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

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 (e2/d0) 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.

Automated summary

This study focuses on the design of double shear bolted connections in structural steel. By utilizing machine learning techniques, the researchers were able to identify and classify the four different failure modes of these connections. A user interface was also developed to facilitate the identification of failure modes.