Evaluation of machine learning models for load-carrying capacity assessment of semi-rigid steel structures

• Published in: Engineering structures 2022-12, Vol.273, p.115001, Article 115001
• Main Authors: Truong, Viet-Hung, Pham, Hoang-Anh, Huynh Van, Thu, Tangaramvong, Sawekchai
• Format: Article
• Language: English
• Subjects: Algorithms; Beam-columns; Bearing strength; Carrying capacity; Deep learning; Failure analysis; Learning algorithms; Load carrying capacity; Load distribution; Machine learning; Regression analysis; Regression models; Semi-rigid connections; Semi-rigid steel connection; Steel frames; Steel structures; Support vector machines; Tree-based ensemble method
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2022.115001

•Twelve ML models are investigated to map out semi-rigid steel structural behaviors.•Distribution of load-carrying capacity responses is non-normal and heavy-tailed.•Linear regression methods are less efficient than tree-based ensemble algorithms.•XGBoost algorithm presents the best performance among all the methods investigated. The paper investigates the potential application of machine learning methods to estimate the load-carrying capacity of semi-rigid connected steel structures. The database is developed using the advanced analysis based on beam-column and zero-length elements. The input variables are member cross-sections and parameters of the high-order nonlinear functions describing semi-rigid connection behaviors. Twelve machine learning algorithms, including three linear regression models, support vector machines, deep learning and seven tree-based ensemble algorithms, were evaluated. Three practical semi-rigid connected steel (including two-dimensional planar and three-dimensional space) structures were studied. The numerical results revealed that the distribution of the load-carrying capacity responses of the structures was non-normal and heavy-tailed. The class of linear regression methods was less efficient than the tree-based ensemble algorithms. Among the seven tree-based ensemble approaches, the extreme gradient boosting method presented the best performance in determining not only the lowest (mean squared, mean absolute and mean absolute percentage) errors but also the highest coefficient of determination. It was followed by the light gradient boosting machines and categorical gradient boosting algorithms. Three linear regression methods (linear regression, Lasso and Ridge), light gradient boosting machines, categorical gradient boosting and extreme gradient boosting provided the competitive computing cost. The extreme gradient boosting algorithm was therefore recommended for the accurate prediction of load-carrying capacities of semi-rigid connected steel frames.