Failure mode identification of column base plate connection using data-driven machine learning techniques

[Display omitted] •Data-driven machine learning (ML) approach for the failure mode identification of column base plate (CBP) connection.•Comprehensive experimental database for steel CBP connections.•Comparison of various ML algorithms for predicting the CBP failure mode.•Determine the influential i...

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Bibliographic Details
Published in:	Engineering structures 2021-08, Vol.240, p.112389, Article 112389
Main Authors:	Asif Bin Kabir, Md, Sajid Hasan, Ahmed, Muntasir Billah, AHM
Format:	Article
Language:	eng
Subjects:	Algorithms Base plates Bayesian analysis Classification Classification model Column base connection Damage assessment Decision trees Diameters Failure mode Failure modes Learning algorithms Machine learning Mathematical models Parameters Retrofitting Steel structure Steel structures Structural members Support vector machines
Online Access:	Get full text
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Summary:	[Display omitted] •Data-driven machine learning (ML) approach for the failure mode identification of column base plate (CBP) connection.•Comprehensive experimental database for steel CBP connections.•Comparison of various ML algorithms for predicting the CBP failure mode.•Determine the influential input parameters affecting CBP connection failure mode.•Comparison of ML model with available empirical equations to identify the CBP failure mode.•Provide an open-source data-driven machine learning model with an easy to use GUI tool. Column base plate (CBP) connection is one of the most important structural elements of steel structures since the failure of these base plate connections can result in the collapse of the entire structure. The prediction of failure mode of CBP connection plays a significant role in ductile behavior of the structure which is critical for damage assessment or retrofitting strategies after any natural hazard. This study introduces a rapid failure mode identification technique for CBP connections by exploring the recent advances in machine learning (ML) techniques. A comprehensive database is assembled with 189 available experimental results for CBP connections including various parameters affecting the CBP behavior. To establish the best classification model, a total of nine different ML algorithms such as Support vector machine, Naïve bayes, K-nearest neighbors, Decision tree, Random forest, Adaboost, XGboost, LightGBM, and Catboost are considered in this study. Comparing the developed ML models, the Decision tree based ML model is suggested in this study which has an overall accuracy of 91% for identifying the failure mode of CBP connections. It is also found that base plate thickness, embedment length, and anchor rod diameter are the influential parameters that govern the failure mode of CBP connections. Furthermore, an open-source classification model is provided to rapidly identify the failure mode of CBP connection by allowing modifications for future developments.
ISSN:	0141-0296 1873-7323