Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP

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dc.authoridCakiroglu, Celal/0000-0001-7329-1230
dc.authoridBekdas, Gebrail/0000-0002-7327-9810
dc.authoridGeem, Zong Woo/0000-0002-0370-5562
dc.authoridKim, Sanghun/0000-0002-1423-6116
dc.contributor.authorBekdas, Gebrail
dc.contributor.authorCakiroglu, Celal
dc.contributor.authorKim, Sanghun
dc.contributor.authorGeem, Zong Woo
dc.date.accessioned2025-02-20T08:42:13Z
dc.date.available2025-02-20T08:42:13Z
dc.date.issued2023
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractThe optimal design of prestressed concrete cylindrical walls is greatly beneficial for economic and environmental impact. However, the lack of the available big enough datasets for the training of robust machine learning models is one of the factors that prevents wide adoption of machine learning techniques in structural design. The current study demonstrates the application of the well-established harmony search methodology to create a large database of optimal design configurations. The unit costs of concrete and steel used in the construction, the specific weight of the stored fluid, and the height of the cylindrical wall are the input variables whereas the optimum thicknesses of the wall with and without post-tensioning are the output variables. Based on this database, some of the most efficient ensemble learning techniques like the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Gradient Boosting (CatBoost) and Random Forest algorithms have been trained. An R-2 score greater than 0.98 could be achieved by all of the ensemble learning models. Furthermore, the impacts of different input features on the predictions of different machine learning models have been analyzed using the SHapley Additive exPlanations (SHAP) methodology. The height of the cylindrical wall was found to have the greatest impact on the optimal wall thickness, followed by the specific weight of the stored fluid. Also, with the help of individual conditional expectation (ICE) plots the variations of predictive model outputs with respect to each input feature have been visualized. By using the genetic programming methodology, predictive equations have been obtained for the optimal wall thickness.
dc.identifier.doi10.3390/su15107890
dc.identifier.issn2071-1050
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85160777918
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su15107890
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1567
dc.identifier.volume15en_US
dc.identifier.wosWOS:000998020300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250220
dc.subjectoptimizationen_US
dc.subjectmachine learningen_US
dc.subjectXGBoosten_US
dc.subjectSHAPen_US
dc.subjectprestressed concreteen_US
dc.subjectpost-tensioningen_US
dc.subjectgenetic programmingen_US
dc.titleOptimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP
dc.typeArticle

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