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

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Abstract

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.

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Notes

  1. Traditionally, the statistical models applied in dam monitoring analysis were based on causal variables, e.g. hydrostatic load and temperature, which are often termed “independent variables”. On the contrary, other algorithms make use of transformed variables (such as gradients or moving averages), and non-causal observations (e.g. the previous value of the output). This has led to the use of various terms to refer to the model inputs, such as “predictors”, “covariates”, and “features”. In this paper they are used indistinctly.

  2. In the 6th ICOLD Benchmark Workshop, the participants were asked to provide a data-based model for predicting the radial displacement of Schlegeiss arch dam for the period 1999–2000. Time histories of water level, air temperature and concrete temperatures at various locations were provided for the period 1992–2000, as well as the observed values of the target variable for the period 1992–1998.

  3. the terminology is not universal; the data which are not used to fit the model is sometimes called test or prediction set.

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Acknowledgments

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).

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Salazar, F., Morán, R., Toledo, M.Á. et al. Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations. Arch Computat Methods Eng 24, 1–21 (2017). https://doi.org/10.1007/s11831-015-9157-9

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