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<p>Fatigue cracking threatens the safety of orthotropic steel decks (OSDs) in long-span bridges, yet current manual inspection lacks predictive depth. We present a closed-loop framework integrating autonomous robotic inspection, vision-based quantification, and finite-element fracture mechanics to enable adaptive fatigue prognosis. In laboratory validation, the robotic platform achieved a mean localization accuracy of 2.7 ± 0.8 cm, meeting structural precision requirements. Field deployment on an in-service cable-stayed bridge demonstrated that automated inspection reduced average time per girder from 124.6 to 50.4 minutes—a 59.6% reduction. Identified cracks were assimilated into a digital twin for adaptive state updating. Analysis of discrepancies between simulated and observed propagation paths—interpreted via stress intensity factor fields—highlighted significant mixed-mode fracture effects, particularly elevated Mode-II (shear) contributions, as a primary source of predictive uncertainty under in-service conditions. This integration of robotics and digital twins provides a scalable solution for automated maintenance. Beyond labor reduction, the framework establishes a data-driven path toward proactive life-cycle management, enhancing the structural resilience and long-term safety of critical transportation infrastructure.</p>

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A closed-loop framework integrating robotic inspection and digital twins for fatigue prognosis of in-service steel bridges

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

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  • Xincheng Li,
  • Zhongqiu Fu,
  • Hongbin Guo,
  • Bohai Ji,
  • Zhaodong Xu

摘要

Fatigue cracking threatens the safety of orthotropic steel decks (OSDs) in long-span bridges, yet current manual inspection lacks predictive depth. We present a closed-loop framework integrating autonomous robotic inspection, vision-based quantification, and finite-element fracture mechanics to enable adaptive fatigue prognosis. In laboratory validation, the robotic platform achieved a mean localization accuracy of 2.7 ± 0.8 cm, meeting structural precision requirements. Field deployment on an in-service cable-stayed bridge demonstrated that automated inspection reduced average time per girder from 124.6 to 50.4 minutes—a 59.6% reduction. Identified cracks were assimilated into a digital twin for adaptive state updating. Analysis of discrepancies between simulated and observed propagation paths—interpreted via stress intensity factor fields—highlighted significant mixed-mode fracture effects, particularly elevated Mode-II (shear) contributions, as a primary source of predictive uncertainty under in-service conditions. This integration of robotics and digital twins provides a scalable solution for automated maintenance. Beyond labor reduction, the framework establishes a data-driven path toward proactive life-cycle management, enhancing the structural resilience and long-term safety of critical transportation infrastructure.