Effects of Measuring Techniques on the Accuracy of Estimating Cable Tension in a Cable-Stay Bridge

Ho, Long Viet; Hoang, Thanh Nam; Roeck, Guido De; Bui, Tung Thanh; Wahab, Magd Abdel

doi:10.1007/978-981-13-8331-1_31

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…After that, an optimization-based model updating technique is used to minimize the difference between modal characteristics of cables with the numerical counterpart, including eigenfrequencies and mode shapes. As detailed solutions for similar problems can be found in the optimization literature [33], [34], thus for brevity, one only presents measured forces in cables in Table V.…”

Section: B Case Study 2: My Thuan Stayed-bridgementioning

confidence: 99%

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Dang

Tran-Ngoc

Nguyen

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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Section: B Case Study 2: My Thuan Stayed-bridgementioning

confidence: 99%

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Dang

Tran-Ngoc

Nguyen

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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“…A simple excitation induced by a hammer blow or under ambient load (Ho et al, 2019;Nguyen et al, 2020a) can provide values of natural frequency, and mode shapes of laboratory beams with high accuracy. Ho et al (2020) used piezoelectric sensors to obtain the vibration frequencies of cables of a real cable-stayed bridge under ambient load. These are the most popular parameters used for damage assessment.…”

Section: Introductionmentioning

confidence: 99%

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Nguyen

Roeck

et al. 2021

J. Zhejiang Univ. Sci. A

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Over recent decades, the artificial neural networks (ANNs) have been applied as an effective approach for detecting damage in construction materials. However, to achieve a superior result of defect identification, they have to overcome some shortcomings, for instance slow convergence or stagnancy in local minima. Therefore, optimization algorithms with a global search ability are used to enhance ANNs, i.e. to increase the rate of convergence and to reach a global minimum. This paper introduces a two-stage approach for failure identification in a steel beam. In the first step, the presence of defects and their positions are identified by modal indices. In the second step, a feedforward neural network, improved by a hybrid particle swarm optimization and gravitational search algorithm, namely FNN-PSOGSA, is used to quantify the severity of damage. Finite element (FE) models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method. For comparison, a traditional ANN is also used to estimate the severity of the damage. The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.

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Effects of Measuring Techniques on the Accuracy of Estimating Cable Tension in a Cable-Stay Bridge

Cited by 2 publications

References 6 publications

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

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