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Anomaly Detection in Dam Behaviour with Machine Learning Classification Models
Article

Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction

1
National Laboratory for Civil Engineering (LNEC), Avenida do Brasil, 101, 1700-066 Lisbon, Portugal
2
International Center for Numerical Methods in Engineering (CIMNE), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Zhi-jun Dai
Water 2021, 13(19), 2717; https://doi.org/10.3390/w13192717
Received: 30 July 2021 / Revised: 24 September 2021 / Accepted: 25 September 2021 / Published: 1 October 2021
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
The main aim of structural safety control is the multiple assessments of the expected dam behaviour based on models and the measurements and parameters that characterise the dam’s response and condition. In recent years, there is an increase in the use of data-based models for the analysis and interpretation of the structural behaviour of dams. Multiple Linear Regression is the conventional, widely used approach in dam engineering, although interesting results have been published based on machine learning algorithms such as artificial neural networks, support vector machines, random forest, and boosted regression trees. However, these models need to be carefully developed and properly assessed before their application in practice. This is even more relevant when an increase in users of machine learning models is expected. For this reason, this paper presents extensive work regarding the verification and validation of data-based models for the analysis and interpretation of observed dam’s behaviour. This is presented by means of the development of several machine learning models to interpret horizontal displacements in an arch dam in operation. Several validation techniques are applied, including historical data validation, sensitivity analysis, and predictive validation. The results are discussed and conclusions are drawn regarding the practical application of data-based models. View Full-Text
Keywords: concrete dam; machine learning methods; structural behaviour; sensitivity analysis; model validation concrete dam; machine learning methods; structural behaviour; sensitivity analysis; model validation
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MDPI and ACS Style

Mata, J.; Salazar, F.; Barateiro, J.; Antunes, A. Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction. Water 2021, 13, 2717. https://doi.org/10.3390/w13192717

AMA Style

Mata J, Salazar F, Barateiro J, Antunes A. Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction. Water. 2021; 13(19):2717. https://doi.org/10.3390/w13192717

Chicago/Turabian Style

Mata, Juan, Fernando Salazar, José Barateiro, and António Antunes. 2021. "Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction" Water 13, no. 19: 2717. https://doi.org/10.3390/w13192717

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