This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well.

This study introduces human-swarm interaction (HSI) strategies to enhance bio-inspired swarm intelligence (SI) algorithms, addressing inherent limitations of the traditional monkey algorithm (MA) such as premature convergence and computational inefficiency in complex search spaces. We propose three HSI integration strategies involving intermittent, persistent, and parameter-setting interactions within the HSI to augment emergent behaviors and refine the MA’s intrinsic optimization mechanisms. Validation through seven benchmark functions (one unimodal and six multimodal) across seven dimensions demonstrates the HSI-MA’s ability to resolve complex, multidimensional optimization problems with statistically significant (p < 0.05) superior accuracy and stability compared to the original MA and four baseline SI algorithms, achieving 85% dominance in test cases while reducing iterations by an order of magnitude. Further evaluation on five engineering design problems reveals the HSI-MA outperforms 36 state-of-the-art optimizers in 70% of scenarios, confirming its enhanced precision and efficiency in practical applications. In contrast to conventional fusion-based approaches, the HSI framework preserves the original algorithm’s theoretical foundations while systematically integrating human intelligence to enhance structural adaptability and operational efficiency.