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<p>Ensuring seismic resilience in reinforced concrete (RC) structures while maintaining construction efficiency and cost-effectiveness is a key challenge in structural engineering. This study introduces a Genetic Algorithm (GA)-based optimization framework to enhance both seismic performance and construction feasibility of RC buildings. The framework incorporates updated Algerian seismic regulations (RPA 2024)&#xa0;and current economic constraints. A MATLAB-SAP2000 system was developed, with MATLAB managing the optimization process and SAP2000 performing structural analysis. This integrated approach was applied to RC buildings of varying complexities (five, eight, and ten-story structures), optimizing beam and column dimensions as well as reinforcement areas, all while respecting practical construction constraints and architectural demands. Results show that the GA-based method significantly reduces construction costs and material usage without compromising seismic safety. Controlled displacements and drift ratios confirm the method’s effectiveness, demonstrating that Genetic Algorithms offer a practical and efficient tool for designing resilient, cost-effective, and constructible RC structures in seismic regions.</p>

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Enhancing seismic performance of reinforced concrete structures using genetic algorithm-based optimization

  • 期刊论文

  • 发表日期：

    2025年8月16日

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  • Saad Loukriz,
  • Mohammed Mekki,
  • Meriem Zoutat,
  • Hamane Mebrouk

摘要

Ensuring seismic resilience in reinforced concrete (RC) structures while maintaining construction efficiency and cost-effectiveness is a key challenge in structural engineering. This study introduces a Genetic Algorithm (GA)-based optimization framework to enhance both seismic performance and construction feasibility of RC buildings. The framework incorporates updated Algerian seismic regulations (RPA 2024) and current economic constraints. A MATLAB-SAP2000 system was developed, with MATLAB managing the optimization process and SAP2000 performing structural analysis. This integrated approach was applied to RC buildings of varying complexities (five, eight, and ten-story structures), optimizing beam and column dimensions as well as reinforcement areas, all while respecting practical construction constraints and architectural demands. Results show that the GA-based method significantly reduces construction costs and material usage without compromising seismic safety. Controlled displacements and drift ratios confirm the method’s effectiveness, demonstrating that Genetic Algorithms offer a practical and efficient tool for designing resilient, cost-effective, and constructible RC structures in seismic regions.