• Steel and Composite Structures
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• 제30권3호
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• pp.217-230
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• 2019

One of the most important design criteria in underground structure is to design lightweight protective layers to resist significant blast loads. Sandwich blast resistant panels are commonly used to protect underground structures. The front face of the sandwich panel is designed to resist the blast load and the core is designed to mitigate the blast energy from reaching the back panel. The design is to allow the sandwich panel to be repaired efficiently. Hence, the underground structure can be used under repeated blast loads. In this study, a novel sandwich panel, named RC panel - Helical springs- RC panel (RHR) sandwich panel, which consists of normal strength reinforced concrete (RC) panels at the front and the back and steel compression helical springs in the middle, is proposed. In this study, a detailed 3D nonlinear numerical analysis is proposed using the nonlinear finite element software, AUTODYN. The accuracy of the blast load and RHR Sandwich panel modelling are validated using available experimental results. The results show that the proposed finite element model can be used efficiently and effectively to simulate the nonlinear dynamic behaviour of the newly proposed RHR sandwich panels under different ranges of free air blast loads. Detailed parameter study is then conducted using the validated finite element model. The results show that the newly proposed RHR sandwich panel can be used as a reliable and effective lightweight protective layer for underground structures.

• Computers and Concrete
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• 제33권4호
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• pp.361-372
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• 2024

Reinforced concrete (RC) bridge columns are typically designated as the primary source of energy dissipation for a bridge structure during an earthquake. Therefore, seismic repair of RC bridge columns has been studied extensively during the past several decades. On the other hand, few studies have been conducted to evaluate how repaired column members influence the system-level response of an RC bridge structure in subsequent earthquakes. In this study, a numerical model was established to simulate the response of two large-scale RC columns, repaired using different techniques, reported in the literature. The columns were implemented into a prototype bridge model that was subjected to earthquake loading. Incremental dynamic analysis (IDA) and fragility analysis were conducted on numerical bridge models to evaluate the efficacy of the repairs and the post-repair seismic performance of the prototype bridge that included one or more repaired columns in various locations. For the prototype bridge herein modeled, the results showed that a confinement-enhanced oriented repair would not affect the seismic behavior of the prototype bridge. Increasing the strength of the longitudinal reinforcement could effectively reduce the drift of the prototype bridge in subsequent earthquakes. A full repair configuration for the columns was the most effective method for enhancing the seismic performance of the prototype bridge. To obtain a positive effect on seismic performance, a minimum of two repaired columns was required.

• 한국전산구조공학회논문집
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• 제24권2호
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• pp.223-233
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• 2011

본 논문에서는 수직하중, 풍하중 및 지진하중에 의해 발생하는 변위 관련 구속조건을 만족하면서 RC(Reinforced Concrete) 빌딩 구조의 부피를 최소화하기 위한 설계문제를 정식화하였다. 구조해석 절차 자동화의 어려움으로 인해 실험 계획법과 근사화기법, 최적화기법을 이용한 근사모델기반 최적설계를 수행하였다. 특히, 만족할 만한 설계 결과를 얻을 때까지 설계변수의 범위와 구속조건의 허용값을 조정하는 단계적 최적설계 방법을 제안하였다. 제안된 단계적 최적설계 방법을 통해 주어진 구속조건을 모두 만족하면서 RC 빌딩 구조의 부피를 초기 설계 대비 53.3% 감소시키는 결과를 얻음으로 써 본 논문에서 보인 단계적 최적설계 방법의 타당성을 보였다.

• 한국지진공학회논문집
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• 제6권5호
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• pp.45-51
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• 2002

비선형동적해석을 통하여 RC 입체라멘교에 대한 지진거동특성 및 파괴메카니즘에 관한 연구를 수행하였다. 파이버모델에 기초한 RC 프레임요소를 교각에 도입하여 3차원영역에서 모델링하여 비선형동적해석을 수행하였다. 해석의 정확성을 향상시키기 위하여 균열 진전후 콘크리트와 철근의 부작특성에 의한 재료역학적 특성차이를 고려하기 위하여 파이버는 철근영역(RC zone)과 무근영역(PC zone)으로 영역화하였다. 대상교량은 관성력 중심위치와 교량의 강성중심 위치가 일치하지 않아 비틀림을 동반한 복잡한 지진거동특성을 나타내었다. 이러한 거동특성에 의하여 유연한 교각 옆에 위치하는 상대적 강성이 큰 교각에 과다한 지진하중이 집중되어 파괴에 이르는 것으로 나타났다.

