• Structural Engineering and Mechanics
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• 제27권6호
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• pp.697-711
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• 2007

Though extensive research has been carried out for the ultimate strength of steel reinforced concrete (SRC) members under static and cyclic load, there was only limited information on the applied analysis models. Modeling of the inelastic response of SRC members can be accomplished by using a microcosmic model. However, generally used microcosmic model, which usually contains a group of parameters, is too complicated to apply in the nonlinear structural computation for large whole buildings. The intent of this paper is to develop an effective modeling approach for the reliable prediction of the inelastic response of SRC columns. Firstly, five SRC columns were tested under cyclic static load and constant axial force. Based on the experimental results, normalized trilinear skeleton curves were then put forward. Theoretical equation of normalizing point (ultimate strength point) was built up according to the load-bearing mechanism of RC columns and verified by the 5 specimens in this test and 14 SRC columns from parallel tests. Since no obvious strength deterioration and pinch effect were observed from the load-displacement curve, hysteresis rule considering only stiffness degradation was proposed through regression analysis. Compared with the experimental results, the applied analysis model is so reasonable to capture the overall cyclic response of SRC columns that it can be easily used in both static and dynamic analysis of the whole SRC structural systems.

• 한국강구조학회 논문집
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• 제10권1호통권34호
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• pp.25-35
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• 1998

각형 강관 기둥과 H형 보의 접합부에 대한 연구는 보의 하중을 기둥에 전달하는 과정에서 생기는 응력의 집중현상 때문에 하중을 효율적으로 전달하는 방식을 고안하는 것에 초점을 맞추었다. 본 연구에서는 외부보강형 접합부의 강도를 증가시키기 위한 방법으로 직사각형의 스티프너를 사용하였다. 실물의 절반크기로 시험체를 제작하여 단순 인장실험을 실시하였다. 항복선이론과 가상일법을 이용하여 접합부의 극한강도를 추정하는 공식을 유도하였으며 실험치에 근접한 결과를 얻었다.

• Computers and Concrete
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• 제32권5호
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• Earthquakes and Structures
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• 제8권5호
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2003년도 봄 학술발표회 논문집
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• pp.896-901
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• 2003

This paper describes a model on shear strength of RC columns strengthened with FRP sheets. In this study, we propose a confined concrete strength model of RC columns confined by transverse reinforcement as well as FRP sheet by introducing corresponding effective confinement coefficient for each confined concrete area. Then, a shear strength model of the confined RC columns is proposed by lower and upper bound limit analysis which are based on the truss-arch model theory and shear band failure theory, respectively. Along with shear test data obtained from strengthened column specimens, the developed analytical models are verified. The comparison shows that the proposed model can be used effectively for the prediction of both ultimate strength and required amount of strengthening in retrofit design for RC columns.

• Steel and Composite Structures
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• 제17권4호
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• pp.497-516
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• 2014

The ultimate carrying capacity of axially loaded welded square box section members made of medium and high strength steels (nominal yield stresses varying from 345 MPa to 460 MPa), with large width-to-thickness ratios ranging from 35 to 70, is analyzed by finite element method (FEM). At the same time, the numerical results are compared with the predicted results using Direct Strength Method (DSM), modified DSM and Effective Yield Strength Method (EYSM). It shows that curve a, rather than curve b recommended in Code for design of steel structures GB50017-2003, should be used to check the local-overall interaction buckling strength of welded square section columns fabricated from medium and high strength steels when using DSM, modified DSM and EYSM. Despite all this, EYSM is conservative. Compared to EYSM and modified DSM, DSM provides a better prediction of the ultimate capacities of welded square box compression members with large width-thickness ratios over a wide range of width-thickness ratios, slenderness ratios and steel grades. However, for high strength steels (nominal yield strength greater than 460 MPa), the numerical and existent experimental results indicate that DSM overestimates the load-carrying capacities of the columns with width-thickness ratio smaller than 45 and slenderness ratio less than 80. Further, for the purpose of making it suitable for a wider scope, DSM has been modified (called proposed modified DSM). The proposed modified DSM is in excellent agreement with the numerical and existing experimental results.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2008년도 추계 학술발표회 제20권2호
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• pp.265-268
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• 2008

슬래브-기둥 접합부는 많은 구조물의 시공에 사용되고 있다. 그러나 횡하중을 받는 슬래브-기둥 접합부의 파괴거동 및 극한강도를 예측하는 것이 매우 어렵기 때문에 현행 구조물 설계기준은 횡하중을 받는 슬래브-기둥 접합부의 파괴 거동을 명확하게 설명하지 못하고 있다. 본 논문에서는 횡하중을 받는 슬래브-기둥 접합부의 극한해석과 설계를 위하여 응력교란영역을 가지는 3차원 구조의 해석과 설계를 위하여 제안된 3차원 격자 스트럿-타이 모델 방법을 파괴실험이 수행된 43개의 횡하중을 받는 슬래브-기둥 접합부의 극한강도 평가에 적용하고, ACI 318-05 기준과 FIB 1999 기준에 의한 극한강도 평가결과와 비교함으로써 3차원 격자 스트럿-타이 모델 방법의 타당성을 검토하였다.

• 한국기계가공학회지
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• 제20권5호
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• pp.96-102
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• 2021

Hexagonal bolt, nut, fittings, and high-pressure valves with special alloy play an important role in many industrial products. Numerical analysis was conducted to obtain data for designing a new drawing system. This study aims to predict the rolling force of the new drawing system compared to that of the established drawing system. The rolling force of the new drawing system was predicted using numerical analysis by assuming that it is in proportion to deformation. The rolling forces of Mo, Ti, and W were approximately 1.4, 0.5, and 2.5 times those of SUS. Because the values of ultimate strength of special alloys were more close to numerical analysis, the values of ultimate strength could be used to predict the rolling force of the new drawing system without numerical analysis in field.

• Steel and Composite Structures
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• 제46권2호
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• 한국강구조학회 논문집
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• 제13권2호
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• pp.177-189
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• 2001

현대 건축물들의 고층화 추세에 따라 등장한 구조물의 공사비 절감을 위한 한 방법으로 H형강보의 웨브 부분에 설비용 덕트와 배관류 등을 관통시키기 위하여 개구부를 두는 유공보를 자주 사용하고 있다. 본 연구에서는 현재 유공보의 설계에 사용되고 있는 기존의 강도식들의 문제점을 살펴보고 소성 붕괴 매커니즘에 기초를 둔 보다 합리적인 강도식을 제안하고자 한다. 제안식은 기존의 강도식들과 비교하여 볼 때 단순하며 이해하기 쉽다. 제안식의 검증은 과거에 이루어진 연구 결과들을 사용하여 평가되어 졌다. 이들 연구결과들을 근거로 제안식의 극한 강도를 비교해 볼 때 현재 실무에서 사용되어 지고있는 강도식들보다 합리적인 결과 값을 기대할 수 있다.