Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming
The physics of local scour around bridge piers is fairly complex because of multiple forces acting on it. Existing empirical formulas cannot cover all scenarios and soft computing methods require ever greater amounts of data to cover all cases with a single formula or a neural network. The approach...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.2001734 |
| _version_ | 1797641092458348544 |
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| author | Kaya Oğuz Aslı Bor |
| author_facet | Kaya Oğuz Aslı Bor |
| author_sort | Kaya Oğuz |
| collection | DOAJ |
| description | The physics of local scour around bridge piers is fairly complex because of multiple forces acting on it. Existing empirical formulas cannot cover all scenarios and soft computing methods require ever greater amounts of data to cover all cases with a single formula or a neural network. The approach proposed in this study brings together observations from over 40 studies, grouping similar observations with hierarchical clustering, and using genetic programming with adaptive operators to evolve formulas specific to each cluster to predict the scour depth. The resulting formulas are made available along with a basic web-based user interface that finds the closest cluster for newly presented data and finds the scour depth using the formula for that cluster. All formulas have R2 scores over 0.8 and have been validated with validation and testing sets to reduce overfitting. When compared to existing empirical formulas, the generated formulas consistently record higher R2 scores. |
| first_indexed | 2024-03-11T13:40:33Z |
| format | Article |
| id | doaj.art-9b930670c33643e6a0471d50cb85c8ab |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:33Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-9b930670c33643e6a0471d50cb85c8ab2023-11-02T13:36:37ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2021.20017342001734Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic ProgrammingKaya Oğuz0Aslı Bor1Izmir University of EconomicsIzmir University of EconomicsThe physics of local scour around bridge piers is fairly complex because of multiple forces acting on it. Existing empirical formulas cannot cover all scenarios and soft computing methods require ever greater amounts of data to cover all cases with a single formula or a neural network. The approach proposed in this study brings together observations from over 40 studies, grouping similar observations with hierarchical clustering, and using genetic programming with adaptive operators to evolve formulas specific to each cluster to predict the scour depth. The resulting formulas are made available along with a basic web-based user interface that finds the closest cluster for newly presented data and finds the scour depth using the formula for that cluster. All formulas have R2 scores over 0.8 and have been validated with validation and testing sets to reduce overfitting. When compared to existing empirical formulas, the generated formulas consistently record higher R2 scores.http://dx.doi.org/10.1080/08839514.2021.2001734 |
| spellingShingle | Kaya Oğuz Aslı Bor Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming Applied Artificial Intelligence |
| title | Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming |
| title_full | Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming |
| title_fullStr | Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming |
| title_full_unstemmed | Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming |
| title_short | Prediction of Local Scour around Bridge Piers Using Hierarchical Clustering and Adaptive Genetic Programming |
| title_sort | prediction of local scour around bridge piers using hierarchical clustering and adaptive genetic programming |
| url | http://dx.doi.org/10.1080/08839514.2021.2001734 |
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