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The paper deals with the structural analysis and vibration test for the scaffolding system of LNG cargo containment. The eight-stories scaffolding system has telescopic area, working area, coner area and storage area in real system. In the structural analysis, the maximum displacement and stress of the each floor for the scaffolding system are investigated by finite element method. In the vibrational analysis, the natural frequencies and mode shapes for 8-stories scaffolding system of the LNG cargo containment are investigated. In order to compare theoretical natural frequencies with experimental ones, small size of 2-step scaffolding structure is used, and the theoretical results for natural frequency have a good agreement with experimental ones.

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Prestressed concrete (PSC) girder bridges are widely used owing to their economic efficiency, durability, and effective maintenance. However, since voids in ducts may cause sudden structural collapse, it is very important to detect them early. To solve this problem, voids are detected by analyzing the impact-echo (IE) signal measured by IE equipment containing a sensor, but it is difficult to accurately detect voids in a short time even by experts. In this study, we aim to detect voids in ducts on the basis of various types of neural networks and IE signals. For more effective learning, the raw IE signal is filtered and then used in its specific range, and it is also converted into a frequency spectrum by the Fourier transform. The filtered IE signal is trained with long short-term memory (LSTM) to reflect the characteristics of its time series. The frequency spectrum is trained with a feed-forward neural network because it is not a time series. After that, a multiplication operation is performed on the outputs of each network, and a model capable of detecting the internal voids of ducts is created by training these integrated features. In the experimental results, our proposed model showed an accuracy of 97.474%.Yu-Seop Kim received his Ph.D. degree in computer engineering from Seoul National University, Republic of Korea. He is currently a professor in the School of Software, Hallym University. His research interests are in the areas of bioinformatics, computational intelligence, and natural language processing.