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<p>Emerging trends in sustainable infrastructure highlight the utility of multi-objective optimization frameworks for minimizing material usage, economic expenditures, and ecological impacts during structural design. In pedestrian bridge applications, serviceability criteria must be met in addition to conventional safety verifications because dynamic loading from pedestrian activity causes resonant vibrational modes. The pedestrian bridge optimization model, which aims to simultaneously minimize (1) life-cycle economic costs, (2) embodied carbon dioxide (CO<sub>2</sub>) emissions throughout the material production and construction phases, and (3) vertical acceleration responses under human-induced dynamic loading is an established benchmark problem in the literature. In this study, in order to generate a Pareto Front to identify non-dominated solutions, a recently developed hybrid of Multi-objective Ant Lion Optimizer (MOALO) with Genetic Algorithm (MOALG) has been utilized. To assess the effectiveness of the MOALG algorithm on the pedestrian bridge optimization model, a set of performance-based metrics was utilized, and its results were compared against those of four established multi-objective optimization algorithms.</p>

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Sustainable pedestrian bridge optimization using a multi-objective optimization approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

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  • Rashmi Sharma,
  • Vishal Sharma,
  • Ashok Pal,
  • Nitin Mittal,
  • Mohd Shukri Ab Yajid,
  • Fikreselam Gared

摘要

Emerging trends in sustainable infrastructure highlight the utility of multi-objective optimization frameworks for minimizing material usage, economic expenditures, and ecological impacts during structural design. In pedestrian bridge applications, serviceability criteria must be met in addition to conventional safety verifications because dynamic loading from pedestrian activity causes resonant vibrational modes. The pedestrian bridge optimization model, which aims to simultaneously minimize (1) life-cycle economic costs, (2) embodied carbon dioxide (CO2) emissions throughout the material production and construction phases, and (3) vertical acceleration responses under human-induced dynamic loading is an established benchmark problem in the literature. In this study, in order to generate a Pareto Front to identify non-dominated solutions, a recently developed hybrid of Multi-objective Ant Lion Optimizer (MOALO) with Genetic Algorithm (MOALG) has been utilized. To assess the effectiveness of the MOALG algorithm on the pedestrian bridge optimization model, a set of performance-based metrics was utilized, and its results were compared against those of four established multi-objective optimization algorithms.