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Multioutput Regression for Reinforced Retaining Wall Optimum Design with Machine Learning
| dc.contributor.author | Aydın, Yaren | |
| dc.contributor.author | Bekdaş, Gebrail | |
| dc.contributor.author | Nigdeli, Sinan Melih | |
| dc.contributor.author | Işıkdağ, Ümit | |
| dc.date.accessioned | 2026-01-16T07:18:23Z | |
| dc.date.available | 2026-01-16T07:18:23Z | |
| dc.date.issued | 2026 | en_US |
| dc.identifier.uri | https://doi.org/10.1007/978-3-032-07738-7_5 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14124/10339 | |
| dc.description.abstract | This study aims to predict optimum reinforced concrete wall dimensions and cost by using the height of the wall (H) and surcharge load (q) inputs using Multioutput Regression. The base learner models for Multioutput Regression used in this study are Decision Tree Regression (DTR), Support Vector Regression (SVR), Elastic Net Regression (ENT) and Histogram Gradient Boosting Regression (HGBR). Coefficient of Determination (R2), Mean Absolute Error (MAE) and Mean Squared Error (MSE) are used for performance evaluation. Among these models, DTR showed the highest prediction performance (R2: 0.855, MSE: 3930.22, MAE: 18.09). | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer International Publishing AG | en_US |
| dc.relation.ispartof | Studies in Systems, Decision and Control | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Decision tree | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Multioutput regression | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Retaining wall | en_US |
| dc.title | Multioutput Regression for Reinforced Retaining Wall Optimum Design with Machine Learning | en_US |
| dc.type | bookPart | en_US |
| dc.department | Fakülteler, Mimarlık Fakültesi, Mimarlık Bölümü | en_US |
| dc.institutionauthor | Işıkdağ, Ümit | |
| dc.identifier.doi | 10.1007/978-3-032-07738-7_5 | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.identifier.scopus | 2-s2.0-105026794957 | en_US |
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