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Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams

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Machine Learning Paradigms

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 1))

Abstract

The protection of critical engineering infrastructures is vital to today’s society, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This control is based on the measurement of physical quantities that characterize the structural behavior, such as displacements, strains and stresses. The analysis of monitoring data and its evaluation against physical and mathematical models is the strongest tool to assess the safety of the structural behavior. Commonly, dam specialists use multiple linear regression models to analyze the dam response, which is a well-known approach among dam engineers since the 1950s decade. Nowadays, the data acquisition paradigm is changing from a manual process, where measurements were taken with low frequency (e.g., on a weekly basis), to a fully automated process that allows much higher frequencies. This new paradigm escalates the potential of data analytics on top of monitoring data, but, on the other hand, increases data quality issues related to anomalies in the acquisition process. This chapter presents the full data lifecycle in the safety control of large-scale civil engineering infrastructures (focused on dams), from the data acquisition process, data processing and storage, data quality and outlier detection, and data analysis. A strong focus is made on the use of machine learning techniques for data analysis, where the common multiple linear regression analysis is compared with deep learning strategies, namely recurrent neural networks. Demonstration scenarios are presented based on data obtained from monitoring systems of concrete dams under operation in Portugal.

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Notes

  1. 1.

    Inspections are either of a routine nature, or may follow unusual occurrences, such as earthquakes or large floods.

  2. 2.

    Laboratory and in situ tests, and long term monitoring are used to measure changes in structural properties, actions, and their effects and consequences.

  3. 3.

    A transducer is a device that converts any type of energy into another.

  4. 4.

    New developments must take into account that dam safety is a continuous requirement due to the potential risk in terms of environmental, social and economical disasters.

References

  1. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., Tensorflow: a system for large-scale machine learning. OSDI 16, 265–283 (2016)

    Google Scholar 

  2. A.L. Antunes, E. Cardoso, J. Barateiro, Adding value to sensor data of civil engineering structures: automatic outlier detection, in ML-ISAPR 2018: 1st Workshop on Machine Learning, Intelligent Systems and Statistical Analysis for Pattern Recognition in Real-life Scenarios (2018)

    Google Scholar 

  3. Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult. Lang. Resour. Eval. 5(2), 157–166 (1994)

    Google Scholar 

  4. K.T.T. Bui, D.T. Bui, J. Zou, C. Van Doan, I. Revhaug, A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput. Appl. 29(12), 1495–1506 (2018)

    Article  Google Scholar 

  5. L. Cheng, D. Zheng, Two online dam safety monitoring models based on the process of extracting environmental effect. Adv. Eng. Softw. 57, 48–56 (2013)

    Article  Google Scholar 

  6. F. Chollet et al., Keras (2015)

    Google Scholar 

  7. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa, Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)

    Google Scholar 

  8. B. Dai, C. Gu, E. Zhao, X. Qin, Statistical model optimized random forest regression model for concrete dam deformation monitoring. Struct. Control Health Monit. 25(6), e2170 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. EDP, Design of Alto Lindoso dam (in Portuguese) Technical report EDP - Energias de Portugal, Oporto (1983)

    Google Scholar 

  11. Y. Gal, Z. Ghahramani, Dropout as a Bayesian approximation: representing model uncertainty in deep learning, in International Conference on Machine Learning (2016), pp. 1050–1059

    Google Scholar 

  12. A. Graves, Supervised sequence labelling, in Supervised Sequence Labelling with Recurrent Neural Networks (Springer, Berlin, 2012), pp. 5–13

    Google Scholar 

  13. A. Graves, A.r. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks, in 2013 IEEE International Conference on Acoustics, Speech And Signal Processing (ICASSP) (IEEE, 2013), pp. 6645–6649

    Google Scholar 

  14. X. Guo, J. Baroth, D. Dias, A. Simon, An analytical model for the monitoring of pore water pressure inside embankment dams. Eng. Struct. 160, 356–365 (2018)

