Optimum Design of Cylindrical Walls Using Ensemble Learning Methods

Applied Sciences

Zong Woo Geem¹  Gebrail Bekdaş²  Celal Cakiroglu³  Sanghun Kim⁴  Kamrul Islam⁵ 

[1] College of IT Convergence, Gachon University, Seongnam 13120, Korea;Department of Civil Engineering, Istanbul University-Cerrahpasa, Istanbul 34320, Turkey;Department of Civil Engineering, Turkish-German University, Istanbul 34820, Turkey;Department of Civil and Environmental Engineering, Temple University, Philadelphia, PA 19122, USA;Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, Montréal, QC H3C 3A7, Canada;

关键词: machine learning;    optimization;    harmony search;    shell structures;   

DOI  :  10.3390/app12042165

来源: DOAJ

【 摘 要 】

The 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.

【 授权许可】

Unknown