Prediction of performance of exterior beam-column connections with headed bars subject to load reversal

• Published in: Engineering structures Vol. 41; pp. 209 - 217
• Main Authors: Kang, Thomas H.-K., Mitra, Nilanjan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2012; Elsevier
• Subjects: Anchorage; Applied sciences; Beam-column connection; Beam-columns; Binomial logistic regression model; Building structure; Buildings; Buildings. Public works; Clear bar spacing; Computation methods. Tables. Charts; Construction (buildings and works); Design parameters; Development; Exact sciences and technology; External envelopes; Headed bar; Joints; Mathematical models; Methodology; Regression; Reinforced concrete; Reinforced concrete structure; Statistical analysis; Statistical methodology; Structural analysis. Stresses; Headed bar; Clear bar spacing; Statistical methodology; Binomial logistic regression model; Beam-column connection; Development; Anchorage; Reinforced concrete; Performance evaluation; Validation; Data analysis; Connection; Spacing; Forecast model; Statistical method; Logistic regression; Concrete construction; Beam column structure; Reinforcing bar
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2012.03.036

► Database of connections with headed bars was empirically and statistically assessed. ► Binomial logistic regression methodology has been developed. ► The effect of each design parameter in determining the performance was quantified. ► A robust goodness-of-fit test and experimental data were used for verification. ► The model is capable to reasonably predict the connection performance. Given that both ACI 318-08 provisions and 352R-02 recommendations have been developed based on quite limited experimental data, an extensive database was assembled by Kang et al. [12], which contains most of the available test data of reinforced concrete exterior beam-column connections with headed bars subject to load reversal. In this study, the database has been further expanded by adding the recent data focusing on the investigation of design parameters of clear bar spacing and head size, and re-evaluated using a variety of statistical and empirical techniques. An effort has been made to find a statistical model linking quantitative design parameters and qualitative connection response. In this study, binomial logistic regression methodology has been applied. The statistical methodology quantifies the effect of each design parameter in determining the performance of the connection. A reliable and robust goodness-of-fit test, the log-likelihood ratio test, was performed to evaluate the developed logistic regression model. Finally, the recent connection data were used to validate the predictive capability of the developed statistical model.