Study of the influence of temperature and water level of the reservoir about the displacement of a concrete dam
More details
Hide details
Department of Physics, Statistics and Mathematics, Federal Technological University of Paraná, Francisco Beltrão, Paraná, BRAZIL
Department of Statistics, Federal University of Paraná, Curitiba, Paraná, BRAZIL
Coordination of the Degree in Mathematics, Federal Technological University of Paraná, Toledo, Paraná, BRAZIL
Division of Civil Engineering and Architecture, Itaipu, Foz do Iguaçu, Paraná, BRAZIL
Online publication date: 2016-03-07
Publication date: 2016-02-01
International Journal of Applied Mechanics and Engineering 2016;21(1):107-120
Multivariate techniques are used in this study to analyze the monitoring data displacements of a concrete dam, measured by means of pendulums, extensometer bases and multiple rod extensometers, taking into account the action of environmental conditions, bounded by the surface temperature of the concrete at ambient temperature and the tank water level. The canonical correlation analysis is used to evaluate the influence of environmental variables in the displacement of structures and dam foundations. The factor analysis is used to identify data sources of variability and order the sensors according to the action of factors. The dates of the measurements are grouped according to similarities in the present observations, by applying the cluster analysis. Then the discriminant analysis is used to assess the groups as to their homogeneity. The results demonstrate that the techniques used for distinguishing the dam responses and identify the effects of changes in environmental conditions on the displacements of the structures and dam foundations.
Carvalho J.V. and Romanel C (2007): Temporal neural networks applied to the monitoring of dams. – Rev. El. Sist. Inf., vol.6, No.1, pp.1-9. (In Portuguese).
Li F., Wang Z.Z. and Liu G. (2013): Towards an error correction model for dam monitoring data analysis based on cointegration theory. – Struct. Saf., vol.43, pp.12–20.
Cruz P.T. (2006): 100 Brazilian dams: case histories, building materials, project. – Oficina dos Textos, São Paulo. (In Portuguese).
Matos S.F. (2002): Assessment tools for auscultation concrete dam. Case Study: deformeter and stress meter for concrete in the Itaipu Dam. – Masters dissertation, UFPR. (In Portuguese).
De Sortis A. and Paoliani P. (2007): Statistical analysis and structural identification in concrete dam monitoring. – Eng. Struct., vol.29, pp.110–120.
Kuperman S.C., Moretti M.R., Cifu S., Celestino T.B., Re G. and Zoellner K. (2005): Criteria to establish limit values of instrumentation readings for old embankment and concrete dams.
Villwock R., Steiner M.T.A., Dyminski A.S. and Chaves Neto A. (2013): Itaipu Hydroelectric Power Plant Structural Geotechnical Instrumentation Temporal Data Under the Application of Multivariate Analysis - Grouping and Ranking Techniques. – In: Multivariate Analysis in Management, Engineering and the Sciences. InTech, pp.81–102.
Medeiros C.H. and Lopes M.G.M. (2011): The risk of dams rating by risk category, based factors weighting method. – In. XXVIII Seminário Nacional de Grandes Barragens. Rio de Janeiro. (In Portuguese).
Farrar C.R. and Worden K. (2007): An introduction to structural health monitoring. – Philos. Trans. R. Soc. A, vol.365, pp.303–315.
Figueiredo E., Park G., Farrar C.R., Worden K. and Figueiras J. (2011): Machine learning algorithms for damage detection under operational and environmental variability. – Struct. Health Monit, vol.10, pp.559–572.
Buzzi M.F. (2007): Evaluation of time-series correlations readings of geotechnical and structural monitoring instruments and environmental variables in dams - Itaipu case study. - Masters dissertation, UFPR. (In Portuguese).
Guedes Q.M. and De Faria É.F. (2007): Statistical model control dislocation monitored the bark dam UHE Funil. - In. XXVII Seminário Nacional de Grandes Barragens, Belém. (In Portuguese).
Deng N., Wang J.-G. and Szostak-Chrzanowski A. (2008): Dam Deformation Analysis Using the Partial Least Squares Method. - In: 13th FIG Int. Symp. on Deformation Measurements and Analysis and 4th IAG Symp. on Geodesy for Geotechnical and Structural Engineering, Lisbon.
Jin-Ping H., Yu-Qun S. (2011): Study on TMTD Statistical Model of Arch Dam Deformation Monitoring. - Procedia Eng., vol.15, pp.2139–2144.
Mata J. (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. - Elsevier Eng. Struct., vol.33, pp.903–910.
Xu C., Yue D. and Deng C. (2012): Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis. - Eng. Appl. Artif. Intell., vol.25, pp.468–475.
Mata J., Tavares de Castro A. and Sá da Costa J. (2013): Time–frequency analysis for concrete dam safety control: Correlation between the daily variation of structural response and air temperature. - Eng. Struct., vol.48, pp.658–665.
Cheng L. and Zheng D. (2013): Two online dam safety monitoring models based on the process of extracting environmental effect. - Adv. Eng. Soft., vol.57, pp.48–56.
Mujica L.E., Ruiz M., Pozo F., et al (2014): A structural damage detection indicator based on principal component analysis and statistical hypothesis testing. - Smart. Mater. Struct., vol.23, pp.25014–25025.
Johnson R.A. and Wichern D.W. (2007): Applied Multivariate Statistical Analysis. - 6th edn. Pearson.
Journals System - logo
Scroll to top