Show simple item record

dc.contributor.authorCakiroglu, Celal
dc.contributor.authorGeem, Zong Woo
dc.contributor.authorKim, Sanghun
dc.contributor.authorBEKDAŞ, GEBRAİL
dc.date.accessioned2023-10-10T12:25:11Z
dc.date.available2023-10-10T12:25:11Z
dc.date.issued2023
dc.identifier.citationBEKDAŞ G., Cakiroglu C., Kim S., Geem Z. W., "Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP", SUSTAINABILITY, sa.10, 2023
dc.identifier.issn2071-1050
dc.identifier.otherav_200adf7e-f397-4fca-b933-2d54de2a118a
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/190073
dc.identifier.urihttps://avesis.istanbul.edu.tr/api/publication/200adf7e-f397-4fca-b933-2d54de2a118a/file
dc.identifier.urihttps://doi.org/10.3390/su15107890
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.language.isoeng
dc.subjectTarımsal Bilimler
dc.subjectÇevre Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.subjectGenel Sosyal Bilimler
dc.subjectDoğa ve Peyzaj Koruma
dc.subjectYönetim, İzleme, Politika ve Hukuk
dc.subjectÇevre Bilimi (çeşitli)
dc.subjectSu Bilimi
dc.subjectFizik Bilimleri
dc.subjectSosyal Bilimler ve Beşeri Bilimler
dc.subjectYaşam Bilimleri
dc.subjectSosyal ve Beşeri Bilimler
dc.subjectSosyoloji
dc.subjectSosyal Bilimler Genel
dc.subjectÇEVRE ÇALIŞMALARI
dc.subjectÇEVRE BİLİMLERİ
dc.subjectTarım ve Çevre Bilimleri (AGE)
dc.subjectÇevre / Ekoloji
dc.subjectYEŞİL VE SÜRDÜRÜLEBİLİR BİLİM VE TEKNOLOJİ
dc.subjectSosyal Bilimler (SOC)
dc.titleOptimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP
dc.typeMakale
dc.relation.journalSUSTAINABILITY
dc.contributor.departmentİstanbul Üniversitesi-Cerrahpaşa , Mühendislik Fakültesi , İnşaat Mühendisliği Bölümü
dc.identifier.issue10
dc.contributor.firstauthorID4314021


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record