Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

  • Fernando SalazarEmail author
  • Rafael Morán
  • Miguel Á. Toledo
  • Eugenio Oñate
Original Paper


Predictive models are an important element in dam safety analysis. They provide an estimate of the dam response faced with a given load combination, which can be compared with the actual measurements to draw conclusions about dam safety. In addition to numerical finite element models, statistical models based on monitoring data have been used for decades for this purpose. In particular, the hydrostatic-season-time method is fully implemented in engineering practice, although some limitations have been pointed out. In other fields of science, powerful tools such as neural networks and support vector machines have been developed, which make use of observed data for interpreting complex systems . This paper contains a review of statistical and machine-learning data-based predictive models, which have been applied to dam safety analysis . Some aspects to take into account when developing analysis of this kind, such as the selection of the input variables, its division into training and validation sets, and the error analysis, are discussed. Most of the papers reviewed deal with one specific output variable of a given dam typology and the majority also lack enough validation data. As a consequence, although results are promising, there is a need for further validation and assessment of generalisation capability. Future research should also focus on the development of criteria for data pre-processing and model application.


Neural Network Model Radial Displacement Impulse Response Function Leakage Flow ANFIS Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research was supported by the Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad, MINECO) through the projects iComplex (IPT-2012-0813-390000) and AIDA (BIA2013-49018-C2-1-R and BIA2013- 49018-C2-2-R).


