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<p>This study presents an early experimental investigation into drive-by bridge monitoring using a Connected Vehicle (CV), a mobile robot, and a portable Structural Health Monitoring (SHM) system for validation. A frequency-domain filtering method is utilized to isolate the bridge’s dynamic response from composite data collected to identify the modal frequencies under operational traffic conditions. The experiments were conducted on a real-world short, multi-span bridge, often underrepresented in such studies due to its stiff dynamic characteristics. Up to five modal frequencies were identified using CV-based monitoring with an average variation of 3.96% from the SHM reference dataset, while the robot-based method identified one frequency with only 0.35% variation, demonstrating high consistency. Then, a comparative evaluation of these three monitoring platforms is conducted based on their reliability, effectiveness, and practicality. It is discussed that while portable SHM provides the most accurate data, it lacks scalability. On the other hand, CV-based monitoring offers the most practical and scalable solution, and the robot-based method balances effectiveness and consistency with moderate practicality in field deployment. This paper provides early experimental insights into the feasibility of CV and robot platforms for scalable and practical bridge monitoring and highlights directions for future research in drive-by uncertainty quantification, enhanced computational methodologies for isolating bridge response, and multi-modal condition assessment.</p>

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Pilot investigation of bridge dynamics monitoring approaches: connected vehicle, robot, and portable SHM

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Furkan Luleci,
  • Abdulrrahman Algadi,
  • F. Necati Catbas

摘要

This study presents an early experimental investigation into drive-by bridge monitoring using a Connected Vehicle (CV), a mobile robot, and a portable Structural Health Monitoring (SHM) system for validation. A frequency-domain filtering method is utilized to isolate the bridge’s dynamic response from composite data collected to identify the modal frequencies under operational traffic conditions. The experiments were conducted on a real-world short, multi-span bridge, often underrepresented in such studies due to its stiff dynamic characteristics. Up to five modal frequencies were identified using CV-based monitoring with an average variation of 3.96% from the SHM reference dataset, while the robot-based method identified one frequency with only 0.35% variation, demonstrating high consistency. Then, a comparative evaluation of these three monitoring platforms is conducted based on their reliability, effectiveness, and practicality. It is discussed that while portable SHM provides the most accurate data, it lacks scalability. On the other hand, CV-based monitoring offers the most practical and scalable solution, and the robot-based method balances effectiveness and consistency with moderate practicality in field deployment. This paper provides early experimental insights into the feasibility of CV and robot platforms for scalable and practical bridge monitoring and highlights directions for future research in drive-by uncertainty quantification, enhanced computational methodologies for isolating bridge response, and multi-modal condition assessment.