Machine learning-based failure mode identification of double shear bolted connections in structural steel

• Failure modes of 455 tested double shear bolted connections of structural steel are categorized. • 10 machine learning models are employed to predict the failure modes of double shear bolted connections. • 10-fold cross-validations were adopted for the model’s performance evaluation. • RF, CatBoost, XGBoost, and GB attained an accuracy of 90–92% on the failure mode classification. • Edge distance to bolt diameter (e 2 /d 0 ) is the most influential in controlling the failure types. The design of double shear bolted connections in structural steel is governed by four different failure modes; tear out, splitting, net-section, and bearing. Ten machine learning (ML) approaches were explored on a comprehensive database of 455 experimental results for identifying the failure modes of double shear bolted connections. Among them, Random Forest (RF), CatBoost, XGBoost, and Gradient Boosting (GB) attained 90–92% accuracy on the testing dataset for classifying the failure modes. The best-performing models revealed that the ratio of the edge distance-to-bolt diameter ( e 2 /d 0 ) is the most important feature with an influence of nearly 30% on the failure mode of the connections. Interestingly, the number of bolt rows in a connection also influences the failure mode, which was not captured by existing equations and design codes. Finally, a user interface capturing all proposed ML models was developed to identify the failure modes of double shear bolted connections.