Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction
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 inter...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/19/2717 |
| _version_ | 1797515652716560384 |
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| author | Juan Mata Fernando Salazar José Barateiro António Antunes |
| author_facet | Juan Mata Fernando Salazar José Barateiro António Antunes |
| author_sort | Juan Mata |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:48:02Z |
| format | Article |
| id | doaj.art-389c1a16064d426697faad89ffbf5911 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T06:48:02Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-389c1a16064d426697faad89ffbf59112023-11-22T17:01:34ZengMDPI AGWater2073-44412021-10-011319271710.3390/w13192717Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and PredictionJuan Mata0Fernando Salazar1José Barateiro2António Antunes3National Laboratory for Civil Engineering (LNEC), Avenida do Brasil, 101, 1700-066 Lisbon, PortugalInternational Center for Numerical Methods in Engineering (CIMNE), Universitat Politècnica de Catalunya, 08034 Barcelona, SpainNational Laboratory for Civil Engineering (LNEC), Avenida do Brasil, 101, 1700-066 Lisbon, PortugalNational Laboratory for Civil Engineering (LNEC), Avenida do Brasil, 101, 1700-066 Lisbon, PortugalThe 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.https://www.mdpi.com/2073-4441/13/19/2717concrete dammachine learning methodsstructural behavioursensitivity analysismodel validation |
| spellingShingle | Juan Mata Fernando Salazar José Barateiro António Antunes Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction Water concrete dam machine learning methods structural behaviour sensitivity analysis model validation |
| title | Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction |
| title_full | Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction |
| title_fullStr | Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction |
| title_full_unstemmed | Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction |
| title_short | Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction |
| title_sort | validation of machine learning models for structural dam behaviour interpretation and prediction |
| topic | concrete dam machine learning methods structural behaviour sensitivity analysis model validation |
| url | https://www.mdpi.com/2073-4441/13/19/2717 |
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