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Digital twin-based multi-objective parameter identification for a cable-stayed bridge
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DOI：10.1016/j.istruc.2026.111723.png)
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Abstract
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Accurate parameter identification is essential for ensuring the structural safety and operational performance of long-span bridges. This paper presents a digital twin-based multi-objective optimization framework for identifying structural parameters of a cable-stayed bridge by matching its frequencies, mode shapes, and deflections. The framework integrates three techniques: (1) adaptive reference point relocation to maintain solution diversity across irregular Pareto fronts; (2) sensitivity-informed variation operators that concentrate exploration on parameters with significant influence; and (3) hybrid local search incorporating quasi-Newton refinement for accelerated convergence. The framework was evaluated through implementation on a case study of a 580-meterlong cable-stayed bridge. Experimental results demonstrate up to a 52% reduction in parameter identification error and 25-35% reductions in total runtime compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and standard NSGA-III. Robustness analysis confirms the framework's reliability with errors consistently below 3%. This research contributes to advancing bridge condition assessment capabilities, thereby enhancing structural monitoring and optimizing maintenance strategies for critical infrastructure.
Keywords:
Cable-stayed bridge
Structural parameter identification
Sensitivity analysis
Multi-objective optimization
Bridge condition assessment
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4.3
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1.3W
Citations:
2.7W
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