结构模型更新的自适应参数反演方法及时空响应计算OA

Structural model updating leverages measured data for parameter inversion,improving computational accuracy and enabling spatiotemporal response analysis.Although Bayesian methods are widely adopted,many existing algorithms depend on numerous empirical parameters,leading to limited efficiency.To overcome this drawback,an adaptive Bayesian model updating approach was developed within the transitional Markov chain Monte Carlo(TMCMC)framework.The method normalized the likelihood function to establish a unified iterative procedure and dynamically updates the optimal TMCMC coefficients using iterative sampling information,thereby significantly enhancing inversion efficiency.Coupled with time-dependent performance evolution functions,the updated model facilitated full life-cycle spatiotemporal response analysis.The proposed approach was validated through comparisons with conventional methods,and a case study was then conducted on a representative nuclear containment structure for parameter identification,response prediction,and probabilistic performance assessment.The results indicate consistently high inversion accuracy across operating conditions and reveal an increased failure risk for containment structures with service lives exceeding 40 years,underscoring the need for periodic inspection.