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A novel cepstrum-based and evolutionary optimization hybrid framework for structural parameter identification
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DOI：10.1177/13694332251405899.png)
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Abstract
En 中文
The accuracy and practicality of structural parameter identification remain critical challenges in Structural Health Monitoring (SHM), as many existing modal updating techniques are limited by noise sensitivity, reliance on expert judgment, and complex system identification procedures. This study introduces a novel model updating method that redefines structural parameter identification process by utilizing Cepstral Coefficients (CCs) of structural responses, which provide a compact, robust, and noise-tolerant representation of dynamic characteristics. This study pioneers the use of response-cepstrum representations in structural model updating. Unlike conventional modal characteristics, the CCs are directly and efficiently extracted from time-domain responses through signal-processing techniques, avoiding the expertise-intensive and laborious modal identification process while significantly accelerating the analysis. Exploiting the unique characteristic representation of the CCs, a customized evolutionary optimization framework is developed based on an advanced variant of the Differential Evolution (DE) algorithm for parameter identification. This development is supported by a systematic investigation of multiple effective evolutionary optimization strategies to ensure robust and accurate matching of CCs between measured and simulated responses. The proposed method was validated through both numerical simulations and experimental tests, demonstrating strong performance in identification accuracy, computational efficiency, and robustness to measurement noise as well as parameter interdependence. This method offers an efficient and scalable alternative to modal updating and establishes a new direction for feature-driven, data-centric SHM systems.
Keywords:
structural dynamics
efficient parameter identification
cepstrum-based model updating
advanced signal processing
customized evolutionary optimization
Journal
A
IF:
2.6
Papers:
137
Citations:
0