Transformative role of machine learning in design optimization of reinforced concrete frames

Reinforced concrete (RC) framed structures are the most commonly used structure framework worldwide.Thus, design optimization of RC frames [1] is necessary to achieve sustainability by reducing cost and carbon emissions.Optimization assists in creating the most cost-effective design [2] while maintaining structural integrity and safety.Construction expenses can be decreased and cost efficiency [3] can be improved by determining the most appropriate section sizes and reinforcing schemes.Improved structural performance is the result of optimized designs [4], which ensure that the frame can sustain the prescribed loads and retain stability throughout its service life.This can result in a more sturdy and long-lasting construction [5].