  1. 1.
    Amberg F (2009) Interpretative models for concrete dam displacements. In: 23th ICOLD congress, q91-R43Google Scholar
  2. 2.
    Arlot S, Celisse A et al (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordzbMATHGoogle Scholar
  4. 4.
    Bonelli S, Félix H (2001) Delayed response analysis of temperature effect. In: Proceedings of the sixth ICOLD benchmark workshop on numerical analysis of dams, Salzburg, AustriaGoogle Scholar
  5. 5.
    Bonelli S, Radzicki K (2007) The impulse response function analysis of pore pressures monitoring data. In: 5th international conference on dam engineering, Lisbon, PortugalGoogle Scholar
  6. 6.
    Bonelli S, Radzicki K (2008) Impulse response function analysis of pore pressure in earthdams. Eur J Environ Civ Eng 12(3):243–262CrossRefGoogle Scholar
  7. 7.
    Bonelli S, Royet P (2001) Delayed response analysis of dam monitoring data. In: Proceedings of the fifth ICOLD European symposium on dams in a European context, Geiranger, NorwayGoogle Scholar
  8. 8.
    Breitenstein F, Klher W, Widman R (1985) Safety control of the dams of the Glockner-Kaprun hydro-electric development. In: 15th ICOLD congress, pp 1121–1134, q56-R59Google Scholar
  9. 9.
    Carrère A, Noret-Duchêne C (2001) Interpretation of an arch dam behaviour using enhanced statistical models. In: Proceedings of the sixth ICOLD benchmark workshop on numerical analysis of dams, Salzburg, AustriaGoogle Scholar
  10. 10.
    Chen BJ, Chang MW et al (2004) Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans Power Syst 19(4):1821–1830CrossRefGoogle Scholar
  11. 11.
    Cheng L, Zheng D (2013) Two online dam safety monitoring models based on the process of extracting environmental effect. Adv Eng Softw 57:4856CrossRefGoogle Scholar
  12. 12.
    Chouinard L, Roy V (2006) Performance of statistical models for dam monitoring data. In: Joint international conference on computing and decision making in civil and building engineering, Montreal, pp 14–16Google Scholar
  13. 13.
    Chouinard L, Bennett D, Feknous N (1995) Statistical analysis of monitoring data for concrete arch dams. J Perform Constr Facil 9(4):286–301CrossRefGoogle Scholar
  14. 14.
    Cortez P, Embrechts MJ (2011) Opening black box data mining models using sensitivity analysis. In: 2011 IEEE Symposium on computational intelligence and data mining (CIDM), IEEE, pp 341–348Google Scholar
  15. 15.
    Crépon O, Lino M (1999) An analytical approach to monitoring. Water Power Dam Constr 51(6):52–54Google Scholar
  16. 16.
    Curt C, Gervais R (2014) Approach to improving the quality of data used to analyse dams-illustrations by two methods. Eur J Environ Civ Eng 18(1):87–105CrossRefGoogle Scholar
  17. 17.
    De Sortis A, Paoliani P (2007) Statistical analysis and structural identification in concrete dam monitoring. Eng Struct 29(1):110–120CrossRefGoogle Scholar
  18. 18.
    Demirkaya S (2010) Deformation analysis of an arch dam using ANFIS. In: Proceedings of the second international workshop on application of artificial intelligence and innovations in engineering geodesy. Braunschweig, Germany, p 2131Google Scholar
  19. 19.
    Demirkaya S, Balcilar M (2012) The contribution of soft computing techniques for the interpretation of dam deformation. In: Proceedings of the FIG working week, Rome, ItalyGoogle Scholar
  20. 20.
    Fabre J, Geffraye G (2013) Study and control of thermal displacements of Gage II dam (France) through the contribution of special heating and cooling devices. In: Proceedings of the seventh argentinian conference on dams, San Juan, Argentina, [in Spanish]Google Scholar
  21. 21.
    Flood I, Kartam N (1994) Neural networks in civil engineering. I: principles and understanding. J Comput Civ Eng 8(2):131–148CrossRefGoogle Scholar
  22. 22.
    Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160(3):249–264CrossRefGoogle Scholar
  23. 23.
    Govindaraju RS (2000) Artificial neural networks in hydrology II: hydrologic applications. J Hydrol Eng 5(2):124–137CrossRefGoogle Scholar
  24. 24.
    Guedes Q, Coelho P (1985) Statistical behaviour model of dams. In: 15th ICOLD congress, pp 319–334, q56-R16Google Scholar
  25. 25.
    Hastie T, Tibshirani R, Firedman J (2009) The elements of statistical learning—data mining, inference, and prediction, 2nd edn. Springer, BerlinzbMATHGoogle Scholar
  26. 26.
    Hill C, Sundaram M (2013) Instrumentation data collection, management and analysis. Tech. rep., United States Society on Dams (USSD) Committee on Monitoring of Dams and Their FoundationsGoogle Scholar
  27. 27.
    International Commission on Large Dams (2012) Dam surveillance guide. Tech. Rep. B-158, ICOLDGoogle Scholar
  28. 28.
    Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  29. 29.
    Jung IS, Berges M, Garrett JH, Kelly CJ (2013) Interpreting the dynamics of embankment dams through a time-series analysis of piezometer data using a non-parametric spectral estimation method. In: Computing in civil engineering (2013), ASCE, p 2532, doi: 10.1061/9780784413029.004
