Dynamic Bayesian identification of mechanical parameters of multi-cell curve box girder based on conjugate gradient theory

• Published in: Science China. Technological sciences Vol. 55; no. 4; pp. 1057 - 1065
• Main Authors: Zhang, Jian, Zhou, ChuWei, Lin, Jing
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science China Press 01.04.2012
• Subjects: Bayesian analysis; Box girders; Conjugate gradients; Dynamic tests; Dynamics; Engineering; Finite strips; Mathematical analysis; Optimization; 优化计算; 公式识别; 共轭梯度法; 动态力学参数; 多细胞; 曲线箱梁; 梯度理论; 贝叶斯理论; multi-cell curve box girder; conjugate gradient theory; mechanical parameters; Bayesian identification
• ISSN: 1674-7321; 1869-1900
• DOI: 10.1007/s11431-011-4741-1

For multi-cell curve box girder, the finite strip governing equation was derived on the basis of Novozhilov theory and orthogonal property of harmonious function series. Dynamic Bayesian error function of mechanical parameters of multi-cell curve box girder was achieved with Bayesian statistical theory. The corresponding formulas of dynamic Bayesian expectation and variance were obtained. After the one-dimensional optimization search method for the automatic determination of step length of the mechanical parameter was put forward, the optimization identification calculative formulas were also obtained by adopting conjugate gradient method. Then the steps of dynamic Bayesian identification of mechanical parameters of multi-cell curve box girder were stated in detail. Through analysis of a classic example, the dynamic Bayesian identification processes of mechanical parameters are steadily convergent to the true values, which proves that dynamic Bayesian theory and conjugate gradient theory are suitable for the identification calculation and the compiled procedure is correct. It is of significance that the foreknown information of mechanical parameters should be set with reliable practical engineering experiences instead of arbitrary selection.