&lt;title&gt;Damage assessment of bridges with jacketed RC columns using vibration test&lt;/title&gt;

Feng, Maria Q.; Bahng, Eun Young

doi:10.1117/12.348682

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Cited by 2 publications

(2 citation statements)

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“…Different reports [188][189][190] assess the benefits and drawbacks of using sensors and NN to detect impact in composite materials, which could be a possibility for SHM of OWT blades.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

225

124

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“…Different reports [188][189][190] assess the benefits and drawbacks of using sensors and NN to detect impact in composite materials, which could be a possibility for SHM of OWT blades.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Martínez-Luengo

Kolios

Wang

2016

Renewable and Sustainable Energy Reviews

225

124

View full text Add to dashboard Cite

“…The authors found that a two-step neural network successfully located the damage in the finite element model. Feng and Bahng (1999) proposed a method for the monitoring of jacketed RC columns using a combination of ANN, finite element techniques and vibration testing. In this research, the input patterns included the mode frequencies and mode shapes of columns determined from the FEA.…”

Section: Framesmentioning

confidence: 99%

Modal parameters based structural damage detection using artificial neural networks - a review

Hakim¹,

Razak²

2014

Smart Structures and Systems

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One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

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<title>Damage assessment of bridges with jacketed RC columns using vibration test</title>

Cited by 2 publications

References 0 publications

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Modal parameters based structural damage detection using artificial neural networks - a review

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