Abstract
This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.
Highlights
Zhitian Zhang and Lei YanAs one kind of flexible structures, long-span bridges tend to vibrate greatly under wind load
Representative models of a bridge deck with aerodynamic and geometrical similarity are elastically suspended in the wind tunnel, and their behavior in the wind flow can be extrapolated to full scale
The proposed method has new improvements in the following three aspects: (1) the weighting factors are optimized in an iterative procedure; (2) an improved heuristic algorithm termed MABC-Powell is proposed for flutter derivatives identification; and (3) a bootstrap scheme is proposed for parameter uncertainty quantification
Summary
As one kind of flexible structures, long-span bridges tend to vibrate greatly under wind load. The random vibration method is to identify the flutter derivatives of the bridge deck by sectional model wind tunnel test in the gridgenerated turbulence field. It is a critical issue for such a kind of heuristic search algorithm to keep a proper balance between the exploration and exploitation Aiming at these problems, this paper introduces a modified ABC algorithm with Powell’s method (MABC-Powell) to solve the optimization problem, where the standard ABC is enhanced by several modifications [15,16,17,18]. The proposed method has new improvements in the following three aspects: (1) the weighting factors are optimized in an iterative procedure; (2) an improved heuristic algorithm termed MABC-Powell is proposed for flutter derivatives identification; and (3) a bootstrap scheme is proposed for parameter uncertainty quantification. The proposed method is validated with simulated data of a thin plate and experimental data of a bridge deck
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