Fusion of experimental and synthetic data for reliable prediction of steel connection behaviour using machine learning

The development of robust prediction tools based on machine learning (ML) techniques requires the availability of complete, consistent, accurate, and numerous datasets. The application of ML in structural engineering has been limited since, although real size experiments provide complete and accurate data, they are time-consuming and expensive. On the other hand, validated finite element (FE) models provide consistent and numerous synthetic data. Depending on the complexity of the problem, they might require large computational time and cost, and could be subjected to uncertainties and limitation in prediction capability given they are approximations of real-world problems. Hybrid approaches to combine experimental and synthetic datasets have emerged as an alternative to improve the reliability of ML model predictions. In this paper, we explore two hybrid methods to propose a robust approach for the prediction of the extended hollo-bolt (EHB) connection strength, stiffness, and column face displacement: (1) supervised ML methods with data fusion (DF) where learning is optimized with particle swarm optimization (PSO), and (2) artificial neural networks (ANN) based method with model fusion (MF). Based on the analysis of a dataset that combines 22 tensile experimental results with 2000 synthetic datapoints based on FE models, we concluded that using the first method (ML with DF and PSO) is the most suitable method for the prediction of the connection behavior. The ANN-based method with MF shows to be a promising method for the characterization of the EHB connection, however, more extensive experimental data is required for its implementation. Finally, a graphical user interface application was developed and shared in a public repository for the implementation of the proposed hybrid model.