An approach using random forest intelligent algorithm to construct a monitoring model for dam safety

Abstract

The mechanism of dam safety monitoring model is analyzed; for the dam system comprehensive affected by multi-factor, the mapping relationship between the influence factors and the dam behavior effects domain is usually nonlinear. Synthesizing each kind of factor, 27 parameters are chosen as the main factors which affect the accuracy of the monitoring model. Taking the actual monitoring data as the evaluation factor, the dam safety monitoring model based on the random forest (RF) intelligent algorithm was built with the actual monitoring data to predict uplift pressure. At the same time, test the significance of each variable based on the RF monitoring model and calculate the importance degree of each variable for the model through the importance function. It is indicated that RF model can be relatively fast and accurately predict the uplift pressure of the dam according to the influence factors. The average prediction accuracy is more than 95%. As compared with other intelligent algorithms such as support vector machine, RF has better robustness, higher prediction accuracy, and faster convergence speed. Because of the uniformity of the calculation procedure and the universality of the prediction method, the RF model also has reasonable extrapolation for other dam safety monitoring models (such as crack opening and seepage discharge). Significance test results obtained by the two methods have shown that the impact of reservoir water level and daily rainfall on the uplift pressure is significant, and other factors’ impact on dam deformation is unstable and changes with the external environmental influence.

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References

  1. 1.

    Wu Z, Su H (2005) Dam health diagnosis and evaluation. Smart Mater Struct 14(3):S130

    Article  Google Scholar 

  2. 2.

    Su H, Wen Z, Wu Z (2011) Study on an intelligent inference engine in early-warning system of dam health. Water Resour Manag 25(6):1545–1563

    Article  Google Scholar 

  3. 3.

    Jeon J et al (2009) Development of dam safety management system. Adv Eng Softw 40(8):554–563

    Article  Google Scholar 

  4. 4.

    Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13–14):861–870

    Article  Google Scholar 

  5. 5.

    Ardito R, Maier G, Massalongo G (2008) Diagnostic analysis of concrete dams based on seasonal hydrostatic loading. Eng Struct 30(11):3176–3185

    Article  Google Scholar 

  6. 6.

    Szostak-Chrzanowski A, Chrzanowski A, Massiéra M (2005) Use of deformation monitoring results in solving geomechanical problems—case studies. Eng Geol 79(1–2):3–12

    Article  Google Scholar 

  7. 7.

    Li F, Wang Z, Liu G (2013) Towards an error correction model for dam monitoring data analysis based on cointegration theory. Struct Saf 43:12–20

    Article  Google Scholar 

  8. 8.

    Xu C, Yue D, Deng C (2012) Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis. Eng Appl Artif Intell 25(3):468–475

    Article  Google Scholar 

  9. 9.

    Stojanovic B et al (2013) Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv Eng Softw 65:182–190

    Article  Google Scholar 

  10. 10.

    Su H, Chen Z, Wen Z (2016) Performance improvement method of support vector machine-based model monitoring dam safety. Struct Control Health Monit 23(2):252–266

    Article  Google Scholar 

  11. 11.

    Su H, Wu Z, Wen Z (2007) Identification model for dam behavior based on wavelet network. Comput Aided Civ Infrastruct Eng 22(6):438–448

    Article  Google Scholar 

  12. 12.

    Su H et al (2015) Time-varying identification model for dam behavior considering structural reinforcement. Struct Saf 57:1–7

    Article  Google Scholar 

  13. 13.

    Huaizhi S, Jiang H, Zhongru W (2012) A study of safety evaluation and early-warning method for dam global behavior. Struct Health Monit 11(3):269–279

    Article  Google Scholar 

  14. 14.

    Hu J, Ma F (2016) Comprehensive investigation method for sudden increases of uplift pressures beneath gravity dams: case study. J Perform Constr Facil 30(5):04016023

    Article  Google Scholar 

  15. 15.

    De Sortis A, Paoliani P (2007) Statistical analysis and structural identification in concrete dam monitoring. Eng Struct 29(1):110–120

    Article  Google Scholar 

  16. 16.

    Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 33(3):903–910

    Article  Google Scholar 

  17. 17.

