• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.1
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• pp.62-69
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• 1986

Fatigue surface crack growth was studied in 7075-T651 aluminum alloy plates subjected largely to bending loads. The surface crack length and its depth were measurement by the unloading elastic compliance method. The surface crack growth rate dc/dN, on the surface and da/dN, in the depth direction were obtained by the secant method. The stress intensity factor range .DELTA.K was computed by means of Newman and Raju equation. The aspect ratio a/c was presented in form of a/c=0.815-0.853(a/T). The effect of the stress ratio on the stable surface crack growth rates under increasing .DELTA.T is larger in lower .DELTA.K, while the relation between dc/dN, da/dN and the effective stress intensity factor range .DELTA.K$_{eff}$ is weakly dependent on the stress ratio.o.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.