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Structural damage identification based on autoencoder neural networks and deep learning

Engineering Structures · 2018 · Vol. 172 · pp. 13–28

Chathurdara Sri Nadith Pathirage(Curtin University)Jun Li(Curtin University)Ling Li✉(Curtin University)Hong Hao(Curtin University)Wanquan Liu(Curtin University)Pinghe Ni(Hong Kong Polytechnic University)

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Structural Health Monitoring Techniques Infrastructure Maintenance and Monitoring Concrete Corrosion and DurabilityAutoencoderArtificial neural networkArtificial intelligenceDimensionality reductionComputer scienceDeep learningCurse of dimensionalityComponent (thermodynamics)Pattern recognition (psychology)Reduction (mathematics)

Funding

Australian Research Council

Citations

385

FWCI

25.10

field-weighted impact

References

43

Percentile

100%

vs. same field & year

Citations per year

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Reducing the Dimensionality of Data with Neural Networks

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Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

Mechanical Systems and Signal Processing · 2015 · 548 citations

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Mechanical Systems and Signal Processing · 2015 · 1,655 citations

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Journal of Sound and Vibration · 2016 · 1,202 citations

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