2015
DOI: 10.1177/1056789515598639
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Ensemble neural networks for structural damage identification using modal data

Abstract: Damage in a structure is defined as changes to its geometric and material properties, leading to a reduction in the structural stiffness which negatively affects the performance of the structure. Reduction in the structural stiffness produces changes in the modal parameters such as the natural frequencies and mode shapes. Artificial neural networks (ANNs) have been applied extensively in recent years due to their excellent pattern recognition ability that is useful for structural damage identification purposes… Show more

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Cited by 17 publications
(10 citation statements)
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“…The NNs have been proved to be a reliable technique not only for solving the inverse/ optimization problems such as image processing, object recognition, speech recognition, and structural damage identification owing to their capabilities of pattern recognition and classification (Wu et al, 1992;Liu et al, 2002;Yam et al, 2003;Zapico et al, 2003;Mehrjoo et al, 2008;Nematollahi et al, 2012;Aydin and Kisi, 2015;Hakim et al, 2016) but also for direct problems in engineering such as finding solutions of partial differential equations (Anitescu et al, 2019;Guo et al, 2019;Samaniego et al, 2020). They also have noise filtering capabilities that make them more robust in the presence of measurement noise and other uncertainties.…”
Section: Multiple Crack Detection By the Neural Networkmentioning
confidence: 99%
“…The NNs have been proved to be a reliable technique not only for solving the inverse/ optimization problems such as image processing, object recognition, speech recognition, and structural damage identification owing to their capabilities of pattern recognition and classification (Wu et al, 1992;Liu et al, 2002;Yam et al, 2003;Zapico et al, 2003;Mehrjoo et al, 2008;Nematollahi et al, 2012;Aydin and Kisi, 2015;Hakim et al, 2016) but also for direct problems in engineering such as finding solutions of partial differential equations (Anitescu et al, 2019;Guo et al, 2019;Samaniego et al, 2020). They also have noise filtering capabilities that make them more robust in the presence of measurement noise and other uncertainties.…”
Section: Multiple Crack Detection By the Neural Networkmentioning
confidence: 99%
“…Each mode corresponds to specific natural frequencies, damping ratios, formations and other natural vibration characteristics (Hwang et al., 2019). Damage in the structure produces reduction in the structural stiffness and changes the modal parameters such as the natural frequencies (Hakim et al., 2016; Sayyad and Kumar, 2012). If the natural frequency of the wind turbine tower structure is the same as the fan impeller rotation frequency, the structure will resonate (Smilden et al., 2020).…”
Section: Stiffness Analysis Of Segmented Concrete Tower With Differenmentioning
confidence: 99%
“…Engineering applications of ensembles of neural networks cover such problems as: prediction of heavy construction equipment, namely tunnel boring machine, performance (Z. Zhao, Gong, Zhang, & J. Zhao, 2007), structural damage identification (Hakim, Razak, & Ravanfar, 2016), prediction of heating energy consumption (R. Jovanović, R. Ž. Jovanović, & Sretenović, 2017), and visual identification of village buildings (Guo et al, 2017).…”
Section: State-of-the-art and Literature Reviewmentioning
confidence: 99%