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<p>Reinforced Concrete (RC) Structure Designs use semi-probabilistic methods that apply safety factors to compensate for uncertainties, increasing loads, and reducing resistances. While this ensures safety and reliability, probabilistic methods offer a more comprehensive approach by quantifying the probability of failure for a structure. This main contribution includes identifying efficient methods for Optimization under Uncertainty, implementing two optimization frameworks (Reliability-Based Design Optimization and Reliability-Based Robust Multiobjective Design Optimization), validating algorithms through benchmark problems, and applying them to real-world problems involving a RC plane frame subjected to permanent and accidental loads. Four algorithms were tested: Reliability Index Approach (RIA), Performance Measure Approach (PMA), Single-Loop Approach (SLA), and Sequential Optimization and Reliability Assessment (SORA). Weighted Sum (WS) and Normal-Boundary Intersection (NBI) were used to construct the Pareto frontier. The study also analyzed efficiency by considering accuracy, processing time, failure function evaluations, and Evness. SLA is the most efficient for RBDO, while SORA is the most efficient for RBRMDO. RIA and PMA underperformed in processing time and limit state function evaluations. NBI outperformed WS in the Pareto point distribution quality.</p>

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Computational efficiency analysis of reliability-based robust multiobjective design optimization applied to a reinforced concrete (RC) plane frame

  • 期刊论文

  • 发表日期：

    2025年4月5日

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  • Jonathan J. A. Cassimiro,
  • Jacqueline C. M. do Nascimento,
  • Renato de S. Motta,
  • Silvana M. B. Afonso da Silva

摘要

Reinforced Concrete (RC) Structure Designs use semi-probabilistic methods that apply safety factors to compensate for uncertainties, increasing loads, and reducing resistances. While this ensures safety and reliability, probabilistic methods offer a more comprehensive approach by quantifying the probability of failure for a structure. This main contribution includes identifying efficient methods for Optimization under Uncertainty, implementing two optimization frameworks (Reliability-Based Design Optimization and Reliability-Based Robust Multiobjective Design Optimization), validating algorithms through benchmark problems, and applying them to real-world problems involving a RC plane frame subjected to permanent and accidental loads. Four algorithms were tested: Reliability Index Approach (RIA), Performance Measure Approach (PMA), Single-Loop Approach (SLA), and Sequential Optimization and Reliability Assessment (SORA). Weighted Sum (WS) and Normal-Boundary Intersection (NBI) were used to construct the Pareto frontier. The study also analyzed efficiency by considering accuracy, processing time, failure function evaluations, and Evness. SLA is the most efficient for RBDO, while SORA is the most efficient for RBRMDO. RIA and PMA underperformed in processing time and limit state function evaluations. NBI outperformed WS in the Pareto point distribution quality.