• Land and Housing Review
• /
• v.2 no.3
• /
• pp.287-297
• /
• 2011

This paper presents the results of performance verification tests of the isolated flat plate apartment building with the laminated rubber bearings. The shaking table test is carried out in CABR(China Academy of Building Research) with two 1/10 scale isolation and non-isolation models under 4 excitation waves. The shaking table test is proceeding from x axis, y axis and x+y axis with different amplitude of acceleration values. The results show that, to non-isolated model, the natural vibration period is remarkably decreased and entered non-linear condition after moderate earthquake. Its accelerations become lager with increasing storey number and completely collapsed under large earthquake. The inter-storey shifts largely exceed the limit values of regulated displacement angles. But to isolated model, the natural vibration period of isolated modal is almost the same in all conditions and still in its elastic condition. The earthquake loading is greatly reduced and the accelerations of superstructure are greatly reduced. The inter-storey drifts are very small and can be neglected. The isolated model is in translational state and can be seen as a rigid whole. The displacements of isolation layer are in the allowable range. This experiment demonstrates that the seismic isolation is very effective to mitigate the influence of earthquake on structures and it is possible to increase the serviceability due to decrease the floor acceleration. facilities from their good states that is superior to non-isolated structure.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.2
• /
• pp.28-36
• /
• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.8
• /
• pp.129-136
• /
• 2006

Engineered mixtures, which consist of rigid sand particles and soft fine-grained rubber particles, are tested to characterize their small and large-strain responses. Engineered soils are prepared with different volumetric sand fraction, sf, to identify the transition from a rigid to a soft granular skeleton using wave propagation, $K_{o}-loading$, and triaxial testing. Deformation moduli at small, middle and large-strain do not change linearly with the volume fraction of rigid particles; instead, deformation moduli increase dramatically when the sand fraction exceeds a threshold value between sf=0.6 to 0.8 that marks the formation of a percolating network of stiff particles. The friction angle increases with the volume fraction of rigid particles. Conversely, the axial strain at peak strength increases with the content of soft particles, and no apparent peak strength is observed in specimens when sand fraction is less than 60%. The presence of soft particles alters the formation of force chains. While soft particles are not part of high-load carrying chains, they play the important role of preventing the buckling of stiff particle chains.