TY  - JOUR
PY  - 2018//
TI  - Stochastic characterization of wind field characteristics of an arch bridge instrumented with structural health monitoring system
JO  - Structural safety
A1  - Ye, X. W.
A1  - Xi, P. S.
A1  - Su, Y. H.
A1  - Chen, B.
A1  - Han, J. P.
SP  - 47
EP  - 56
VL  - 71
IS  - 
N2  - 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. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 0167-4730
UR  - http://dx.doi.org/10.1016/j.strusafe.2017.11.003
ID  - ref1
ER  -