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<p>Reinforced concrete bridges play a pivotal role in transportation networks by ensuring continuous traffic flow and regional connectivity. Given the progressive aging of existing infrastructure, accurately assessing structural reliability over time has become essential for informed and cost-effective maintenance planning. This study presents a time-dependent reliability analysis of reinforced concrete bridges, accounting for degradation mechanisms such as creep, shrinkage, and reinforcement corrosion, combined with evolving traffic loads modeled using real Weigh-In-Motion (WIM) data. To address the computational limitations of traditional probabilistic methods such as FORM, SORM, or Monte Carlo simulations, a novel active learning strategy based on hybrid metamodels using Radial Basis Function Neural Networks (RBF-NN) is proposed. This approach efficiently identifies critical failure regions while significantly reducing computation time. The study further introduces a predictive maintenance framework by tracking the reliability index over time, triggering interventions when a critical safety threshold (β = 3) is reached. Corrective actions such as beam jacketing or heavy vehicle restrictions are evaluated for their effectiveness in restoring structural safety and slowing long-term deterioration. The proposed methodology offers a practical and efficient tool to support proactive reliability-based management of aging bridge infrastructure.</p>

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Predictive maintenance based on the time-dependent reliability of RC bridges under material degradation and traffic growth

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Najib Zemed,
  • Hanane Moulay Abdelali,
  • Toufik Cherradi,
  • Azzeddine Bouyahyaoui,
  • Kaoutar Mouzoun

摘要

Reinforced concrete bridges play a pivotal role in transportation networks by ensuring continuous traffic flow and regional connectivity. Given the progressive aging of existing infrastructure, accurately assessing structural reliability over time has become essential for informed and cost-effective maintenance planning. This study presents a time-dependent reliability analysis of reinforced concrete bridges, accounting for degradation mechanisms such as creep, shrinkage, and reinforcement corrosion, combined with evolving traffic loads modeled using real Weigh-In-Motion (WIM) data. To address the computational limitations of traditional probabilistic methods such as FORM, SORM, or Monte Carlo simulations, a novel active learning strategy based on hybrid metamodels using Radial Basis Function Neural Networks (RBF-NN) is proposed. This approach efficiently identifies critical failure regions while significantly reducing computation time. The study further introduces a predictive maintenance framework by tracking the reliability index over time, triggering interventions when a critical safety threshold (β = 3) is reached. Corrective actions such as beam jacketing or heavy vehicle restrictions are evaluated for their effectiveness in restoring structural safety and slowing long-term deterioration. The proposed methodology offers a practical and efficient tool to support proactive reliability-based management of aging bridge infrastructure.