Ensemble machine learning-based sensitivity and parametric assessment of headed stud shear connectors behavior in composite construction.

Indeed, understanding the behavior of headed stud shear connectors in composite steel and concrete construction is essential for ensuring structural integrity and optimal performance. This research focuses on the sensitivity and parametric assessment of the behavior of headed stud shear connectors in composite steel and concrete construction using ensemble machine learning techniques. The study aims to uncover hidden correlations and patterns in the data using a detailed database from 464 push tests, where connectors are welded within the ribs of both trapezoidal and re-entrant steel decks. These patterns provide insights into the performance of shear connectors under various conditions, including different welding methods. The application of ensemble machine learning offers an opportunity to understand complex relationships between variables that may not be immediately evident through conventional analysis. Within the study context, eight types of ensemble machine learning models are implemented and applied to estimate the shear capacity of shear studs and conduct feature importance and partial dependence analysis. The outcomes of this research contribute to a deeper understanding of the factors influencing the performance of shear connectors, providing valuable input for structural design and evaluation in composite construction practices. As a result, this research not only enriches the current academic discourse on shear connectors but also offers pragmatic insights for professionals in the field, thereby bridging the gap between theoretical research and real-world applications in composite construction practices.