LAUSR

Design code provisions for reinforced concrete are often based on empirical relations resulting from simple statistical treatments of experimental data. Hence, they may provide inaccurate results for predicting complex structural behavior. In the present study, novel nonlinear regression for prediction of the reinforcing bar development length is developed using dynamical self-adjusted harmony search optimization. The nonlinear mathematical relations are regressed using 534 results of simple pullout tests on short unit bar lengths. A novel bi-nonlinear expression is proposed, and its predictive capability outperformed that of design code formulas such as the ACI 318-14, ACI 408R-03, and Eurocode 2 along with other existing empirical models. A parametric study was conducted to explore the sensitivity of the proposed models to influential input parameters. It was found that the new model offers a powerful predictive tool for reinforcing bar bond strength which differs from that of existing models that assume unrealistic uniform bond stress along the rebar. This flexible and data-intensive model could be further scrutinized for consideration in future design code revisions and enhancements.