• Smart Structures and Systems
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• v.26 no.5
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• pp.545-558
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• 2020

A design method of a Multiple Tuned Mass Damper (MTMD) system is presented for wind induced vibration control of a cable-supported roof structure. Modal contribution analysis is carried out to determine the dominating modes of the structure for the MTMD design. Two MTMD systems are developed for two most dominating modes. Each MTMD system is composed of multiple TMDs with small masses spread at multiple locations with large responses in the corresponding mode. Frequencies of TMDs are distributed uniformly within a range around the dominating frequencies of the roof structure to enhance the robustness of the MTMD system against uncertainties of structural frequencies. Parameter optimizations are carried out by minimizing objective functions regarding the structural responses, TMD strokes, robustness and mass cost. Two optimization approaches are used: Single Objective Approach (SOA) using Sequential Quadratic Programming (SQP) with multi-start method and Multi-Objective Approach (MOA) using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The computation efficiency of the MOA is found to be superior to the SOA with consistent optimization results. A Pareto optimal front is obtained regarding the control performance and the total weight of the TMDs, from which several specific design options are proposed. The final design may be selected based on the Pareto optimal front and other engineering factors.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.