    Article  Google Scholar 

  15. M.A. Hariri-Ardebili, F. Pourkamali-Anaraki, Support vector machine based reliability analysis of concrete dams. Soil Dyn. Earthq. Eng. 104, 276–295 (2018)

    Article  Google Scholar 

  16. A.R. Hevner, S.T. March, J. Park, S. Ram, Design science in information systems research. MIS Q 28(1), 75–105 (2004), http://dl.acm.org/citation.cfm?id=2017212.2017217

    Article  Google Scholar 

  17. G. Hinton, N. Srivastava, K. Swersky, Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Coursera (2012a)

    Google Scholar 

  18. G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors (2012b). arXiv:12070580

  19. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  20. V. Hodge, J. Austin, A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)

    Article  Google Scholar 

  21. ICOLD, General considerations on instrumentation for concrete dams. Bulletin number 23. International Commission on Large Dams, Paris (1972)

    Google Scholar 

  22. ICOLD, Ageing of dams and appurtenant works. Review and recommendations, in Bulletin Number 93. International Commission on Large Dams, Paris (1994)

    Google Scholar 

  23. ICOLD, Automated dam monitoring systems - guidelines and case histories, in Bulletin Number 118. International Commission on Large Dams, Paris (2000)

    Google Scholar 

  24. ICOLD, Surveillance: basic elements in a “Dam Safety” process, in Bulletin Number 138. International Commission on Large Dams, Paris (2009)

    Google Scholar 

  25. ISO 14721:2012, Space Data and Information Transfer Systems - Open Archival Information System (OAIS) - Reference Model. Standard, International Organization for Standardization, Geneva, CH (2012)

    Google Scholar 

  26. ISO 16363:2012, Space Data and Information Transfer Systems - Audit and Certification of Trustworthy Digital Repositories. Standard, International Organization for Standardization, Geneva, CH (2012)

    Google Scholar 

  27. I.S. Jung, M. Berges, J. Garrett, J.C. Kelly, 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 - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering (2013), pp. 25–32

    Google Scholar 

  28. N. Kalchbrenner, I. Danihelka, A. Graves, Grid long short-term memory (2015). arXiv:150701526

  29. F. Kang, J. Liu, J. Li, S. Li, Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct. Control Health Monit. 24(10), e1997 (2017)

    Article  Google Scholar 

  30. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105

    Google Scholar 

  31. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  32. N. Leitão, Alto Lindoso dam. Behaviour analysis report (in Portuguese). Technical report, Portuguese National Laboratory for Civil Engineering, Lisbon (2009)

    Google Scholar 

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

    Article  Google Scholar 

  34. F. Li, Z. Wang, G. Liu, C. Fu, J. Wang, Hydrostatic seasonal state model for monitoring data analysis of concrete dams. Struct. Infrastruct. Eng. 11(12), 1616–1631 (2015)

    Article  Google Scholar 

  35. X. Li, H. Su, J. Hu, The prediction model of dam uplift pressure based on random forest, in IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol. 229 (2017), p. 012025

    Article  Google Scholar 

  36. Z.C. Lipton, J. Berkowitz, C. Elkan, A critical review of recurrent neural networks for sequence learning (2015). arXiv:150600019

  37. Z.C. Lipton, D.C. Kale, C. Elkan, R. Wetzel, Learning to diagnose with LSTM recurrent neural networks (2015). arXiv:151103677

  38. M. Ljunggren L. Tim, P. Campbell, Is your dam as safe as your data suggest, in NZSOLD/ANCOLD Conference, vol. 1 (2013)

    Google Scholar 

  39. J. Ma, R.P. Sheridan, A. Liaw, G.E. Dahl, V. Svetnik, Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55(2), 263–274 (2015)

    Article  Google Scholar 

  40. J. Mata, Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng. Struct. 33(3), 903–910 (2011). https://doi.org/10.1016/j.engstruct.2010.12.011, http://www.sciencedirect.com/science/article/pii/S0141029610004839

