Genetic Algorithm-based tension identification of hanger by solving inverse eigenvalue problem

Xie, Xiang; Li, Xiaozhang

doi:10.1080/17415977.2013.848432

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…The state-of-art on use of computational intelligence techniques also includes successful applications pertaining to system identification and model updating of critical bridge elements, such as cables and hangers. For instance, Xie and Li ( 2014 ) address the identification of the tension force of bridge hangers. Specifically, a FE model of the hanger is developed, whereas its parameters (tension force, bending stiffness and boundary conditions) are identified simultaneously through a GA, which looks for the best match between numerical predictions of the hanger frequencies and the corresponding experimental values.…”

Section: Computational Intelligence In Shmmentioning

confidence: 99%

“…Specifically, a FE model of the hanger is developed, whereas its parameters (tension force, bending stiffness and boundary conditions) are identified simultaneously through a GA, which looks for the best match between numerical predictions of the hanger frequencies and the corresponding experimental values. The approach developed by Xie and Li (2014) is applied to hangers without and with dampers, by using laboratory test data as well as experimental data from field tests conducted on one hanger of a tied-arch bridge. The PSO is employed in (Dan et al 2018) and (Xu et al 2019a) for bridge cable systems identification.…”

Section: Computational Intelligence For Identification and Model Upda...mentioning

confidence: 99%

See 1 more Smart Citation

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Karakostas,

Quaranta,

Chatzi

et al. 2023

Bull Earthquake Eng

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The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed.

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Section: Computational Intelligence In Shmmentioning

confidence: 99%

Section: Computational Intelligence For Identification and Model Upda...mentioning

confidence: 99%

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Karakostas,

Quaranta,

Chatzi

et al. 2023

Bull Earthquake Eng

View full text Add to dashboard Cite

show abstract

“…Therefore, for complex boundary conditions (elastic complex rotational constraints and fixed constraints at both ends), the bending stiffness, as well as the values of elastic complex rotational stiffness and cable force, should be identified synchronously as unknown parameters. Referring to Li Xiaozhang and Xie Xu et al [19,25] for the limitation of the search space range of the boom, in this paper, the value of the cable force, bending stiffness and elastic rotational stiffness are limited as shown in Eq. ( 8).…”

Section: Explanation Of Mathematical Problems In Cable Force Identifi...mentioning

confidence: 99%

“…The Cuckoo search algorithm combines long and short-distance searches, ensuring population diversity and better traversal performance in large search spaces [11,[25][26]. Its random walk strategy prevents the algorithm from falling into local optima, to some extent overcoming the issue of multiple spurious solutions in cable force identification.…”

Section: The Proposal Of Mutual Fusion Mechanism and Algorithm Optimi...mentioning

confidence: 99%

Accuracy Improvement of Mutual Integration Mechanism Driven Algorithms for Boom Cable Force Recognition

Zhang,

Yang,

Zhang

et al. 2023

Preprint

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Accurate measurement of cable tension is crucial for real-time monitoring of bridge systems, preventing potential risks, and ensuring bridge safety and continuous operation. However, traditional cable tension measurement often faces the challenge of ensuring accuracy when dealing with complex elastic boundary conditions. This article uses 9 finite element model suspension cables with complex elastic boundary conditions as data for cable force identification, and uses heuristic algorithms to achieve cable force identification with the goal of minimizing the frequency and actual frequency. Based on the recognition results and process, the reasons for inaccurate identification of cable forces under complex elastic boundaries were analyzed, and a mutual fusion mechanism was proposed to improve the accuracy of cable force identification. The results show that the proposed mutual fusion mechanism reduces the maximum relative error of cable tension in accuracy by 12.6%, significantly improving accuracy, and reduces the relative error of most initial cable tension identification to 5%, meeting the needs of practical engineering. In addition, the non parametric test statistical method also proves that the introduction of mutual fusion mechanism has a significant impact on the relative error value of cable tension. Finally, the fusion mechanism was verified through data from three engineering cables to meet engineering requirements. This method provides a new technical solution for intelligent and accurate identification of cable forces in long bridge beams, and has broad application prospects.

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“…Papadimitriou [11] discussed two different categories of model classified by different end plate connections and boundary conditions, using the Bayesian approach to estimate the cable force. Xie [12] used a genetic algorithm to optimize this inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach

Zhan

et al. 2018

Sensors

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The tensile force on the hanger cables of a suspension bridge is an important indicator of the structural health of the bridge. Tensile force estimation methods based on the measured frequency of the hanger cable have been widely used. These methods empirically pre-determinate the corresponding model order of the measured frequency. However, because of the uncertain flexural rigidity, this empirical order determination method not only plays a limited role in high-order frequencies, but also hinders the online cable force estimation. Therefore, we propose a new method to automatically identify the corresponding model order of the measured frequency, which is based on a Markov chain Monte Carlo (MCMC)-based Bayesian approach. It solves the limitation of empirical determination in the case of large flexural rigidity. The tensile force and the flexural rigidity of cables can be calculated simultaneously using the proposed method. The feasibility of the proposed method is validated via a numerical study involving a finite element model that considers the flexural rigidity and via field application to a suspension bridge.

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Genetic Algorithm-based tension identification of hanger by solving inverse eigenvalue problem

Cited by 13 publications

References 11 publications

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Accuracy Improvement of Mutual Integration Mechanism Driven Algorithms for Boom Cable Force Recognition

Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach

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