Performance of data-based models for early detection of damage in concrete dams

References

• Bowerman, B. L., O’Connell, R. T., & Koehler, A. B. (2005). Forecasting, time series, and regression: An applied approach, vol. 4. South-Western Pub.
   Google Scholar
• Bühlmann, M., Vetsch, D. F., & Boes, R. M. (2015). Influence of measuring intervals on goodness of fit of dam behaviour analysis models. In 13th ICOLD Benchmark Workshop on the Numerical Analysis of Dams (pp. 317–325). Switzerland: Lausanne.
   Google Scholar
• Cheng, L., & Zheng, D. (2013). Two online dam safety monitoring models based on the process of extracting environmental effect. Advances in Engineering Software, 57, 48–56.
   Web of Science ®Google Scholar
• Chouinard, L. E., Bennett, D. W., & Feknous, N. (1995). Statistical analysis of monitoring data for concrete arch dams. Journal of Performance of Constructed Facilities, 9(4), 286–301. doi:10.1061/(ASCE)0887-3828(1995)9:4(286)
   Google Scholar
• Dai, B., Gu, C., Zhao, E., & Qin, X. (2018). Statistical model optimized random forest regression model for concrete dam deformation monitoring. Structural Control and Health Monitoring, (6), 25. doi:10.1002/stc.2170
   Web of Science ®Google Scholar
• Dassault Systemes. (2017). Abaqus Online documentation.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Fabre, J. P., & Hueber, R. (2009). Exploitation of monitoring result to adapt the operation of dams to their behaviour. In Long term behaviour of dams (pp. 347–352). Austria: Graz.
   Google Scholar
• Gamse, S., & Oberguggenberger, M. (2016). Assessment of long-term coordinate time series using hydrostatic-season-time model for rock-fill embankment dam. Structural Control and Health Monitoring, 24(1), 1–18. doi:10.1002/stc.1859
   Web of Science ®Google Scholar
• Geokon (2015). Pendulum readout: Model 6850. Technical Report, Geokon, Lebanon, USA.
   Google Scholar
• Geosense (2017). Thermistors: Datasheet. Technical report, Geosense Ltd, Suffolk, England.
   Google Scholar
• Hariri-Ardebili, M. A., & Saouma, V. (2015). Quantitative failure metric for gravity dams. Earthquake Engineering & Structural Dynamics, 44(3), 461–480. doi:10.1002/eqe.2481
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference and prediction (2nd ed.). Springer,.
   Google Scholar
• Hellgren, R., Malm, R., & Ansell, A. (2019). Data for: Performance of data-based models for early detection of damage in concrete dams.
   Google Scholar
• Hellgren, R., Malm, R., & Nordström, E. (2019). Modeller för övervakning av betongdammar [Models for monitoring of concrete dams]. Technical report, Energiforsk, Stockholm, Sweden.
   Google Scholar
• ICOLD’s Committee on Dam Surveillance. (2016). Dam surveillance guide, bulletin 158. Technical report, International Commission on Large Dams (ICOLD), Paris.
   Google Scholar
• Jung, I. S., Berges, M., Garrett, J. H., & Poczos, B. (2015). Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data. Advanced Engineering Informatics, 29(4), 902–917. doi:10.1016/j.aei.2015.10.002
   Web of Science ®Google Scholar
• Kao, C. Y., & Loh, C. H. (2013). Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Structural Control and Health Monitoring, 20(3), 282–303. doi:10.1002/stc.492
   Web of Science ®Google Scholar
• Krounis, A., Johansson, F., & Larsson, S. (2015). Effects of spatial variation in cohesion over the concrete-rock interface on dam sliding stability. Journal of Rock Mechanics and Geotechnical Engineering, 7(6), 659–667. doi:10.1016/j.jrmge.2015.08.005
   Web of Science ®Google Scholar
• Léger, P., & Seydou, S. S. (2009). Seasonal thermal displacements of gravity dams located in northern regions. Journal of Performance of Constructed Facilities, 23(3), 166–174. doi:10.1061/(ASCE)0887-3828(2009)23:3(166)
   Web of Science ®Google Scholar
• Li, F., Wang, Z., & Liu, G. (2013). Towards an error correction model for dam monitoring data analysis based on cointegration theory. Structural Safety, 43, 12–20.
   Web of Science ®Google Scholar
• Li, X., Li, Y., Lu, X., Wang, Y., Zhang, H., & Zhang, P. (2019). An online anomaly recognition and early warning model for dam safety monitoring data. Structural Health Monitoring, 1–14. pages doi:10.1177/1475921719864265
   Web of Science ®Google Scholar
• Loh, C. H., Chen, C. H., & Hsu, T. Y. (2011). Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam. Structural Health Monitoring: An International Journal, 10(6), 587–601. doi:10.1177/1475921710395807
   Web of Science ®Google Scholar
• Lombardi, G., Amberg, F., & Darbre, G. R. (2008). Algorithm for the prediction of functional delays in the behaviour of concrete dams. International Journal on Hydropower and Dams, 15(3), 111–116.
   Google Scholar
• Malm, R. (2009). Predicting shear type crack initiation and growth in concrete with non-linear finite element method (Doctoral dissertation) KTH, Structural Design and Bridges.
   Google Scholar
• Malm, R. (2016). Guideline for FE analyses of concrete dams. Technical report, Energiforsk, Stockholm, Sweden.
