Bayesian model updating method based on multi-sampling strategy integration

The Bayesian model updating method is widely applied in structural health monitoring. Traditional Bayesian model updating methods suffer from limitations such as dimensional constraints, slow convergence, and low computational efficiency. To enhance the convergence speed and computational efficiency of Bayesian model updating methods, this paper proposes a multi-sampling strategy integrated Bayesian model updating method based on the Differential Evolution Adaptive Metropolis (DREAM) algorithm. First, the Kalman-inspired distribution and sampling difference vectors from past states are introduced into the DREAM algorithm, addressing the issue of parallel chain limitations in DREAM and improving the exploration efficiency of the posterior distribution. Second, drawing inspiration from clustering algorithms that use centroids for data point clustering, a novel centroid update sampling strategy is proposed and integrated with other sampling strategies to increase sampling diversity among different chains, thereby avoiding local optima and accelerating the convergence process. Finally, the proposed method's effectiveness is validated through numerical examples of simply supported beams and experimental examples of a three-story frame structure. The results demonstrate that the proposed Bayesian model updating method based on multi-sampling strategy integration achieves high updating accuracy and faster convergence. The updated model shows improved ability to simulate the inherent properties and response characteristics of actual structures, making it suitable for use as a benchmark model in structural health monitoring.