Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning

During the long-term operation of hydro-junction infrastructure, water flow erosion causes concrete surfaces to crack, resulting in seepage, spalling, and rebar exposure. To ensure infrastructure safety, detecting such damage is critical. We propose a highly accurate damage detection method using a...

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Veröffentlicht in:	KSCE Journal of Civil Engineering Jg. 23; H. 10; S. 4493 - 4502
Hauptverfasser:	Feng, Chuncheng, Zhang, Hua, Wang, Shuang, Li, Yonglong, Wang, Haoran, Yan, Fei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Seoul Korean Society of Civil Engineers 01.10.2019 Springer Nature B.V 대한토목학회
Schlagworte:	Accuracy Artificial neural networks Asphalt pavements Cameras Civil Engineering Concrete Cracks Damage detection Datasets Deep learning Detection Engineering Erosion Erosion mechanisms Geotechnical Engineering & Applied Earth Sciences Global positioning systems GPS High definition Hydroelectric power Industrial Pollution Prevention Infrastructure Internet Learning Machine learning Methods Neural networks Rebar Reinforcing steels Research centers Science Seepage Spalling Structural damage Structural Engineering Students Support vector machines Transfer learning Unmanned aerial vehicles Water flow 토목공학 hydro-junction infrastructure damage detection transfer learning structural health monitoring concrete surface defect deep convolutional neural network
ISSN:	1226-7988, 1976-3808
Online-Zugang:	Volltext
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