A novel evolutionary algorithm for structural model updating with a hybrid initialization and multi-stage update strategy

Abstract

Measurement errors, spatial incompleteness, and parameter uncertainties frequently lead to significant deviations between the estimated correction coefficients of structural model updating and their actual values. To address the limitations, a novel multi-objective evolutionary algorithm called MOEA-HiMs is proposed for structural model updating, which incorporates a hybrid initialization and multi-stage update strategy. Two main contributions are summarized: (a) the introduction of optimal point and elite set initialization to enhance population diversity, which is achieved through the use of Latin hypercube sampling and a regularization technique, respectively; (b) the division of the optimization process into multiple stages, where solutions are corrected to improve converging efficiency using an appropriate update strategy. Additionally, the elite set is selected through the execution of sensitivity classification using a novel mathematical sensitivity index. The effectiveness and robustness of the MOEA-HiMs are demonstrated by the numerical offshore jacket platform and an experimentally-scaled platform structure. Both numerical and experimental results prove that the novel proposed MOEA-HiMs is more effective and robust than the traditional MOEA when using limited information, even under heavy noise conditions. The algorithm can serve as preliminary methods for online structural model updating of offshore platforms.