Data-driven design approaches for hollow section columns—Database analysis and implementation

Abstract

Structural engineering has a plethora of existing data from previous experiments and computational modeling results, yet the benefits of employing data methods in structural engineering are still largely unexplored. As a test case to demonstrate the use of data-driven design approaches in structural engineering, this study applies both conventional interpolation and advanced machine learning techniques, Extreme Gradient Boosting and Multi-layer Perceptron (MLP), to estimate capacity strength of SHS and RHS columns using a comprehensive database consisting of 695 experimental results and 3,794 finite element (FE) analysis results. The database covers a wide range of material and geometric properties, including steel grades ranging from normal-strength to high-strength steel, cross-sectional dimensions, member slenderness, and forming process (cold-formed or hot-rolled). The impact of data source (experiment or FE models) and ratios of training to testing sets on the model prediction accuracy are explored. The best model predictions are also compared to predictions from established design standards including AISC 360 and Eurocode 3. It was found that the MLP model performed the best among the data driven models and the MLP predictions across the range of member slenderness ratios, and steel grades, and forming methods performed better than either established design standard, indicating the potential benefits of using advanced data methods. To demonstrate the future potential of how data-driven design methods can enhance structural engineering design, the developed models and database are available in a public repository and a practical example of how to use the database is detailed.