams
A1 - Xing Liu
A1 - Zhong-ru Wu
A1 - Yang Yang
A1 - Jiang Hu
A1 - Bo Xu
J0 - Journal of Zhejiang University Science A
VL - 13
IS - 9
SP - 687
EP - 699
%@ 1673-565X
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1200122

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service. Current analysis methods used to obtain safety monitoring data are suited only to single survey point data. Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data, thereby causing difficulty in acquiring precise conclusions. Therefore, we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams. In addition, we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams. Data metrics reduction is achieved using information fusion at the data level. A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis. early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance. A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points. It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Bao, T.F., Wu, Z.R., Gu, C.S., 2008. Influence of fractality of fracture surfaces on stress and displacement fields at crack tips. Science China Serial E-Technological Sciences, 51(Supp II):95-100.

[2]Chen, S.M., 2009. A New Method to Forecast Enrollments Using Fuzzy Time Series and Clustering Techniques. International Conference on Machine Learning and Cybernetics, Baoding, China, p.3026-3029.

[3]De Sortis, A., Paoliani, P., 2007. Statistical analysis and structural identification in concrete dam monitoring. Engineering Structures, 29(1):110-120.

[4]Friedman, N., Linial, M., Nachman, I., 2000. Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7(3-4):601-620.

[5]Hecht-Nielsen, R., 1989. Theory of the Back Propagation Neural Network. International Joint Conference on Neural Networks, Washington DC, USA, p.593-605.

[6]Huang, H.W., Yang, J.N., Zhou, L., 2010. Comparison of various structural damage tracking techniques based on experimental data. Smart Structure and Systems, 6(9):1057-1077.

[7]Kim, H.S., Melhem, H., 2004. Damage detection of structures by wavelet analysis. Engineering Structures, 26(3):347- 362.

[8]Leger, P., Leclerc, M., 2007. Hydrostatic, temperature, time- displacement model for concrete dams. Journal of Engineering Mechanics, 133(3):267-277.

[9]Mata, J., 2011. Methods of analysis for the prediction and the verification of dam behavior. Engineering Structures, 33(3):903-910.

[10]Otsu, N., 1979. A threshold selection method from gray level histograms. IEEE Transactions on Systems Man and Cybernetics, 9(1):62-66.

[11]Su, H.Z., 2003. Intelligent Sensing and Fusion System and Its Application to Dam Safety Monitoring. MS Thesis, Hohai University, Nanjing, China (in Chinese).

[12]Su, H.Z., Wu, Z.R., Wen, Z.P., 2007. Identification model for dam behavior based on wavelet network. Computer-Aided Civil and Infrastructure Engineering, 22(6):438-448.

[13]Trivedi, H.V., Singh, J.K., 2005. Application of grey system theory in the development of a runoff prediction model. Biosystems Engineering, 92(4):521-526.

[14]Wu, Z.R., Su, H.Z., 2005. Dam health diagnosis and evaluation. Smart Materials and Structures, 14(3):130-136.

[15]Wu, Z.R., Gu, C.S., 2006. Safety Diagnosis and Hidden Defects Detection of Major Hydraulic Concrete Structures. Higher Education Press, Beijing, China, p.1-4 (in Chinese).

[16]Wu, Z.R., Li, J., Gu, C.S., 2007. Review on hidden trouble detection and health diagnosis of hydraulic concrete structures. Science China Serial E-Technological Sciences, 50(1):34-50.

[17]Wu, H.Y., Zhou, Z.Y., Xiong, S.S., Wang, X.H., Lan, J.H., 2010. A review of detection techniques for dam hidden defects. Journal of Yangtze River Scientific Research Institute, 17(3):38-40 (in Chinese).

[18]Yang, J., Hu, D.X., Wu, Z.R., 2006. Bayesian uncertainty inverse analysis method based on pome. Journal of Zhejiang University (Engineering Science), 40(5):801- 808 (in Chinese).

[19]Yuen, K.V., Lam, H.F., 2006. On the complexity of artificial neural networks for smart structures monitoring. Engineering Structures, 28(7):977-984.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE