Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. (1st April 2018)

• Record Type: Journal Article
• Authors: Mangalathu, Sujith; Jeon, Jong-Su
• Is Part Of: Engineering structures. Volume 160(2018)
• Page Start: 85
• Page End: 94
• Publication Date: 2018-04-01
• Subjects: Beam-column joints -- Joint shear failure -- Failure mode -- Machine learning -- Probabilistic models; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.01.008
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Identification of mode of failure of beam-column joints through machine learning techniques. Probabilistic models to capture the type of failure and shear strength of beam-column joints. Sensitivity of input variables to joint shear strength. Comparison of various machine learning techniques to estimate the shear strength of beam-column joints. Abstract: Beam-column joints are one of critical components that control the oveerall performance of reinforced concrete building frames under seismic loadings. To identify the response mechanism, including the classification of failure mode and the prediction of associated shear strength, of beam-column joints, this paper introduces the application of machine learning techniques. The efficiency of various machine learning techniques is evaluated using extensive experimental data from 536 experimental tests, all of which exhibited either non-ductile joint shear failure prior to beam yielding or ductile joint shear failure after beam yielding. It has been seen from the comparison that lasso regression has a better efficiency and reasonable accuracy in the classification and prediction. The suggested formulations as a function of influential input variables can be easily used by structural engineers to provide an optimal rehabilitation strategy for existing buildings and to design new structures.