Soft computing models for assessing bond performance of reinforcing bars in concrete at high temperatures

Abstract The bond between steel and concrete in reinforced structures is a multifaceted intricate phenomenon that plays vital role the design overall performance of such structures. It refers to adhesion mechanical interlock reinforcement bars surrounding matrix. Under elevated temperatures, more complex under higher yet having an accurate estimate important factor design. Therefore, this paper focuses on using data-driven models explore concrete-steel high temperatures Gene Expression Programming (GEP) soft computing model. GEP are developed simulate order understand effect bond. results were compared multi-objective evolutionary polynomial regression analysis (MOGA-EPR) for different input variables. new model would help designers with strength predictions fire. dataset used was obtained from experiments conducted laboratory setting gathered 316-point database investigate at range fibre contents. This study also investigates impact variables equation sensitivity analysis. show able predict accurately. provides useful tool engineers better behaviour temperatures.