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

Journal Article


Abstract


  • 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.

Publication Date


  • 2022

Citation


  • Zakir Sarothi, S., Sakil Ahmed, K., Imtiaz Khan, N., Ahmed, A., & Nehdi, M. L. (2022). Machine learning-based failure mode identification of double shear bolted connections in structural steel. Engineering Failure Analysis, 139. doi:10.1016/j.engfailanal.2022.106471

Scopus Eid


  • 2-s2.0-85131068976

Volume


  • 139

Issue


Place Of Publication


Abstract


  • 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.

Publication Date


  • 2022

Citation


  • Zakir Sarothi, S., Sakil Ahmed, K., Imtiaz Khan, N., Ahmed, A., & Nehdi, M. L. (2022). Machine learning-based failure mode identification of double shear bolted connections in structural steel. Engineering Failure Analysis, 139. doi:10.1016/j.engfailanal.2022.106471

Scopus Eid


  • 2-s2.0-85131068976

Volume


  • 139

Issue


Place Of Publication