Open Access

ARTICLE

A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function

Xiansong Xie^1,*, Xiaoqian Qian²

1 School of Architectural Engineering, Quzhou University, Quzhou, 324000, China
2 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China

* Corresponding Author: Xiansong Xie. Email:

(This article belongs to the Special Issue: Sustainable and Resilient Civil Infrastructure with Intelligence and Digital Transformation)

Structural Durability & Health Monitoring 2026, 20(2), 12 https://doi.org/10.32604/sdhm.2025.074148

Received 03 October 2025; Accepted 13 November 2025; Issue published 31 March 2026

Abstract

Correlation function of acceleration responses-based damage identification methods has been developed and employed, while they still face the difficulty in identifying local or minor structural damages. To deal with this issue, a robust structural damage identification method is developed, integrating a modified holistic swarm optimization (MHSO) algorithm with a hybrid objective function. The MHSO is developed by combining Hammersley sequence-based population initialization, chaotic search around the worst solution, and Hooke-Jeeves pattern search around the best solution, thereby improving both global exploration and local exploitation capabilities. A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions, effectively leveraging the complementary sensitivities of global and local responses. To further suppress spurious solutions and promote sparsity in parameter estimation, an additional L_0.5 regularization term is introduced. The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure. Comparative studies with sequential quadratic programming, genetic algorithm, and HSO demonstrate that the MHSO achieves superior accuracy and convergence efficiency, even with limited sensors and 20% noise-contaminated measurements. Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages, while the inclusion of sparse regularization improves robustness against noise and model uncertainties. The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages, offering promising applicability to real-world structural health monitoring scenarios.

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Keywords

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