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<p>Pre-engineered steel buildings (PESBs) are increasingly adopted for industrial applications due to their cost efficiency and rapid deployment. However, ensuring sustainability in PESBs requires a balanced evaluation of economic, environmental, and certification-related goals. This study develops a hybrid multi-objective optimization framework that combines the non-dominated sorting genetic algorithm III (NSGA-III) with multi-objective particle swarm optimization (MOPSO) to simultaneously optimize life cycle cost, embodied carbon emissions, green framework compliance scores, and construction time. A case study of an industrial warehouse in Hyderabad, India, is used to demonstrate the framework, incorporating green building standards such as LEED, IGBC, and GRIHA. The optimization explores alternative design configurations involving material selection, insulation thickness, sheeting type, and bracing systems. The resulting Pareto-optimal solutions highlight trade-offs among key performance metrics, enabling informed decision-making for stakeholders. Sensitivity analysis under varied stakeholder preferences further supports targeted design strategies. Comparative evaluation with other optimization techniques confirms the superiority of the proposed hybrid approach in convergence quality and solution diversity. This study offers a practical decision-support tool for sustainable PESB design, aligning industry practices with climate goals and certification requirements.</p>

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Multi-objective optimization-based evaluation of green rating frameworks for pre-engineered steel buildings using hybrid NSGA-III–MOPSO

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Shailendra Kumar Khare,
  • Anjali Gupta,
  • Devendra Vashist

摘要

Pre-engineered steel buildings (PESBs) are increasingly adopted for industrial applications due to their cost efficiency and rapid deployment. However, ensuring sustainability in PESBs requires a balanced evaluation of economic, environmental, and certification-related goals. This study develops a hybrid multi-objective optimization framework that combines the non-dominated sorting genetic algorithm III (NSGA-III) with multi-objective particle swarm optimization (MOPSO) to simultaneously optimize life cycle cost, embodied carbon emissions, green framework compliance scores, and construction time. A case study of an industrial warehouse in Hyderabad, India, is used to demonstrate the framework, incorporating green building standards such as LEED, IGBC, and GRIHA. The optimization explores alternative design configurations involving material selection, insulation thickness, sheeting type, and bracing systems. The resulting Pareto-optimal solutions highlight trade-offs among key performance metrics, enabling informed decision-making for stakeholders. Sensitivity analysis under varied stakeholder preferences further supports targeted design strategies. Comparative evaluation with other optimization techniques confirms the superiority of the proposed hybrid approach in convergence quality and solution diversity. This study offers a practical decision-support tool for sustainable PESB design, aligning industry practices with climate goals and certification requirements.