Intelligent Shear Wall Optimization in Reinforced Concrete Buildings Subjected to Earthquake Excitations

This study presents a novel framework for the optimization of the distribution and thickness of shear walls in reinforced concrete (RC) buildings. Unlike traditional trial‐and‐error methods that are time‐consuming and often lead to suboptimal designs, this framework leverages advanced optimization algorithms, specifically the Gray Wolf Optimizer (GWO) algorithm, and Artificial Intelligence to automate the design process using the SAP2000 API. The framework integrates both structural and architectural constraints, offering flexibility in the placement of shear walls, whether at the periphery or inside the building. It uniquely incorporates the newly released Algerian Seismic Code (RPA2024) to automate iterative adjustments and ensure compliance with specific regional constraints, including seismic zoning, interstory drift limits, and base shear verification procedures, which are tailored within the framework. The effectiveness of the framework is demonstrated through numerical examples of regular and irregular buildings, showcasing significant improvements in structural performance, substantial weight reduction, and overall cost efficiency, unlike trial‐and‐error methods. Furthermore, the framework's open accessibility allows structural designers and practitioners to widely use this innovative tool to integrate different seismic codes, providing accessible optimized design techniques for more cost‐effective construction projects.