Machine-learning-assisted design of high strength steel I-section columns.

High strength steel has been attracting attention in the building industry due to its superior mechanical properties. The accurate design of high strength steel structures is crucial to boost its wide application. In this paper, an accurate and unified design approach for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions (including cross-section sizes and member lengths) and failure modes is proposed based on machine learning. Firstly, 871 experimental and numerical data were collected from the literature to establish a database. Then, seven machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Adaptive Boosting, Extreme Gradient Boosting and Categorical Boosting, were applied to establish machine learning regression models to predict buckling resistances of high strength steel I-section columns. The model performance was then evaluated through statistic indices, with the evaluation results indicating that the Categorical Boosting trained model yields the highest level of accuracy. Based on the data in the collected database, the regression model trained by Categorical Boosting and existing codified design provisions, as given in the European code and American specification, were assessed and compared. The European code and American specification were found to yield scattered and inaccurate failure load predictions, while the Categorical Boosting trained model led to substantially more accurate and consistent failure load predictions for high strength steel I-section columns with different material grades, boundary conditions, geometric dimensions and failure modes. • A database including 871 structural performance data on high strength steel I-section columns was established. • Regression models were developed by using machine learning algorithms to predict load-carrying capacities. • The regression models were evaluated and the Categorical Boosting model provided the best performance. • The machine learning design method yielded improved design accuracy than the existing design codes. [ABSTRACT FROM AUTHOR]