• 국제초고층학회논문집
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• 제6권3호
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• pp.285-296
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• 2017

It's been a decade since post-tension system began to be applied in earnest to buildings in Korea. In the meantime, post-tension system has been used in various buildings as main structural system including tall buildings. And post-tension system plays a role to overcome architectural limit of regular RC tall buildings particularly in the realization of long span with shallower depth than other structural system. The post-tensioned building market of Korea has been steadily grown in recent years with such advantage. Recently, post-tension technology is adapted for special structural members like belt walls. In this paper, the authors would like to explain design and construction of tall buildings in Korea using post-tension technology.

• Smart Structures and Systems
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• 제31권4호
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• 한국구조물진단유지관리공학회 논문집
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• 제5권4호
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• pp.147-153
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• 2001

The major objectives of this study are to investigate experimental and analytical behavior of composite steel plate strengthened by external post prestressing method and to study the increasing magnitude of load carrying capacity by the external post prestressing method. With installed strain gauges and LVDT, the change of structural behaviors according to the amount of prestressing force is measured and the effects of shear strengthening according to the degree of angle in tendon are studied. The analytical structural behavior according to the amount of prestressing force is also investigated using finite element method. The effectiveness of strengthening of external post prestressing method is proved and an efficient FEM model is suggested by comparing the test results and analyzing results.

• 국제초고층학회논문집
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• 제10권4호
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• pp.323-334
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• 2021

Concrete structures may rarely collapse in fire incidents but fire induced damage to structural members is inevitable as a result of material degradation and thermal expansion. This requires certain repairing measures to be applied to restore the performance of post-fire members. A brief review on investigation of post-fire damage of concrete material and concrete structural members is presented in this paper, followed by a review of post-fire repair research regarding various types of repairing techniques (FRP, steel plate, and concrete section enlargement) and different type of structural members including columns, beams, and slabs. Particularly, the fire scenarios adopted in these studies leading to damage are categorized as three levels according to the duration of gas-phase temperature above 600℃ (t₆₀₀). The repair effectiveness in terms of recovered performance of concrete structural members compared to the initial undamaged performance has been summarized and compared regarding the repairing techniques and fire intensity levels. The complied results have shown that recovering the ultimate strength is achievable but the stiffness recovery is difficult. Moreover, the current fire loading scenarios adopted in the post-fire repair research are mostly idealized as constant heating rates or standard fire curves, which may have produced unrealistic fire damage patterns and the associated repairing techniques may be not practical. For future studies, the realistic fire impact and the system-level structural damage investigation are necessary.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1998년도 학술발표회 발표논문집
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• pp.176-179
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• 1998

In structural analysis using FEM or BEM, pre_ and post_processor is necessary pre_ and post_processor and analyzer use same structural model. But many other tasks related to structural design, such as optimization, design of layout, etc, do not share that model in spite of their resemblance of requiring data. So, a pre_ and post_ processor was developed using a relational database managing system. Developed system uses the DBMS as a data storage and interacts with it using SQL interface. In this way, many other tasks that uses same structural data can be developed.

• 한국초전도ㆍ저온공학회논문지
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• 제3권1호
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• pp.45-49
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• 2001

A magnet supporting post installed between the lower TF coil tooled by 4.5 K supercritical helium and the cryostat base is one of the most important components of the superconducting magnet supporting structure for KSTAR Tokamak. This structure should be flexible to absorb thermal shrink of the magnet and also should be rigid to support the magnet weight and the Plasma disruptions load. The Post was designed with stainless steel 316LN and CFRP that have low thermal conductivity and high structural strength at low temperature. In order to verify the possibility of fabrication and the structural safety. a whole scale prototype of the KSTAR magnet supporting post was manufactured and tested. Static and compressive cyclic load tests under the maximum Plasma vertical disruption load and the magnet dead weight were performed. The teat results showed that the magnet supporting post of KSTAR Tokamak was possible to manufacture and structurally rigid.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 가을 학술발표회 논문집
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• pp.103-110
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• 1995

This Paper develops a new control system to reduce the vibration of bridges using King-post mechanism. The optimal active control theory is used in the control system. The positions of post and roller in the King-post mechanism are important factors, since the vibration of bridges is very sensitive to their variations. To demonstrate the efficiency of the proposed control mechanism, a simply supported beam under moving load is considered.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1994년도 가을 학술발표회 논문집
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• pp.17-24
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• 1994

The pre- and post-processor for finite element structural analysis considering the user-friendly device are developed by using GUI. These can be used on WINDOWS' environment which is realized the multi-tasking and the concurrency by object-oriented paradigm. They are designed to control integratedly the pre-processing, execution and the post-processing of the finite element structural analysis program on multiple windows. These object-oriented modeling approach can be used for complex integrated engineering systems.

• Structural Engineering and Mechanics
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• 제2권1호
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• pp.113-123
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• 1994

A method for computing the elastic buckling prestressing force of a post-tensioned composite steel-concrete tee-beam is presented. The method is based on a virtual work formulation, and incorporates the restraint provided by the concrete slab to the buckling displacements of the steel beam. The distortional buckling solutions are shown to be given by a quadratic equation. The application of the analysis to calculation buckling strengths is given, based on codified rules for beam-columns. Conclusions are then drawn on the importance of distortional buckling when a post-tensioned composite beam is stressed during jacking.

• Structural Engineering and Mechanics
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• 제91권5호
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.