Identification of dam behavior by means of machine learning classification models

ABSTRACT: The improvements in monitoring devices result in databases of increasing size showing dam behaviour. Advanced tools are required to extract useful information from such large amounts of data. Machine learning is increasingly used for that purpose worldwide: data-based models are built to estimate the dam response in front of a given combination of loads. The results of the comparison between model predictions and actual measurements can be used for decision support in dam safety evaluations. However, most of the works to date consider each device separately. A different approach is used in this contribution: a set of displacement records are jointly considered to identify patterns using a classification model. First, potential anomaly scenarios are defined and the response of the dam for each of them is obtained with numerical models under a realistic load combination. Then, the resulting displacements are used to generate a machine learning classifier. This model is later used to predict the most probable class of dam behavior corresponding to a new set of records. The methodology is applied to a double-curvature arch dam, showing great potential for anomaly detection.

Keywords: Machine Learning, Random Forest, Arch Dam, Anomaly Detection.

References

1. Salazar, F., Morán, R., Toledo, M. Á., & 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.

2. 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.

3. Willm, Beaujoint. Les méthodes de surveillance des barrages au service de la production hydraulique d’Electricité de France, problems anciens et solutions nouvelles. IXth Int. Congr. Large Dams, Istanbul; 1967. p. 529–50.

4. 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.

5. Mata, J. (2011). Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Engineering Structures, 33(3), 903-910.

6. De Granrut, M., Simon, A., &Dias, D. (2019). Artificial neural networks for the interpretation of piezometric levels at the rock-concrete interface of arch dams. Engineering Structures, 178, 616-634.

7. Tinoco, J. A. B., Granrut, M. D., Dias, D., Miranda, T. F., & Simon, A. G. (2018). Using soft computing tools for piezometric level prediction. In Third International Dam World Conference (pp. 1-10).

8. Salazar, F., Toledo, M. Á., Oñate, E., & Suárez, B. (2016). Interpretation of dam deformation and leakage with boosted regression trees. Engineering Structures, 119, 230-251.

9. Mata, J., Leitão, N. S., De Castro, A. T., & Da Costa, J. S. (2014). Construction of decision rules for early detection of a developing concrete arch dam failure scenario. A discriminant approach. Computers & Structures, 142, 45-53.

10. Vicente, D. J., San Mauro, J., Salazar, F., & Baena, C. M. (2017). An Interactive Tool for Automatic Predimensioning and Numerical Modeling of Arch Dams. Mathematical Problems in Engineering, 2017.

11. Dadvand, P., Rossi, R., & Oñate, E. (2010). An object-oriented environment for developing finite element codes for multi-disciplinary applications. Archives of Computational Methods in Engineering, 17(3), 253-297.

12. Ribó, R., Pasenau, M., Escolano, E., Pérez, J., Coll, A., Melendo, A., & González, S. (2008). GiD The Personal Pre and Postprocessor. Reference Manual, version, 9.

13. Santillán, D., Salete, E., Vicente, D. J., & Toledo, M. Á. (2014). Treatment of solar radiation by spatial and temporal discretization for modeling the thermal response of arch dams. Journal of Engineering Mechanics, 140(11), 05014001.

14. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

15. Díaz-Uriarte, R., & De Andres, S. A. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(1), 3.

16. Salazar, F. and Hariri-Ardebili, M.A., 2019, Machine Learning Based Seismic Stability Assessment of Dams with Heterogeneous Concrete, 3rd Meeting of EWG Dams and Earthquakes, Lisbon, Portugal, May 06-09.

17. Salazar, F., & Crookston, B. M. (2019). A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways. Water, 11(3), 544.

18. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.

19. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.