    Article  Google Scholar 

  41. J. Mata, Structural safety control of concrete dams aided by automated monitoring systems. Ph.D. thesis, Instituto Superior Técnico - Universidade de Lisboa, Lisbon (2013)

    Google Scholar 

  42. J. Mata, T. de A. Castro, Assessment of stored automated measurements in concrete dams. Dam World 2015, Portugal (2015)

    Google Scholar 

  43. J. Mata, A. Tavares de Castro, J. Sá da Costa, Constructing statistical models for arch dam deformation. Struct. Control Health Monit. 21(3), 423–437 (2014)

    Article  Google Scholar 

  44. T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, S. Khudanpur, Recurrent neural network based language model, in Eleventh Annual Conference of the International Speech Communication Association (2010)

    Google Scholar 

  45. V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski et al., Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)

    Article  Google Scholar 

  46. B. Myers, J. Stateler, Why include instrumentation in dam monitoring programs? Technical report, U.S. Society on Dams - Committee on monitoring of dams and their foundations, United States of America (2008)

    Google Scholar 

  47. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, A.Y. Ng, Multimodal deep learning, in Proceedings of the 28th International Conference on Machine Learning (ICML-11) (2011), pp. 689–696

    Google Scholar 

  48. C. Olah, Understanding LSTM networks. GITHUB blog, posted on August 27 2015 (2015)

    Google Scholar 

  49. K. Peffers, T. Tuunanen, M. Rothenberger, S. Chatterjee, A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45–77 (2007). https://doi.org/10.2753/MIS0742-1222240302

    Article  Google Scholar 

  50. F. Perner, P. Obernhuber, Analysis of arch dam deformations. Front. Arch. Civ. Eng. China 4(1), 102–108 (2010)

    Article  Google Scholar 

  51. G. Prakash, A. Sadhu, S. Narasimhan, J.M. Brehe, Initial service life data towards structural health monitoring of a concrete arch dam. Struct. Control Health Monit. 25(1), e2036 (2018)

    Article  Google Scholar 

  52. D. Quang, X. Xie, Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Nucleic Acids Res. 44(11), e107–e107 (2016)

    Article  Google Scholar 

  53. V. Ranković, N. Grujović, D. Divac, N. Milivojević, Development of support vector regression identification model for prediction of dam structural behaviour. Struct. Saf. 48, 33–39 (2014)

    Article  Google Scholar 

  54. V. Ranković, A. Novaković, N. Grujović, D. Divac, N. Milivojević, Predicting piezometric water level in dams via artificial neural networks. Neural Comput. Appl. 24(5), 1115–1121 (2014)

    Article  Google Scholar 

  55. F. Salazar, M. Toledo, E. Oñate, R. Morán, An empirical comparison of machine learning techniques for dam behaviour modelling. Struct. Saf. 56, 9–17 (2015)

    Article  Google Scholar 

  56. F. Salazar, M.Á. Toledo, E. Oñate, B. Suárez, Interpretation of dam deformation and leakage with boosted regression trees. Eng. Struct. 119, 230–251 (2016)

    Article  Google Scholar 

  57. F. Salazar, R. Morán, M.Á. Toledo, E. Oñate, Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch. Comput. Methods Eng. 24(1), 1–21 (2017)

    Article  Google Scholar 

  58. F. Salazar, M.Á. Toledo, J.M. González, E. Oñate, Early detection of anomalies in dam performance: a methodology based on boosted regression trees. Struct. Control Health Monit. 24(11), e2012 (2017)

    Article  Google Scholar 

  59. H. Salehi, R. Burgueño, Emerging artificial intelligence methods in structural engineering. Eng. Struct. 171, 170–189 (2018)

    Article  Google Scholar 

  60. B. Stojanovic, M. Milivojevic, M. Ivanovic, N. Milivojevic, D. Divac, Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv. Eng. Softw. 65, 182–190 (2013)

