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<p>Structural optimization has attracted considerable attention worldwide due to its potential economic and environmental benefits. Despite substantial advances in optimal structural design, significant challenges persist, particularly in the optimization of three-dimensional (3D) reinforced concrete (RC) building frames. This study aims to minimize material costs in 3D RC building structures. To achieve this objective, an optimization software package, BK-HSH, was developed in Python that employs the Jaya Algorithm (JA) and Genetic Algorithm (GA). The software establishes an automated link between structural analysis and design in ETABS and material cost optimization through the ETABS Open Application Programming Interface (ETABS-OAPI), enabling efficient and reliable structural design optimization. Two case studies, a three-story (small-scale) and a six-story (medium-scale) 3D RC frame, were analyzed to optimize the costs of concrete and steel reinforcement in beams and columns. The results demonstrate that the proposed BK-HSH software effectively reduces material costs in both case studies. For the three-story building, total material cost reductions ranged from 19.9 to 25%, while for the six-story building, reductions varied between 26.6 and 27.3%. The JA achieved faster convergence for the small-scale optimization problem, whereas the GA generally required shorter overall computational time. In addition to economic benefits, the optimized structural designs led to a significant reduction in embodied carbon (EC) emissions compared with the baseline designs.</p>

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Material cost optimization in the structural design of three-dimensional reinforced concrete building frames using evolutionary algorithms

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Hao Dinh Phan,
  • Sang Van Phan

摘要

Structural optimization has attracted considerable attention worldwide due to its potential economic and environmental benefits. Despite substantial advances in optimal structural design, significant challenges persist, particularly in the optimization of three-dimensional (3D) reinforced concrete (RC) building frames. This study aims to minimize material costs in 3D RC building structures. To achieve this objective, an optimization software package, BK-HSH, was developed in Python that employs the Jaya Algorithm (JA) and Genetic Algorithm (GA). The software establishes an automated link between structural analysis and design in ETABS and material cost optimization through the ETABS Open Application Programming Interface (ETABS-OAPI), enabling efficient and reliable structural design optimization. Two case studies, a three-story (small-scale) and a six-story (medium-scale) 3D RC frame, were analyzed to optimize the costs of concrete and steel reinforcement in beams and columns. The results demonstrate that the proposed BK-HSH software effectively reduces material costs in both case studies. For the three-story building, total material cost reductions ranged from 19.9 to 25%, while for the six-story building, reductions varied between 26.6 and 27.3%. The JA achieved faster convergence for the small-scale optimization problem, whereas the GA generally required shorter overall computational time. In addition to economic benefits, the optimized structural designs led to a significant reduction in embodied carbon (EC) emissions compared with the baseline designs.