Bridge Deck Modal Parameters Identification Using Traffic Loads

Articles in Press

Document Type : Regular Paper

Authors

1 Ph.D. Candidate, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

2 Postdoctoral Fellow, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

3 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Structural Health Monitoring (SHM) has gained significant importance in recent decades, with various methods developed to detect structural damage. Many non-destructive damage detection techniques are based on vibration response analysis, where changes in modal parameters provide insights into the condition of the structure. For long-term monitoring, utilizing operational loads as the source of vibration is more practical. This paper presents a methodology that processes the forced vibration response of a bridge deck subjected to traffic loading for modal parameter identification. Specifically, the free vibration response is estimated using the Random Decrement (RD) technique combined with Empirical Mode Decomposition (EMD). The natural frequencies and mode shapes are extracted using Frequency Domain Decomposition (FDD). To validate the proposed approach, numerical models of 2D and 3D bridge decks are employed, considering various loading scenarios and the effects of load path and speed. The results indicate that the proposed method is effective for modal identification under real traffic loads, with improved accuracy observed when more complex load patterns, closer to actual conditions, are used. Additionally, the proximity of degrees of freedom to the load path enhances the precision of the results. Quantitative comparisons of modal frequencies and mode shapes validate the robustness of the methodology.

Keywords

Main Subjects

Journal of Rehabilitation in Civil Engineering

Articles in Press, Accepted Manuscript
Available Online from 14 January 2025

Files

History

  • Receive Date: 06 August 2024
  • Revise Date: 15 December 2024
  • Accept Date: 06 January 2025

Share

How to cite

Statistics