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Spatio-temporal modelling of dam deformation using independent component analysis

Dai, Wujiao; Liu, Bin; Meng, Xiaolin; Huang, D.

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Wujiao Dai

Bin Liu

Xiaolin Meng

D. Huang


Modelling dam deformation based on monitoring data plays an important role in the assessment of a dam’s safety. Traditional dam deformation modelling methods generally utilise single monitoring point. It means it is necessary to model for each monitoring point and the spatial correlation between points will not be considered using traditional modelling methods. Spatio-temporal modelling methods provide a way to model the dam deformation with only one functional expression and analyse the stability of dam in its entirety. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observables. In this paper, ICA is introduced as a spatio-temporal modelling method for dam deformation. In this method, the deformation data series of all points were processed using ICA as input signals, and a few output independent signals were used to model. The real data experiment with displacement measurements by wire alignment of Wuqiangxi Dam was conducted and the results show that the output independent signals are correlated with physical responses of causative factors such as temperature and water level respectively. This discovery is beneficial in analysing the dam deformation. In addition, ICA is also an effective dimension reduced method for spatio-temporal modelling in dam deformation analysis applications.


Dai, W., Liu, B., Meng, X., & Huang, D. (in press). Spatio-temporal modelling of dam deformation using independent component analysis. Survey Review, 46(339),

Journal Article Type Article
Acceptance Date Jun 13, 2014
Online Publication Date Jul 8, 2014
Deposit Date Jul 25, 2016
Publicly Available Date Jul 25, 2016
Journal Survey Review
Print ISSN 0039-6265
Electronic ISSN 1752-2706
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 46
Issue 339
Public URL
Publisher URL
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis in Survey Review on 8 July 2014, available online:
Contract Date Jul 25, 2016


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