With advancements in processors and the development of numerical methods, precise, cost-effective structural design and the simulation of complex systems have become feasible. In the realm f reinforced concrete structures, due to the more complex behavior of sections and structures compared to steel structures and the longer history of research in steel structures, relatively fewer studies have focused on optimizing this type of structure. This gap highlights the need for further investigation to better understand the behavior of reinforced concrete structures and to develop improved optimization methods. In this research, tools such as Python, Matlab, PyMOO, and OpenSees were utilized to model, analyze, and design reinforced concrete structures with shear walls. A non-revisiting genetic algorithm was applied for optimization. To achieve this, modeling at different scales (materials, elements, and structures) was performed, and the results were validated by comparing them with previous numerical and experimental models. Proposed problems, as well as benchmark problems in structural optimization, were examined. In the next step, by optimizing each component (beams, columns, and shear walls), a list of optimized sections was prepared for use in structural optimization. These optimized sections were then used as design variables for optimizing the reinforced concrete structure. After demonstrating the feasibility and effectiveness of the proposed two-stage approach, several practical examples under different optimization conditions were solved to further eva‎luate its performance. In addition to optimizing the three-dimensional frame, the placement of shear walls was optimized based on architectural layouts. Finally, the proposed two-stage method was applied to multi-objective optimization, considering cost and water consumption as secondary objective functions. The results indicated that the proposed two-stage method not only accelerated the optimization process but also significantly improved the final outcome. This method proved effective not only for single-objective optimization but also for multi-objective decision-making problems. The two-stage approach reduced the search space and enhanced optimization efficiency, decreasing total structural costs by up to 34% and computational costs by 51%. Additionally, it improved the stability of the optimization process by 100% and reduced water consumption by 5.4%, mitigating environmental impacts. This study demonstrates that the proposed algorithm can find more optimal solutions compared to conventional methods and enables optimized shear wall placement and more sustainable designs. Ultimately, the presented program is capable of optimizing a reinforced concrete structure based on existing codes with a focus on optimal design at a realistic scale. Beyond optimizing reinforced concrete structures and shear wall placement, its adaptability makes it suitable for future research applications.

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پيام اسدي , نيما نورمحمدي , طاها بخشپوري