Optimum design of cylindrical walls using ensemble learning methods

Basit Öğe Kaydı
dc.authorid0000-0001-7329-1230
dc.contributor.authorBekdaş, Gebrail
dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorIslam, Kamrul
dc.contributor.authorKim, Sanghun
dc.contributor.authorGeem, Zong Woo
dc.date.accessioned2022-10-27T08:48:24Z
dc.date.available2022-10-27T08:48:24Z
dc.date.issued2022
dc.departmentTAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe optimum cost of the structure design is one of the major goals of structural engineers. The availability of large datasets with preoptimized structural configurations can facilitate the process of optimum design significantly. The current study uses a dataset of 7744 optimum design configurations for a cylindrical water tank. Each of them was obtained by using the harmony search algorithm. The database used contains unique combinations of height, radius, total cost, material unit cost, and corresponding wall thickness that minimize the total cost. It was used to create ensemble learning models such as Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Gradient Boosting (CatBoost). Generated machine learning models were able to predict the optimum wall thickness corresponding to new data with high accuracy. Using SHapely Additive exPlanations (SHAP), the height of a cylindrical wall was found to have the greatest impact on the optimum wall thickness followed by radius and the ratio of concrete unit cost to steel unit cost.
dc.identifier.citationThe optimum cost of the structure design is one of the major goals of structural engineers. The availability of large datasets with preoptimized structural configurations can facilitate the process of optimum design significantly. The current study uses a dataset of 7744 optimum design configurations for a cylindrical water tank. Each of them was obtained by using the harmony search algorithm. The database used contains unique combinations of height, radius, total cost, material unit cost, and corresponding wall thickness that minimize the total cost. It was used to create ensemble learning models such as Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Gradient Boosting (CatBoost). Generated machine learning models were able to predict the optimum wall thickness corresponding to new data with high accuracy. Using SHapely Additive exPlanations (SHAP), the height of a cylindrical wall was found to have the greatest impact on the optimum wall thickness followed by radius and the ratio of concrete unit cost to steel unit cost.
dc.identifier.doi10.3390/app12042165
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85124991199
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12846/659
dc.identifier.volume12en_US
dc.identifier.wosWOS:000767553300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇakıroğlu, Celal
dc.language.isoen
dc.publisherMDPI-Multidisciplinary Digital Publishing Institute
dc.relation.ispartofApplied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMachine Learningen_US
dc.subjectOptimizationen_US
dc.subjectHarmony Searchen_US
dc.subjectShell Structuresen_US
dc.subjectMaschinelles Lernenen_US
dc.subjectOptimierungen_US
dc.subjectHarmonie Sucheen_US
dc.subjectShell Strukturenen_US
dc.subjectMakine Öğrenimien_US
dc.subjectOptimizasyonen_US
dc.subjectKabuk Yapılaren_US
dc.titleOptimum design of cylindrical walls using ensemble learning methods
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
applsci-12-02165-v2.pdf
Boyut:
6.22 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
İnşaat Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu