• 건축사
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• 통권487호
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• pp.13-13
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• 2009

• 건축사
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• 통권575호
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• pp.90-103
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• 2017

• 건축사
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• 통권475호
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• pp.50-55
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• 2008

• 건축사
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• 통권491호
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• pp.40-45
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• 2010

• 지구물리와물리탐사
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• 제27권2호
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• pp.144-153
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• 2024

최근 탄성파를 기반으로 건축물 안전진단(structure health monitoring, SHM)을 수행하는 방법들에 대한 연구들이 많이 수행되고 있다. 특히 지구물리탐사에서 주로 적용되어 오던 배경 잡음을 이용하는 탄성파 간섭법(seismic interferometry)이 SHM에 많이 적용되고 있다. 탄성파가 건축물 내부로 전파하며 발생하는 건축물의 반응을 분석하여 건축물의 강성 변화를 추정할 수 있을 뿐만 아니라, 건축물의 손상 여부와 그 위치도 평가할 수 있다. SHM에 적용되는 탄성파 간섭법에 대해 분석한 뒤 실제 적용 사례들도 분석한 결과, 탄성파 간섭법은 건축물의 안정성 평가나 모니터링 등에 적용할 수 있는 건축물 손상 탐지 평가 방법으로써 매우 효과적으로 활용할 수 있다고 판단된다.

• 한국지진공학회논문집
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• 제26권2호
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• pp.83-93
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• 2022

In this study, to verify the structural performance of the Composite Joint System (CJS) hybrid structural model, a cyclic load test was performed and evaluated and verified through the test. To verify the structural performance of the CJS hybrid structural systems' joint and evaluate the seismic performance, four three-dimensional real-size specimens were developed with three internal beam-column specimens and one external beam-column specimen. The three interior column specimens were classified by different methods of joining the upper column and lower column, and the same bonding method as the primary specimen was used for the exterior column. The structural performances in terms of drift, strength, and energy dissipation capacity were analyzed and compared based on the experimental results. From the displacement-based loading experiment, all specimens showed a lateral drift of 4.0% without any significant strength drop and stable energy dissipation capacity.

• 한국지진공학회논문집
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• 제26권2호
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• pp.71-82
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• 2022

This paper presents the effect on the inelastic behavior and structural performance of concrete and filled steel pipe through a numerical method for reliable judgment under various load conditions of the CJS composite structural system. Variable values optimized for the CJS synthetic structural system and the effects of multiple variables used for finite element analysis to present analytical modeling were compared and analyzed with experimental results. The Winfrith concrete model was used as a concrete material model that describes the confinement effect well, and the concrete structure was modeled with solid elements. Through geometric analysis of shell and solid elements, rectangular steel pipe columns and steel elements were modeled as shell elements. In addition, the slip behavior of the joint between the concrete column and the rectangular steel pipe was described using the Surface-to-Surface function. After finite element analysis modeling, simulation was performed for cyclic loading after assuming that the lower part of the foundation was a pin in the same way as in the experiment. The analysis model was verified by comparing the calculated analysis results with the experimental results, focusing on initial stiffness, maximum strength, and energy dissipation capability.

• 안전기술
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• 제195호
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• pp.21-23
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• 2014

서울의 중심인 광화문 일대는 '경제대국 대한민국'을 상징하는 각종 건축물이 모여 있는 곳이다. 우뚝 솟은 건물들이 만들어내는 아름다운 경관에 우리 국민은 물론 이곳을 찾는 수많은 외국 관광객들이 탄성을 쏟아낸다. 그런 이곳에 또 하나의 랜드마크가 베일을 벗고 모습을 보일 준비를 하고 있다. 그 주인공은 바로 대림산업이 세종로 사거리 옛 금강제화 터에 건설 중인 '세종로호텔' 현장이다. 우리나라 경제와 관광의 중심지에 건립되는 만큼, 이곳은 안전중심의 철저한 시공을 하고 있다. 도심 속 건설현장의 우수사례로 손꼽히고 있는 이곳 현장을 찾아가봤다.

• 한국공간구조학회논문집
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• 제21권2호
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• pp.89-97
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• 2021

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.