    Article  Google Scholar 

  61. H. Su, X. Li, B. Yang, Z. Wen, Wavelet support vector machine-based prediction model of dam deformation. Mech. Syst. Signal Process. 110, 412–427 (2018)

    Article  Google Scholar 

  62. I. Sutskever, O. Vinyals, Q.V. Le, Sequence to sequence learning with neural networks, in Advances in Neural Information Processing Systems (2014), pp. 3104–3112

    Google Scholar 

  63. Swiss Committee on Dams, Methods of analysis for the prediction and the verification of dam behaviour, in 21st Congress of the International Commission on Large Dams, Montreal, Switzerland (2003)

    Google Scholar 

  64. M. Tatin, M. Briffaut, F. Dufour, A. Simon, J.P. Fabre, Thermal displacements of concrete dams: accounting for water temperature in statistical models. Eng. Struct. 91, 26–39 (2015)

    Article  Google Scholar 

  65. M. Tatin, M. Briffaut, F. Dufour, A. Simon, J.P. Fabre, Statistical modelling of thermal displacements for concrete dams: influence of water temperature profile and dam thickness profile. Eng. Struct. 165, 63–75 (2018)

    Article  Google Scholar 

  66. G. Tayfur, D. Swiatek, A. Wita, V.P. Singh, Case study: finite element method and artificial neural network models for flow through jeziorsko earthfill dam in Poland. J. Hydraul. Eng. 131(6), 431–440 (2005)

    Article  Google Scholar 

  67. H. Wang, N. Wang, D.Y. Yeung, Collaborative deep learning for recommender systems, in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2015), pp 1235–1244

    Google Scholar 

  68. C. Xu, D. Yue, C. Deng, Hybrid GA/SIMPLE as alternative regression model in dam deformation analysis. Eng. Appl. Artif. Intell. 25(3), 468–475 (2012). https://doi.org/10.1016/j.engappai.2011.09.020, http://www.sciencedirect.com/science/article/pii/S0952197611001734

    Article  Google Scholar 

  69. K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, Y. Bengio, Show, attend and tell: neural image caption generation with visual attention, in International Conference on Machine Learning (2015), pp. 2048–2057

    Google Scholar 

  70. J. Yang, M.N. Nguyen, P.P. San, X. Li, S. Krishnaswamy, Deep convolutional neural networks on multichannel time series for human activity recognition. IJCAI 15, 3995–4001 (2015)

    Google Scholar 

  71. H. Yu, Z. Wu, T. Bao, L. Zhang, Multivariate analysis in dam monitoring data with PCA. Sci. China Technol. Sci. 53(4), 1088–1097 (2010)

    Article  Google Scholar 

  72. J. Zhang, Y. Zheng, D. Qi, Deep spatio-temporal residual networks for citywide crowd flows prediction, in AAAI (2017), pp. 1655–1661

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratório Nacional de Engenharia Civil, Av. Brasil, 101, Lisbon, Portugal

    João Rico, José Barateiro, Juan Mata & António Antunes

  2. INESC-ID, R. Alves Redol, 9, Lisbon, Portugal

    José Barateiro & Elsa Cardoso

  3. ISCTE-Instituto Universitário de Lisboa, Av. Forças Armadas, Lisbon, Portugal

    Elsa Cardoso

Authors
  1. João Rico
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  2. José Barateiro
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  3. Juan Mata
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  4. António Antunes
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  5. Elsa Cardoso
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Corresponding author

Correspondence to José Barateiro .

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Editors and Affiliations

  1. University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  3. University of Piraeus, Piraeus, Greece

    Evangelos Sakkopoulos

  4. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, NSW, Australia

    Lakhmi C. Jain

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Rico, J., Barateiro, J., Mata, J., Antunes, A., Cardoso, E. (2019). Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams. In: Tsihrintzis, G., Virvou, M., Sakkopoulos, E., Jain, L. (eds) Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-15628-2_10

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