Abstract
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.
Similar content being viewed by others
References
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. [doi:10.1007/s11431-008-6004-3]
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. [doi:10.1109/ICMLC.2009.5212604]
De Sortis, A., Paoliani, P., 2007. Statistical analysis and structural identification in concrete dam monitoring. Engineering Structures, 29(1):110–120. [doi:10.1016/j.engstruct.2006.04.022]
Friedman, N., Linial, M., Nachman, I., 2000. Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7(3–4):601–620. [doi:10.1089/106652700750050961]
Hecht-Nielsen, R., 1989. Theory of the Back Propagation Neural Network. International Joint Conference on Neural Networks, Washington DC, USA, p.593–605. [doi:10. 1109/IJCNN.1989.118638]
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. [doi:10.1117/12.774621]
Kim, H.S., Melhem, H., 2004. Damage detection of structures by wavelet analysis. Engineering Structures, 26(3):347–362. [doi:10.1016/j.engstruct.2003.10.008]
Leger, P., Leclerc, M., 2007. Hydrostatic, temperature, time-displacement model for concrete dams. Journal of Engineering Mechanics, 133(3):267–277. [doi:10.1061/(ASCE) 0733-9399(2007)133:3(267)]
Mata, J., 2011. Methods of analysis for the prediction and the verification of dam behavior. Engineering Structures, 33(3):903–910. [doi:10.1016/j.engstruct.2010.12.011]
Otsu, N., 1979. A threshold selection method from gray level histograms. IEEE Transactions on Systems Man and Cybernetics, 9(1):62–66. [doi:10.1109/TSMC.1979.4310076]
Su, H.Z., 2003. Intelligent Sensing and Fusion System and Its Application to Dam Safety Monitoring. MS Thesis, Hohai University, Nanjing, China (in Chinese).
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. [doi:10.1111/j.1467-8667.2007.00499.x]
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. [doi:10.1016/j. biosystemseng.2005.09.005]
Wu, Z.R., Su, H.Z., 2005. Dam health diagnosis and evaluation. Smart Materials and Structures, 14(3):130–136. [doi:10. 1088/0964-1726/14/3/016]
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).
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. [doi:10.1007/s11431-007-6003-9]
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).
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).
Yuen, K.V., Lam, H.F., 2006. On the complexity of artificial neural networks for smart structures monitoring. Engineering Structures, 28(7):977–984. [doi:10.1016/j.engstruct.2005.11.002]
Author information
Authors and Affiliations
Additional information
Project supported by the National Natural Science Foundation of China (Nos. 51139001, 51179066, 51079046, and 50909041)
Rights and permissions
About this article
Cite this article
Liu, X., Wu, Zr., Yang, Y. et al. Information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams. J. Zhejiang Univ. Sci. A 13, 687–699 (2012). https://doi.org/10.1631/jzus.A1200122
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A1200122