A Novel Bayesian Empowered Piecewise Multi-Objective Sparse Evolution for Structural Condition Assessment

In this study, a novel Bayesian empowered piecewise multi-objective function is developed, in which a traditional objective function is applied to realize the rough optimization in the first stage to determine the approximate results. Then, a sparse Bayesian learning-based objective function is applied to realize refined optimization with the obtained approximate results in the second stage. On the other hand, considering the sparsity of the structural damage identification, two simple but effective calculation frameworks, the colony initial sparsification and elite clustering framework, are integrated into the evolution, making the algorithm adaptable to handle the defined sparse optimization problem. Therefore, the proposed calculation framework is more efficient and robust while no initial conditions are needed. We will carry out a numerical example on a truss and an experimental validation on a fixed-end beam with a single-sensor measurement system to verify the method.