基于敏感性分析的降参模型更新混合模拟方法

Since sensitivity analyses have the problem of low efficiency or even inability to analyze when dealing with a large number of identification parameters,a hybrid simulation method for reduced-parameter models updating based on sensitivity analysis is proposed. It incorporates categorical sensitivity analysis and staged model updating to notably reduce the number of parameters analyzed and updated,enhancing parameter identification efficiency. Taking the steel frame model as an example,8 model parameters are selected for analysis. The impact of the hybrid simulation updating in the reduced-parameter model with different sensitivity shares as the core parameter selection criteria is discussed,employing two parameter reduction strategies,respectively. The numerical simulation results indicate that the discrepancy between errors under two reduction strategies is less than 0. 5% . Categorical and staged reduction strategies significantly decrease the computational burden of parameter identification and the number of convergence steps in comparison to full parametric strategy. Thus, the feasibility and effectiveness of the proposed method in enhancing the speed of hybrid simulation parameter identification are confirmed.