A novel semi-empirical supervised model of vortex-induced vertical force on a flat closed-box bridge deck - Xiaoxia Tian; Jingwen Yan (International Journal of Distributed Sensor Networks)
This study presents a novel single-degree-of-freedom model of vortex-induced vertical force, which is based on supervised learning. There are three steps in the process of modeling. First, a hypothesis function based on the Taylor expansion is applied to describe the complicated of vortex-induced vertical force. Second, this hypothesis function is optimized by spectrum and correlation analysis. The terms in this function are deleted when they meet one of the following cases: the frequency amplitudes are close to 0; the correlation coefficients with the vortex-induced vertical force are less than 0.3; the correlation coefficients with other low-order terms are more than 0.8. Third, the validity and reliability of the optimized function are verified by comparative and residual analysis. The process of optimization makes the proposed model simple and well describes the main characteristics of vortex-induced vertical forces. Moreover, the maximum displacement is accurately predicted according to the proposed model. Simulation results show that the proposed model has a high coefficient of determination ( R 2 ) compared with Scanlan’s and Zhu’s models, which means that the proposed model is more suitable to describe vortex-induced vertical forces.