    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):183–195

    Article  Google Scholar 

  18. 18.

    Riquelme F et al (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

  19. 19.

    Kao CY, Loh CH (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):282–303

    Article  Google Scholar 

  20. 20.

    Santillán D, Fraile-Ardanuy J, Toledo M (2014) Seepage prediction in arch dams by means of artificial neural networks. Water Technol Sci 3:81–96

    Google Scholar 

  21. 21.

    Simon A et al (2013) Analysis and interpretation of dam measurements using artificial neural networks. In: Proceedings of the 9th ICOLD European club symposium, Venice, Italy

  22. 22.

    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    MathSciNet  Article  Google Scholar 

  23. 23.

    Ranković V et al (2014) Development of support vector regression identification model for prediction of dam structural behaviour. Struct Saf 48:33–39

    Article  Google Scholar 

  24. 24.

    Salazar F et al (2016) Interpretation of dam deformation and leakage with boosted regression trees. Eng Struct 119:230–251

    Article  Google Scholar 

  25. 25.

    Salazar F et al (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Methods Eng 24(1):1–21

    Article  Google Scholar 

  26. 26.

    Salazar F et al (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17

    Article  Google Scholar 

  27. 27.

    Dong L-J, Li X-B, Kang P (2013) Prediction of rockburst classification using random forest. Trans Nonferrous Met Soc China 23(2):472–477

    Article  Google Scholar 

  28. 28.

    Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99(6):323–329

    Article  Google Scholar 

  29. 29.

    Tesfamariam S, Liu Z (2010) Earthquake induced damage classification for reinforced concrete buildings. Struct Saf 32(2):154–164

    Article  Google Scholar 

  30. 30.

    Deng H, Runger G (2013) Gene selection with guided regularized random forest. Pattern Recogn 46(12):3483–3489

    Article  Google Scholar 

  31. 31.

    Immitzer M, Atzberger C, Koukal T (2012) Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens 4(9):2661–2693

    Article  Google Scholar 

  32. 32.

    Mihailescu DM et al (2013) Computer aided diagnosis method for steatosis rating in ultrasound images using random forests. Med Ultrason 15(3):184

    Article  Google Scholar 

  33. 33.

    Zhao T et al (2012) Predict seasonal low flows in the upper Yangtze River using random forests model. J Hydroelectr Eng 31(3):18–38

    Google Scholar 

  34. 34.

    Wang Z et al (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141

    Article  Google Scholar 

  35. 35.

    You K, Yuan-fang C, Sheng-hua G (2014) Assessment of sustainable utilization of regional water resources based on random forest. Water Resour Power 32(3):34–38

    Google Scholar 

  36. 36.

    Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  37. 37.

    Fang K et al (2011) A review of technologies on random forests. Stat Info Forum 26(3):32–38

    Google Scholar 

  38. 38.

    Peters J et al (2007) Random forests as a tool for ecohydrological distribution modelling. Ecol Model 207(2–4):304–318

    Article  Google Scholar 

  39. 39.

    Breiman L (2002) Manual on setting up, using, and understanding random forests v3. 1. Statistics Department University of California Berkeley, CA, USA, p 1

  40. 40.

    Li X, Su H, Hu J (2017) The prediction model of dam uplift pressure based on random forest. Mater Sci Eng 229(1):012025

    Google Scholar 

  41. 41.

    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. Salzburg, Austria

Download references

Acknowledgements

This research has been partially supported by the National Key Research and Development Program of China (SN: 2018YFC0407101, 2016YFC0401601, 2017YFC0804607), National Natural Science Foundation of China (SN: 51739003, 51579083, 51479054), Key R&D Program of Guangxi (SN: AB17195074), Open Foundation of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (SN: 20165042112, 20145027612), the Fundamental Research Funds for the Central Universities (SN: 2018B40514, 2015B25414).

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Correspondence to Zhiping Wen.

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Li, X., Wen, Z. & Su, H. An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Engineering with Computers 37, 39–56 (2021). https://doi.org/10.1007/s00366-019-00806-0

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Keywords

  • Dam safety
  • Monitoring model
  • Random forest
  • Support vector machine
  • Significance test