• 한국구조물진단유지관리공학회 논문집
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• 제10권6호
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• pp.175-182
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• 2006

강 콘크리트 합성구조에서 강재와 콘크리트 사이의 경계면에 효과적인 응력전달과 합성거동을 유도하기 위하여 스터드, 채널, 유공강판 등이 사용된다. 가장 광범위하게 사용하는 전단연결재는 스터드 형식이고, 최근에 들어서는 강판에 구멍을 뚫은 유공강판전단연결재인 Perfobond가 주목을 받고 있다. 본 연구는 강 콘크리트 합성교량에 적용하기 위한 Perfobond형 전단연결재의 연성능력과 전단성능을 향상시킬 목적으로 횡방향 전단구속철근을 배치하고, Push-out 실험을 수행하여 전단내력을 비교하였다. 실험결과, 유공강판 전단연결재에 전단구속철근, 횡방향 관통철근, 단부 지압판 등을 설치함에 따른 수평 저항성능이나 다웰효과 등에 의해 전단내력이 상승하였으며, 또한 최대내력 이후 변형능력이 유지되면서 연성거동 특성을 보였다.

• 한국구조물진단유지관리공학회 논문집
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• 제10권1호
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• pp.123-130
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• 2006

콘크리트의 열화 및 철근부식에 대응하기 위하여 다양한 보수재료가 존재하나 이러한 보수재료를 평가하는 방법은 단순히 염해, 중성화 등의 단독열화만을 대상으로 하고 있어 여러 가지 열화인자가 복합적으로 작용하는 실제 환경과는 차이를 나타내게 된다. 따라서 본 연구에서는 폴리머시멘트계 보수용 단면복구재가 KS 기준을 만족하는지 확인한 후 복합열화 환경 하에서 염해, 중성화. 철근부식을 평가하였다. 실험결과 각각의 보수재료들은 KS 기준을 만족하였지만 내구성능은 상이하게 나타나 향후 이에 대한 고려가 필요할 것으로 사료된다.

• 한국구조물진단유지관리공학회 논문집
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• 제12권1호
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• pp.81-90
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• 2008

본 연구에서는 유한 요소법과 고도화된 손상 탐지 기법을 결합하여 구조적 손상을 규명하는 방법을 다룬다. 본 연구의 특징은 충격하중을 받는 구조물의 동적 거동 특성을 분석하여 이를 임의의 손상 형태를 갖는 판에 적용한다는 것이다. 이러한 방법은 손상된 부위의 강성 분포를 추정할 뿐만 아니라 손상의 정도도 파악할 수 있는 장점을 갖으며 분할 요소수의 제한을 두지 않는다. 제안된 방법을 검증하기 위하여 본 알고리즘은 임의의 손상을 갖는 박판에 대하여 적용하기 한다. 수치해석 결과로부터 제안된 알고리즘은 수치적 효율성과 함께 임의의 손상 분포를 규명할 수 있음을 보여준다.

• 한국구조물진단유지관리공학회 논문집
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• 제13권6호통권58호
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• pp.126-134
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• 2009

일반적으로 철근콘크리트 건축물은 외부의 기후에 노출되어 있어 겨울에서 이른 봄까지 동결과 융해의 반복적인 작용에 영향을 받는다. 이러한 동결융해 작용은 콘크리트의 균열을 발생시키거나 콘크리트 표면의 박리를 일으켜 내구성 저하의 원인이 된다. 본 연구에서는 철근콘크리트 보의 동결융해 노출에 따른 휨 거동특성의 평가를 위해 주근비와 동결융해 사이클을 변수로 하였다. $-18{\sim}4^{\circ}C$의 온도범위에서 150 및 300 사이클의 동결융해에 노출시킨 실험체를 비롯하여 14개의 축소모형 실험체를 제작, 단조 및 반복하중 하에서 실험을 실시하였다. 실험결과를 통해 동결융해에 노출되어있는 철근콘크리트 보의 휨 거동특성을 평가하는데 기초적인 자료를 제시하고자 하였다.

• 한국구조물진단유지관리공학회 논문집
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• 제12권4호
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• pp.195-202
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• 2008

현존하는 교량의 실제적인 거동에 대한 보다 정확한 예측방법의 개발은 보수보강이 필요한 교량에 예산이 집중될 수 있도록 하여 교량운영의 경제성 및 안전성 측면에서 매우 중요하다. 특히 교량의 형태와 설치 지역의 활하중의 특성을 고려하며 활하중에 교량이 반응하는 실제적인 거동을 파악하여 실제적인 교량의 내하력 평가 이외에도 평가대상 교량의 선정 및 평가의 우선순위를 결정하여 교량의 유지 보수에 사용되는 예산의 보다 효율적인 집행을 가능하게 할 수 있다. 이 연구에서는 교량 현장실험에서 얻어지는 결과를 신뢰성 해석에 반영하여 보다 실제적인 교량 안전성 평가의 방법론을 연구하였다. 17개의 강거더 교량에 대해 기존의 교량 실험 결과를 토대로 교량의 내하력을 평가하기 위하여 2단계의 신뢰성 해석을 수행하였다. 우선 대상교량에 대해 설계에 사용된 계수 및 공칭강도를 이용하여 신뢰성 해석을 수행하였으며 2단계 신뢰성 해석에서는 교량 실험 결과를 신뢰성 해석에 포함하였다. 해석 결과를 비교해 본 결과 교량실험을 통한 각종 구조적 계수의 불확실성 제거를 통해 교량의 안전성을 저해하지 않고도 대상 교량의 신뢰성이 대폭 증가하는 결과를 얻을 수 있었다.

• Steel and Composite Structures
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• 제52권2호
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• pp.121-134
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• 2024

To investigate the compressive behavior of partially encased recycled aggregate concrete (PERAC) stub columns after exposed to elevated temperatures, 22 specimens were tested. The maximum temperature suffered, the replacement ratio of recycled coarse aggregate (RCA), the endurance time and the spacing between links were considered as the main parameters. It was found that the failure mode of post-heated PERAC columns generally matched that of traditional partially encased composite (PEC) columns, but the flange of specimens appeared premature buckling after undergoing the temperature of 400℃ and above. Additionally, the ultimate strength and ductility of the specimens deteriorated with the elevated temperatures and extended heating time. When 400℃< T ≤ 600℃, the strength reduction range is the largest, about 11% ~ 17%. The higher the replacement ratio of RCA, the lower the ultimate strength of specimens. At the temperature of 600℃, the ultimate strength of specimens with the RCA replacement ratio of 50% and 100% is 0.94 and 0.91 times than that of specimens without RCA, respectively. But the specimen with 50% replacement ratio of RCA showed the best ductility performance. And the bearing capacity and ductility of PERAC stub columns were changed for the better due to the application of links. When the RCA replacement ratio is 100%, the ultimate strength of specimens with the link spacing of 100 mm and 50 mm increased 14% and 25% than that of the specimen without links, respectively. Based on the results above, a formula for calculating the ultimate strength of PERAC stub columns after exposure to high temperatures was proposed.

• Steel and Composite Structures
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• 제52권2호
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• pp.135-143
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• 2024

Once the foundation displacement of the transmission tower occurs, additional stress will be generated on the tower members, which will affect the seismic response of transmission tower-line systems (TTLSs). Furthermore, existing research has shown that the reciprocating slippage of joints needs to be considered in the seismic analysis. The hysteretic behavior of joints is obtained by model tests or numerical simulations, which leads to the low modeling efficiency of TTLSs. Therefore, this paper first utilized numerical simulation and model tests to construct a BP neural network for predicting the skeleton curve of joints, and then a numerical model for a TTLS considering the bolt slippage was established. Then, the seismic response of the TTLS under foundation displacement was studied, and the member stress changes and the failed member distribution of the tower were analyzed. The influence of foundation displacement on the seismic performance were discussed. The results showed that the trained BP neural network could accurately predict the hysteresis performance of joints. The slippage could offset part of the additional stress caused by foundation settlement and reduce the stress of some members when the TTLS with foundation settlement was under earthquakes. The failure members were mainly distributed at the diagonal members of the tower leg adjacent to the foundation settlement and that of the tower body. To accurately analyze the seismic performance of TTLSs, the influence of foundation displacement and the joint effect should be considered, and the BP neural network can be used to improve modeling efficiency.

• Steel and Composite Structures
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• 제52권2호
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Steel and Composite Structures
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• 제52권3호
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• Steel and Composite Structures
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• 제52권3호
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• pp.273-291
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• 2024

This paper presents a three-dimensional displacement-based formulation to investigate the free vibration of functionally graded nanoplates resting on a Winkler-Pasternak foundation based on the nonlocal elasticity theory. The material properties of the FG nanoplate are considered to vary continuously through the thickness of the nanoplate according to the power-law distribution model. A general three-dimensional displacement field is considered for the plate, which takes into account the out-of-plane strains of the plate as well as the in-plane strains. Unlike the shear deformation theories, in the present formulation, no predetermined form for the distribution of displacements and transverse strains is considered. The equations of motion for functionally graded nanoplate are derived based on Hamilton's principle. The solution is obtained for simply-supported nanoplate, and the predicted results for natural frequencies are compared with the predictions of shear deformation theories which are available in the literature. The predictions of the present theory are discussed in detail to investigate the effects of power-law index, length-to-thickness ratio, mode numbers and the elastic foundation on the dynamic behavior of the functionally graded nanoplate. The present study presents a three-dimensional solution that is able to determine more accurate results in predicting of the natural frequencies of flexural and thickness modes of nanoplates. The effects of parameters that play a key role in the analysis and mechanical design of functionally graded nanoplates are investigated.