Stochastic characterization of wind field characteristics of an arch bridge instrumented with structural health monitoring system

This paper aims to conduct a stochastic characterization of wind field characteristics nearby an arch bridge based on long-term monitoring data from an instrumented structural health monitoring (SHM) system. The fluctuating wind characteristics are first presented by analyzing the real-time wind monitoring data. A genetic algorithm (GA)-based finite mixture modeling approach is proposed to formulate the joint distribution of the wind speed and direction. For the probability density function (PDF) of the wind speed, a two-parameter Weibull distribution is applied, and a von Mises distribution is selected to present the PDF of the wind direction. The parameters of finite mixture models are estimated by the GA-based parameter estimation method. The effectiveness of the proposed direct probabilistic modeling approach is validated by use of one-year of wind monitoring data, and compared with the traditional angular-linear (AL) distribution-based indirect modeling approach in terms of the Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and R² value. Results indicate that the proposed GA-based finite mixture modeling approach fits the measured data better than the AL distribution-based indirect modeling approach. In addition, the joint distribution of the wind speed and direction will facilitate the wind-resistant design and wind-induced fatigue assessment of long-span bridges.

• Record URL:
  • https://doi.org/10.1016/j.strusafe.2017.11.003
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0167473017300723
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01674730
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Ye, X W
  • Xi, P S
  • Su, Y H
  • Chen, Bin
  • Han, J P
• Publication Date: 2018-3

Language

• English

Media Info

• Media Type: Print
• Features: References;
• Pagination: pp 47-56
• Serial:
  • Structural Safety
  • Volume: 71
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0167-4730
  • Serial URL: https://www.sciencedirect.com/journal/structural-safety

Subject/Index Terms

• TRT Terms: Arch bridges; Genetic algorithms; Instrumentation; Long span bridges; Stochastic processes; Structural health monitoring; Wind
• Subject Areas: Bridges and other structures; Environment; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01671826
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 8 2018 5:12PM