  30. 30.
    Kao C, Loh C (2013) Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Health Monit 20(3):282–303CrossRefGoogle Scholar
  31. 31.
    Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRefGoogle Scholar
  32. 32.
    Léger P, Leclerc M (2007) Hydrostatic, temperature, time-displacement model for concrete dams. J Eng Mech 133(3):267277CrossRefGoogle Scholar
  33. 33.
    Li F, Wang Z, Liu G (2013) Towards an error correction model for dam monitoring data analysis based on cointegration theory. Struct Saf 43:1220CrossRefGoogle Scholar
  34. 34.
    Li F, Wang Z, Liu G, Fu C, Wang J (2014) Hydrostatic seasonal state model for monitoring data analysis of concrete dams. Struct Infrastruct Eng (ahead-of-print):1–16Google Scholar
  35. 35.
    Ljunggren M, Logan T, Campbell P (2013) Is your dam as safe as your data suggest? In: Proceedings of the NZSOLD/ANCOLD conference, vol I. Rotorua, New Zealand Google Scholar
  36. 36.
    Loh CH, Chen CH, Hsu TY (2011) Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam. Struct Health Monit 10(6):587–601CrossRefGoogle Scholar
  37. 37.
    Lombardi G (2004) Advanced data interpretation for diagnosis of concrete dams. Tech. rep., CISMGoogle Scholar
  38. 38.
    Lombardi G, Amberg F, Darbre G (2008) Algorithm for he prediction of functional delays in the behaviour of concrete dams. Hydropower Dams 3:111–116Google Scholar
  39. 39.
    Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 3(3):03–910. doi: 10.1016/j.engstruct.2010.12.011 Google Scholar
  40. 40.
    Mata J, de Castro AT, da Costa JS (2013) Time-frequency analysis for concrete dam safety control: correlation between the daily variation of structural response and air temperature. Eng Struct 48:658–665CrossRefGoogle Scholar
  41. 41.
    Mata J, Tavares de Castro A, Sá da Costa J (2014a) Constructing statistical models for arch dam deformation. Struct Control Health Monit 21(3):423–437. doi: 10.1002/stc.1575 CrossRefGoogle Scholar
  42. 42.
    Mata J, Leitão NS, de Castro AT, da Costa JS (2014b) Construction of decision rules for early detection of a developing concrete arch dam failure scenario. A discriminant approach. Comput Struct 142:45–53CrossRefGoogle Scholar
  43. 43.
    Nedushan B (2002) Multivariable statistical analysis of monitoring data for concrete dams. PhD thesis, McGill UniversityGoogle Scholar
  44. 44.
    Nourani V, Babakhani A (2012) Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modeling. J Comput Civ Eng 27(2):183–195CrossRefGoogle Scholar
  45. 45.
    Olden JD, Jackson DA (2002) Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks. Ecol Model 154(1):135–150CrossRefGoogle Scholar
  46. 46.
    On Large Dams IC (2000) Automated dam monitoring systems. guidelines and case histories. Tech. Rep. B-118, ICOLDGoogle Scholar
  47. 47.
    Opyrchal L (2003) Application of fuzzy sets method to identify seepage path through dams. J Hydraul Eng 129(7):546–548CrossRefGoogle Scholar
  48. 48.
    Palumbo P, Piroddi L, Lancini S, Lozza F (2001) NARX modeling of radial crest displacements of the Schlegeis arch dam. In: Proceedings of the sixth ICOLD benchmark workshop on numerical analysis of dams, Salzburg, AustriaGoogle Scholar
  49. 49.
    Panizzo A, Petaccia A (2009) Analysis of monitoring data for the safety control of dams using neural networks. In: New trends in fluid mechanics research, Springer, p 344347Google Scholar
  50. 50.
    Penot I, Daumas B, Fabre J (2005) Monitoring behaviour. Water Power Dam Constr 57(12):24–27Google Scholar
  51. 51.
    Perner F, Obernhuber P (2010) Analysis of arch dam deformations. Front Archit Civ Eng China 4(1):102–108CrossRefGoogle Scholar
  52. 52.
    Piroddi L, Spinelli W (2003) Long-range nonlinear prediction: a case study. In: Proceedings of the 42nd IEEE conference on decision and control, vol 4. IEEE, pp 3984–3989Google Scholar
  53. 53.
    Popovici A, Ilinca C, Ayvaz T (2013) The performance of the neural networks to model some response parameters of a buttress dam to environment actions. In: Proceedings of the 9th ICOLD European club symposium, Venice, ItalyGoogle Scholar
  54. 54.
    Ranković V, Grujović N, Divac D, Milivojević N, Novaković A (2012) Modelling of dam behaviour based on neuro-fuzzy identification. Eng Struct 35:107113. doi: 10.1016/j.engstruct.2011.11.011 Google Scholar
  55. 55.
    Ranković V, Grujović N, Divac D, Milivojević N (2014a) Development of support vector regression identification model for prediction of dam structural behaviour. Struct Saf 48:33–39CrossRefGoogle Scholar
  56. 56.
    Ranković V, Novaković A, Grujović N, Divac D, Milivojević N (2014b) Predicting piezometric water level in dams via artificial neural networks. Neural Comput Appl 24(5):1115–1121CrossRefGoogle Scholar
  57. 57.
    Restelli F (2010) Systemic evaluation of dam monitoring using PCA. In: Proceedings of the sixth argentinian conference on dams, Neuquén, Argentina, [in Spanish]Google Scholar
