Structural Damage Detection Using Modal Parameters and Particle Swarm Optimization

Gökdağ, Hakan; Yıldız, Ali Rıza

doi:10.3139/120.110346

Cited by 69 publications

(15 citation statements)

References 20 publications

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“…7) are held by Neural Networks [384]- [391], Evolutionary Computing [392]- [395] and Genetic Algorithms [396]- [403]. Fuzzy logic is significantly represented in the field [404]- [410] while Swarm Intelligence [411]- [417], Particle Swarm Optimization [418]- [423], and artificial immune systems [424]- [427] have not been extensively explored yet. …”

Section: F Applications Of Computational Intelligence To Materialsmentioning

confidence: 99%

Applications of computational intelligence to mechanical engineering

Belfiore

Rudas

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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Most of the methods and tools of Computational Intelligence (CI), such as Artificial Neural Networks, Evolutionary Computation, Fuzzy logic, Computational Swarm Intelligence and Artificial Immune Systems, have been developed for decades. However, their applications are not disseminated as exhaustively as they could be, for many reasons. In this work, a survey on some applications of Computational Intelligence to Mechanical Engineering will be presented. This review has been based on statistical analysis of a large amount of data extracted from major bibliographic databases. This analysis provides a quantitative map of the variety of CI methods and tools and their applications to some fields of Mechanical Engineering. The results will suggest to researchers some important topics of Mechanical Engineering where CI has great potential and may improve its impact

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Section: F Applications Of Computational Intelligence To Materialsmentioning

confidence: 99%

Applications of computational intelligence to mechanical engineering

Belfiore

Rudas

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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“…Many researchers have suggested using efficient sampling methods to alleviate constraints on computational cost. Residual minimization has been implemented with optimization algorithms such as genetic algorithms [45], artificial bee colony optimization [46,47], particle swarm optimization [48] and ant colony optimization [49] to reduce computational cost. BMU has been implemented using Markov-Chain Monte Carlo (MCMC) sampling [50], transitional MCMC sampling [51], and evolutionary MCMC sampling [52].…”

Section: Introductionmentioning

confidence: 99%

Data-Interpretation Methodologies for Practical Asset-Management

Pai

Reuland

Smith

2019

JSAN

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Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding uncertainty conditions. While much research has been conducted on the scientific development of these methodologies and some research has evaluated the applicability of underlying assumptions, little research is available on the suitability of these methodologies to satisfy practical engineering challenges. For use in practice, data-interpretation methodologies need to be able, for example, to respond to changes in a transparent manner and provide accurate model updating at minimal additional cost. This facilitates incremental and iterative increases in understanding of structural behavior as more information becomes available. In this paper, three data-interpretation methodologies, Bayesian model updating, residual minimization and error-domain model falsification, are compared based on their ability to provide robust, accurate, engineer-friendly and computationally inexpensive model updating. Comparisons are made using two full-scale case studies for which multiple scenarios are considered, including incremental acquisition of information through measurements. Evaluation of these scenarios suggests that, compared with other data-interpretation methodologies, error-domain model falsification is able to incorporate, iteratively and transparently, incremental information gain to provide accurate model updating at low additional computational cost.

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“…Qian et al [31] implemented a hybrid PSO simplex method for delamination detection in laminated beams using a delamination parameter-based objective function with robustness and efficient performance. Kang et al [32] as well as Gökdağ and Yildiz [33] proposed two different PSO versions to track damage with successful performance. Zhu et al [34] developed a bird mating optimizer (BMO) in the time-frequency domain for damage detection in 2D structures.…”

Section: Introductionmentioning

confidence: 99%

Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

2018

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Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

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Structural Damage Detection Using Modal Parameters and Particle Swarm Optimization

Cited by 69 publications

References 20 publications

Applications of computational intelligence to mechanical engineering

Applications of computational intelligence to mechanical engineering

Data-Interpretation Methodologies for Practical Asset-Management

Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

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