Buildings - Special Topics in Structures

Full Session with Abstracts

339370-2 - Failure-dependent shear strength model for RC beam column joints using Machine Learning Techniques

Saturday, April 21
8:00 AM - 9:30 AM
Location: 202A

Beam-column joints are one of the critical elements that govern the overall performance of a building under the seismic loading. This paper presents the application of machine learning techniques to predict the mode of failure and shear strength of beam-column joints. To evaluate the efficiency of various machine learning techniques, extensive experimental data comprising of 536 experimental tests was used. It has been seen from the comparison that Lasso Regression has the better efficiency in classification and prediction. An easy to use equations are suggested in this paper for identifying the failure mode and to estimate the shear strength capacity. The suggested equation can be used by the engineers to determine the mode of failure and associated shear strength of beam-column joints.

Sujith Mangalathu

Post-doctoral Scholar
University of California, Los Angeles

An engineer with extensive research experiences in finite element modeling, static and dynamic analysis of
structures, probabilistic risk assessment of structures, machine learning and statistical methods.

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339370-2 - Failure-dependent shear strength model for RC beam column joints using Machine Learning Techniques



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