  58. 58.
    Restelli F (2013) Systemic evaluation of the response of large dams instrumentation. Application at El Chocón dam. In: Proceedings of the 9th ICOLD European Club Symposium, Venice, ItalyGoogle Scholar
  59. 59.
    Riquelme F, Fraile J, Santillán D, Morán R, Toledo M (2011) Application of artificial neural network models to determine movements in an arch dam. In: Proceedings of the 2nd international congress on dam maintenance and rehabilitation. Zaragoza, Spain, pp 117–123Google Scholar
  60. 60.
    Ruiz H (2013) Fisher networks: a principled approach to retrieval-based classification. PhD thesis, Liverpool John Moores UniversityGoogle Scholar
  61. 61.
    Salazar F, Toledo M, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17. doi: 10.1016/j.strusafe.2015.05.001 CrossRefGoogle Scholar
  62. 62.
    Sánchez Caro FJ (2007) Dam safety: contributions to the deformation analysis and monitoring as an element of prevention of pathologies of geotechnical origin. PhD thesis, UPM, [In Spanish]Google Scholar
  63. 63.
    Santillán D, Fraile-Ardanuy J, Toledo M (2013) Dam seepage analysis based on artificial neural networks: The hysteresis phenomenon. In: The 2013 international joint conference on neural networks (IJCNN), IEEE, pp 1–8Google Scholar
  64. 64.
    Santillán D, Fraile-Ardanuy J, Toledo M (2014) Seepage prediction in arch dams by means of artificial neural networks. Water Technol Sci V(3), 81–96. [in Spanish] Google Scholar
  65. 65.
    Saouma V, Hansen E, Rajagopalan B (2001) Statistical and 3d nonlinear finite element analysis of Schlegeis dam. In: Proceedings of the sixth ICOLD benchmark workshop on numerical analysis of dams, pp 17–19Google Scholar
  66. 66.
    Silva Gomes AF, Silva Matos D (1985) Quantitative analysis of dam monitoring results. State of the art, applications and prospects. In: 15th ICOLD congres, pp 319–334, q56-R39Google Scholar
  67. 67.
    Simon A, Royer M, Mauris F, Fabre J (2013) Analysis and interpretation of dam measurements using artificial neural networks. In: Proceedings of the 9th ICOLD European club symposium, Venice, ItalyGoogle Scholar
  68. 68.
    Smola AJ, Schlkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199222MathSciNetCrossRefGoogle Scholar
  69. 69.
    Stojanovic B, Milivojevic M, Ivanovic M, Milivojevic N, Divac D (2013) Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv Eng Softw 65:182190CrossRefGoogle Scholar
  70. 70.
    Su H, Wu Z, Wen Z (2007) Identification model for dam behavior based on wavelet network. Comput Aided Civ Infrastruct Eng 22(6):438–448CrossRefGoogle Scholar
  71. 71.
    Swiss Committee on Dams (2003) Methods of analysis for the prediction and the verification of dam behaviour. Tech. rep., ICOLDGoogle Scholar
  72. 72.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132CrossRefzbMATHGoogle Scholar
  73. 73.
    Tatin M, Briffaut M, Dufour F, Simon A, Fabre J (2013) Thermal displacements of concrete dams: finite element and statistical modelling. In: Proceedings of the 9th ICOLD European club symposium, Venice, ItalyGoogle Scholar
  74. 74.
    Tatin M, Briffaut M, Dufour F, Simon A, Fabre JP (2015) Thermal displacements of concrete dams: accounting for water temperature in statistical models. Eng Struct 91:26–39CrossRefGoogle Scholar
  75. 75.
    Tayfur G, Swiatek D, Wita A, Singh VP (2005) Case study: finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland. J Hydraul Eng 131(6):431440CrossRefGoogle Scholar
  76. 76.
    Willm G, Beaujoint N (1967) Les méthodes de surveillance des barrages au service de la production hydraulique d’Electricité de France-Problèmes ancients et solutions nouvelles. In: 9th ICOLD Congres, pp 529–550, q34-R30. [in French]Google Scholar
  77. 77.
    Xu C, Yue D, Deng C (2012) Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis. Eng Appl Artif Intell 25(3):468475CrossRefGoogle Scholar
  78. 78.
    Xu H, Li X (2012) Inferring rules for adverse load combinations to crack in concrete dam from monitoring data using adaptive neuro-fuzzy inference system. Sci China Technol Sci 55(1):136141Google Scholar
  79. 79.
    Yao Y, Sharma A, Golubchik L, Govindan R (2010) Online anomaly detection for sensor systems: a simple and efficient approach. Perform Eval 67(11):1059–1075CrossRefGoogle Scholar
  80. 80.
    Yu H, Wu Z, Bao T, Zhang L (2010) Multivariate analysis in dam monitoring data with PCA. Sci China Technol Sci 53(4):1088–1097. doi: 10.1007/s11431-010-0060-1 CrossRefzbMATHGoogle Scholar
  81. 81.
    Zenz G, Obernhuber P (2001) ICOLD benchmark workshops on dam safety. Hydropower Dams 8:75–78Google Scholar

Copyright information

© CIMNE, Barcelona, Spain 2015

Authors and Affiliations

  1. 1.CIMNE Centre Internacional de Metodes Numerics en EnginyeriaUniversitat Politècnica de Catalunya (UPC)BarcelonaSpain
  2. 2.Department of Civil Engineering: Hydraulics, Energy and EnvironmentTechnical University of Madrid (UPM)MadridSpain

Personalised recommendations