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<p>Pre-engineered steel buildings (PESBs) have emerged as a fast and cost-effective construction solution for industrial infrastructure; however, their sustainability performance remains inadequately addressed due to high embodied carbon and limited integration with green rating systems. This study proposes a hybrid multi-objective optimization framework combining Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Particle Swarm Optimization (MOPSO) to evaluate and optimize PESB designs across economic, environmental, and regulatory dimensions. Four conflicting objectives—life cycle cost (LCC), embodied carbon emissions (ECE), green framework compliance score (GCS), and construction time (CT)—are simultaneously optimized. A real-world industrial warehouse case study in Hyderabad, India is analyzed under LEED, IGBC, and GRIHA rating frameworks. The hybrid algorithm leverages the diversity preservation capability of NSGA-III and the fast convergence of MOPSO to generate well-distributed Pareto-optimal solutions. Results demonstrate significant trade-offs among objectives, with high-compliance designs incurring higher costs and moderate carbon reductions, while cost-optimal solutions exhibit lower sustainability performance. The proposed framework outperforms conventional optimization techniques in terms of convergence, diversity, and solution quality. Scenario-based decision analysis further highlights the adaptability of the model to stakeholder-specific priorities. The study provides a robust decision-support tool for sustainable PESB design and contributes to advancing optimization-driven green building assessment in industrial construction.</p>

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An integrated optimization framework for sustainable pre-engineered steel buildings: coupling NSGA-III–MOPSO with green building rating systems

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Esar Ahmad,
  • P. D. Maneeth,
  • Shashank Gupta,
  • Shyamveer Singh Chauhan,
  • Nageswara Rao Lakkimsetty,
  • G. Pavithra

摘要

Pre-engineered steel buildings (PESBs) have emerged as a fast and cost-effective construction solution for industrial infrastructure; however, their sustainability performance remains inadequately addressed due to high embodied carbon and limited integration with green rating systems. This study proposes a hybrid multi-objective optimization framework combining Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Particle Swarm Optimization (MOPSO) to evaluate and optimize PESB designs across economic, environmental, and regulatory dimensions. Four conflicting objectives—life cycle cost (LCC), embodied carbon emissions (ECE), green framework compliance score (GCS), and construction time (CT)—are simultaneously optimized. A real-world industrial warehouse case study in Hyderabad, India is analyzed under LEED, IGBC, and GRIHA rating frameworks. The hybrid algorithm leverages the diversity preservation capability of NSGA-III and the fast convergence of MOPSO to generate well-distributed Pareto-optimal solutions. Results demonstrate significant trade-offs among objectives, with high-compliance designs incurring higher costs and moderate carbon reductions, while cost-optimal solutions exhibit lower sustainability performance. The proposed framework outperforms conventional optimization techniques in terms of convergence, diversity, and solution quality. Scenario-based decision analysis further highlights the adaptability of the model to stakeholder-specific priorities. The study provides a robust decision-support tool for sustainable PESB design and contributes to advancing optimization-driven green building assessment in industrial construction.