• Geomechanics and Engineering
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• 제32권1호
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• pp.69-84
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• 2023

This paper proposes a novel frame element on Winkler-Pasternak foundation for analysis of a non-ductile reinforced concrete (RC) member resting on foundation. These structural members represent flexural-shear critical members, which are commonly found in existing buildings designed and constructed with the old seismic design standards (inadequately detailed transverse reinforcement). As a result, these structures always experience shear failure or flexure-shear failure under seismic loading. To predict the characteristics of these non-ductile structures, efficient numerical models are required. Therefore, the novel frame element on Winkler-Pasternak foundation with inclusion of the shear-flexure interaction effect is developed in this study. The proposed model is derived within the framework of a displacement-based formulation and fiber section model under Timoshenko beam theory. Uniaxial nonlinear material constitutive models are employed to represent the characteristics of non-ductile RC frame and the underlying foundation. The shear-flexure interaction effect is expressed within the shear constitutive model based on the UCSD shear-strength model as demonstrated in this paper. From several features of the presented model, the proposed model is simple but able to capture several salient characteristics of the non-ductile RC frame resting on foundation, such as failure behavior, soil-structure interaction, and shear-flexure interaction. This confirms through two numerical simulations.

• Computers and Concrete
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• 제33권3호
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• pp.285-299
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• 2024

Recently, many engineering computations have realized their digital transformation to Machine Learning (ML)-based systems. Predicting the behavior of a structure, which is mainly computed with structural analysis software, is an essential step before construction for efficient structural analysis. Especially in the seismic-based design procedure of the structures, predicting the lateral load capacity of reinforced concrete (RC) columns is a vital factor. In this study, a novel ML-based model is proposed to predict the maximum lateral load capacity of RC columns under varying axial loads or cyclic loadings. The proposed model is generated with a Deep Neural Network (DNN) and compared with traditional ML techniques as well as a popular commercial structural analysis software. In the design and test phases of the proposed model, 319 columns with rectangular and square cross-sections are incorporated. In this study, 33 parameters are used to predict the maximum lateral load capacity of each RC column. While some traditional ML techniques perform better prediction than the compared commercial software, the proposed DNN model provides the best prediction results within the analysis. The experimental results reveal the fact that the performance of the proposed DNN model can definitely be used for other engineering purposes as well.

• 한국전산구조공학회논문집
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• 제24권3호
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• pp.319-328
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• 2011

이 논문은 ESO 기법을 기초로 한 Strut-Tie 모델의 구성을 제안하고 있다. 평면응력 요소를 사용한 기존의 ESO방법과 달리, ESO기법에 의해 최적화된 구조가 트러스와 비슷한 형태를 가지는 사실에 기인하여, Strut-Tie 모델을 통한 전단설계에 트러스 요소를 사용한 ESO기법을 새롭게 적용하였다. 예제들을 통해 제안된 방법이 가장 좋은 Strut-Tie 모델을 찾을 수 있음을 입증하였으며, 앞서 2차원 평면응력 요소와 Strut-Tie 모델의 연관성에 대한 연구를 통해 ESO방법이 효과적으로 사용될 수 있음은 물론 경험하지 못한 특히 복잡한 철근 콘크리트 구조물의 전단설계에 효과적으로 사용이 가능한 대안이 될 수 있을 것으로 판단된다.

• Earthquakes and Structures
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• 제10권2호
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• pp.261-291
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• 2016

This paper deals with the seismic assessment of a real RC frame building located in Italy, designed according to the current Italian seismic code. The first part of the paper deals with the calibration of the structural model of the investigated building. The results of an in-situ dynamic identification test are employed in a sensitivity and parametric study in order to find the best fit model in terms of frequencies and modal shapes. In the second part, the safety of the structure is evaluated by means of nonlinear static analyses, taking into account the results of the previous dynamic study. In order to investigate the influence of the infills on the seismic response of the structure, the nonlinear static analyses are performed both neglecting and taking into account the infill panels. The infill panels differently change the behavior of the structure in terms of strength and stiffness at different seismic intensity levels. The assessment study also verifies the absence of brittle failures in structural elements, which could be caused by either the local interaction with infills or the failure of the strength hierarchy.

• 대한건축학회논문집:구조계
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• 제34권2호
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• pp.41-48
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• 2018

Carbonation is one of the major detrimental factors to the reinforced concrete structures owing to penetration of atmospheric CO2 through the micro pores, thereby it reduces the durability of the concrete. The maintenance periods and cost for concrete according to the coefficient variation of different finishing materials is documented in literature. However, it is required to carry out the systematic and well planned studies. Therefore, keeping them in mind, surface repair was carried out to the carbonated concrete and the maintenance cost was calculated to measure the durability life after repair with different variable. The deterministic and probabilistic methods were applied for durability and repair cost of the concrete. In the existing deterministic model, the cost of repair materials increases significantly when the concrete structure reaches its service life. In present study using a stochastic model, the maintenance period and cost was evaluated. According to obtained results, there was no significant difference in the number of maintenance of the coefficient variation. The initial durability has a great influence on the maintenance time and cost of the structure. Unlike the deterministic model, the probabilistic cost estimating model reduces the number of maintenance to the target service life expectancy.

• Structural Engineering and Mechanics
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• 제63권4호
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.