Damage Assessment of Jacketed RC Columns Using Vibration Tests

Feng, Maria Q.; Bahng, Eun Young

doi:10.1061/(asce)0733-9445(1999)125:3(265)

Cited by 46 publications

(23 citation statements)

References 13 publications

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“…Proof of this is the review of damage detection methods through the change in modal properties presented in [26]. The relevant method are: Natural Frequency Based Methods [93], Mode Shape Based Methods [94][95][96][97][98], Mode Shape Curvature Based Methods [99][100][101][102][103], Strain Mode Shape Based Methods [71,[104][105][106][107][108], Dynamically Measured Flexibility Based Methods [109][110][111], and Neural Network Based Methods [112][113][114][115][116][117].…”

Section: Offshore Wind Turbines Damage Definition and Detectionmentioning

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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Section: Offshore Wind Turbines Damage Definition and Detectionmentioning

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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“…In civil engineering, neural networks were well applied to the detection of structural damage (Feng and Bahng 1999), the identification and control of structural systems (Feng and Bahng 1999;Chen et al 1995), the modeling of material behavior (Adeli and Park 1995), and the proportion of concrete mixtures (Oh et al 1999). In addition, the compressive strength of concrete (Kim et al 2004;Kim et al 2005) was effectively estimated by several researchers, including the authors of this paper, who have applied the neural networks.…”

Section: Principles Of Neural Networkmentioning

confidence: 99%

Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method

Lee

Cho

2012

Int J Concr Struct Mater

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The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water-cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

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“…An important issue in structural health monitoring is selection of the members and locations of the structure to be monitored. Feng and Bahng (1999) use the BP algorithm to estimate the change in stiffness based on the measured vibration characteristics for damage assessment of reinforced concrete columns retrofitted by advanced composite jackets. Kim et al (2000b) describe a two-stage procedure where in the first stage traditional sensitivity analysis is used to rank and select critical members.…”

Section: Structural Condition Assessment and Monitoringmentioning

confidence: 99%

Neural Networks in Civil Engineering: 1989–2000

Adeli

2001

Computer aided Civil Eng

711

344

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The first journal article on neural network application in civil/structural engineering was published in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.

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Damage Assessment of Jacketed RC Columns Using Vibration Tests

Cited by 46 publications

References 13 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

Effective Prediction of Thermal Conductivity of Concrete Using Neural Network Method

Neural Networks in Civil Engineering: 1989–2000

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