• International Journal of High-Rise Buildings
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• v.8 no.3
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• pp.211-220
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• 2019

Three cases of supertall connected structures are presented and each of them represents a quite style. The first case is a strong-connected structure. The coupling function of towers and connector contributes a lot to the structural stiffness and stability. Its special construction scheme had great impact on the construction quality and the structural safety, and must be accurately considered. For the second case which is a weak-connected structure, the influences of different connecting modes to the structural dynamic characteristic were explained. Then the combined bearings were proposed to achieve the design presume. In the third case which represents the multi-supported structures, the structural distinctive mechanical properties were discussed. For the structural state during construction process is quite different from that in final service condition, two construction procedures were simulated to get an optimal one. Although there are great challenges to designers, the advantages of the supertall connected buildings are obvious. Further work is needed in this area to adapt to the development of future cities.

• Journal of the Korean Society for Advanced Composite Structures
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• v.4 no.3
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• pp.30-37
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• 2013

It is same such as the provision of shear buckling strength of steel composite box girder web panel and plate girder web panel in Korea Highway Bridge Design Standards(2012). But the web panel of steel composite box girder is different from the web of plate girder in that the upper slab and lower flange are connected to the web. So a different shear behavior of the girders is expected. In this study, To calculate a reasonable elastic shear buckling strength of steel composite box girder web panel, ABAQUS program was used. The results from F.E.A and previous studies are compared. It is shown that the web shear buckling strength of steel composite box girder of Korea Highway Bridge Design Standards(2012) is the most conservative.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.