   Google Scholar
• Malm, R., & Ansell, A. (2011). Cracking of concrete buttress dam due to seasonal temperature variation. ACI Structural Journal, 108(1), 13–22.
   Web of Science ®Google Scholar
• Malm, R., Hassanzadeh, M., & Hellgren, R. (2017). Proceedings of the 14th ICOLD International Benchmark Workshop on Numerical Analysis of Dams. TRITA-ABE-RPT-1802001, pp. 720. Stockholm, Sweden. KTH, Concrete Structures, KTH.
   Google Scholar
• Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441. doi:10.1137/0111030
   Google Scholar
• Mata, J. (2011). Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Engineering Structures, 33(3), 903–910. doi:10.1016/j.engstruct.2010.12.011
   Web of Science ®Google Scholar
• Mata, J., Schclar Leitão, N., Tavares De Castro, A., & Sá Da Costa, J. (2014). Construction of decision rules for early detection of a developing concrete arch dam failure scenario. A discriminant approach. Computers & Structures, 142, 45–53. doi:10.1016/j.compstruc.2014.07.002
   Web of Science ®Google Scholar
• Nedushan, B. A. (2002). Multivariate statistical analysis of monitoring data for concrete dams (Doctoral dissertation). McGill University.
   Google Scholar
• Nguyen, L. H., & Goulet, J. A. (2018). Anomaly detection with the Switching Kalman Filter for structural health monitoring. Structural Control and Health Monitoring, (4), 25. doi:10.1002/stc.2136
   Web of Science ®Google Scholar
• Nordström, E., Malm, R., Johansson, F., Ligier, P.-L., & Øyvind, L. (2015). Betongdammars brottförlopp Energiforsk rapport 2015:122 [Failure of concrete dams-literature reviw and potential for development]. Technical report, Energiforsk, Stockholm, Sweden.
   Google Scholar
• Oliveira, S., & Faria, R. (2006). Numerical simulation of collapse scenarios in reduced scale tests of arch dams. Engineering Structures, 28(10), 1430–1439. doi:10.1016/j.engstruct.2006.01.012
   Web of Science ®Google Scholar
• Salazar, F., Morán, R., Toledo, M., & Oñate, E. (2017). Data-based models for the prediction of dam behaviour: A review and some methodological considerations. Archives of Computational Methods in Engineering, 24(1), 1–21. doi:10.1007/s11831-015-9157-9
   Web of Science ®Google Scholar
• Salazar, F., Toledo, M. Á., González, J. M., & Oñate, E. (2017). Early detection of anomalies in dam performance: A methodology based on boosted regression trees. Structural Control and Health Monitoring, 24(11), e2012–16. doi:10.1002/stc.2012
   Web of Science ®Google Scholar
• Salazar, F., Toledo, M. A., Oñate, E., & Morán, R. (2015). An empirical comparison of machine learning techniques for dam behaviour modelling. Structural Safety, 56, 9–17. doi:10.1016/j.strusafe.2015.05.001
   Web of Science ®Google Scholar
• Sevieri, G., Andreini, M., De Falco, A., & Matthies, H. G. (2019). Concrete gravity dams model parameters updating using static measurements. Engineering Structures, 196, 109231. doi:10.1016/j.engstruct.2019.05.072
   Web of Science ®Google Scholar
• SMHI. (2019). Swedish Meteorological and Hydrological Institute open data.
   Google Scholar
• Svensen, D. (2016). Numerical analyses of concrete buttress dams to design dam monitoring (Master dissertation). KTH Royal Institute of Technology.
   Google Scholar
• SwissCOLD (2003). Methods of analysis for the prediction and the verification of dam behaviour. Technical report, SwissCOLD, Baden, Switzerland.
   Google Scholar
• Tatin, M., Briffau, M., Dufour, F., Simon, A., Fabre, J.-P P., Briffaut, M., … Fabre, J.-P P. (2018). Statistical modelling of thermal displacements for concrete dams: Influence of water temperature profile and dam thickness profile. Engineering Structures, 165, 63–75. doi:10.1016/j.engstruct.2018.03.010
   Web of Science ®Google Scholar
• Tatin, M., Briffaut, M., Dufour, F., Simon, A., & Fabre, J.-P. (2015). Thermal displacements of concrete dams: Accounting for water temperature in statistical models. Engineering Structures, 91, 26–39. doi:10.1016/j.engstruct.2015.01.047
   Web of Science ®Google Scholar
• The MathWorks Inc. (2018). MATLAB 2018b.
   Google Scholar
• Willm, G., & Beaujoint, N. (1967). Les méthodes de surveillance des barrages au service de la production hydraulique d’Electricité de France, problèmes anciens et solutions nouvelles. In IXth international congress on large dams (pp. 529–550). Istanbul, Turkey: ICOLD.
   Google Scholar
• Yu, H., Wu, Z. R., Bao, T. F., & Zhang, L. (2010). Multivariate analysis in dam monitoring data with PCA. Science China Technological Sciences, 53(4), 1088–1097. doi:10.1007/s11431-010-0060-1
   Web of Science ®Google Scholar
• Zou, J., Bui, K. T. T., Xiao, Y., & Doan, C. V. (2018). Dam deformation analysis based on BPNN merging models. Geo-Spatial Information Science, 21(2), 149–157. doi:10.1080/10095020.2017.1386848
   Web of Science ®Google Scholar