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 R-2 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. (C) 2017 Elsevier Ltd. All rights reserved.