• 콘크리트학회논문집
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• 제25권2호
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• pp.225-232
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• 2013

최근 건설시장의 세계적인 흐름은 방법 및 수행 절차보다는 최종 성과물의 성능을 제시하는데 초점을 맞춘 성능중심 설계기준으로 변화하고 있는 실정이다. 또한, 콘크리트 재료 및 구조물의 성능 검증을 위하여 재령효과에 따른 콘크리트 강도를 검토할 경우 적절한 모델을 사용하여야 한다. 따라서 이 논문에서는 국내 재료 특성을 반영한 콘크리트 강도발현 상수를 제안하고, 그 적합성을 평가하였다.

• Steel and Composite Structures
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• 제45권2호
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• pp.281-291
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• 2022

Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2009년도 춘계 학술대회 제21권1호
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• pp.565-566
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• 2009

Supplementary cementing materials (SCM), such as silica fume, slag, and low-calcium fly ash, have been widely used as mineral admixtures in high strength and high performance concrete. Due to the chemical and physical effect of SCM on hydration, compared with Portland cement, hydration process of cement incorporating SCM is much more complex. This paper presents a numerical hydration model which is based on multi-component concept and can simulate hydration of cement incorporating SCM. The proposed model starts with mixture proportion of concrete and considers both chemical and physical effect of SCM on hydration. Using this proposed model, this paper predicts the following properties of hydrating cement-SCM blends as a function of hydration time: reaction ratio of SCM, calcium hydroxide content, heat evolution, porosity, chemically bound water and the development of the compressive strength of concrete. The prediction results agree well with experiment results.

• Steel and Composite Structures
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• 제50권4호
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• 콘크리트학회논문집
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• 제23권6호
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• pp.741-747
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• 2011

이 연구에서는 강도수준 및 강도발현 속도에 따른 콘크리트의 자기수축과 예측모델의 적용성을 비교하였고, 다양한 양생조건을 적용시켜 자기수축을 검토하였다. 연구 결과에 따르면 콘크리트가 강도가 증가할수록 자기수축이 증가하는 것으로 나타났다. 그러나 동일한 콘크리트 강도의 경우라도 강도발현 속도가 빠른 OPC의 경우 초기 자기수축은 크지만 최종 자기수축은 BFS의 경우가 더 큰 것으로 나타났다. 초기 습윤양생은 자기수축 저감에 영향을 미치며 특히 24시간 이상 습윤양생을 실시하면 최종 자기수축은 크게 감소하는 것으로 나타났다. 기존의 EC2모델은 콘크리트 특성을 적절히 반영하지 못하는 것으로 나타났으며 자기수축을 보다 효과적으로 예측할 수 있는 수정 모델식을 제안하였다.

• Steel and Composite Structures
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• 제29권1호
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• pp.125-138
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• 2018

The research on the confinement behavior of ultra high performance concrete without and with the use of steel fibers (UHPC and UHPFRC) has been extremely limited. In previous studies, authors experimentally investigated the axially compressive behavior of circular steel tube confined concrete (STCC) short and intermediate columns with the employment of UHPC and UHPFRC. Under loading on only the concrete core, the confinement effect induced by the steel tube was shown to significantly enhance the utimate stress and its corresponding strain of the concrete core. Therefore, this paper develops a simplified stress - strain model for circular STCC columns using UHPC and UHPFRC with compressive strength ranging between 150 MPa and 200 MPa. Based on the regression analysis of previous test results, formulae for predicting peak confined stress and its corresponding strain are proposed. These proposed formulae are subsequently compared against some previous empirical formulae available in the literature to assess their accuracy. Finally, the simplified stress - strain model is verified by comparison with the test results.

• 콘크리트학회논문집
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• 제24권2호
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• pp.137-145
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• 2012

이 연구는 수산화나트륨과 탄산나트륨이 혼합된 알칼리 활성화제에 의한 고로슬래그 모르타르의 강도발현 특성을 파악하기 위한 연구이다. 주요 변수는 활성화제의 첨가량, 물-바인더비(W/B) 그리고 골재-바인더비(S/A)이다. 활성화제의 첨가량에 따른 강도 특성을 수산화나트륨 3%, 4% 및 탄산나트륨 4%~8%까지 조절하여 측정하였다. 물-바인더비는 0.45~0.60까지 그리고 골재-바이더비는 2.05~2.85의 범위 내에서 변화하며 측정하였다. 원재료의 주요 성분 및 수산화나트륨, 탄산나트륨에 포함된 산화나트륨($Na_2O$) 양에 따라 조합된 알칼리 품질계수($Q_A$)를 산정하고, 이를 적용하여 알칼리 활성 모르타르의 28일 압축강도 예측식을 제안하였다. 각 변수에 따른 시험값과 제안된 예측식을 통한 결과값은 오차범위 5% 이내의 범위에서 만족하는 것으로 나타났다.

• International Journal of Concrete Structures and Materials
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• 제10권1호
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• pp.47-60
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• 2016

The objective of this study was to evaluate the capability of different strength-maturity models to account for the effect of the hydration heat on the in-place strength development of high-strength concrete specifically developed for nuclear facility structures under various ambient curing temperatures. To simulate the primary containment-vessel of a nuclear reactor, three 1200-mm-thick wall specimens were prepared and stored under isothermal conditions of approximately $5^{\circ}C$ (cold temperature), $20^{\circ}C$ (reference temperature), and $35^{\circ}C$ (hot temperature). The in situ compressive strengths of the mock-up walls were measured using cores drilled from the walls and compared with strengths estimated from various strength-maturity models considering the internal temperature rise owing to the hydration heat. The test results showed the initial apparent activation energies at the hardening phase were approximately 2 times higher than the apparent activation energies until the final setting. The differences between core strengths and field-cured cylinder strengths became more notable at early ages and with the decrease in the ambient curing temperature. The strength-maturity model proposed by Yang provides better reliability in estimating in situ strength of concrete than that of Kim et al. and Pinto and Schindler.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 추계 학술논문 발표대회
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• pp.42-43
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• 2018

The research is to suggest the compensating strength values depending on various managing periods of concrete based on the strength development model calculated with equivalent age method for 20% of blast furnace slag replaced concrete. As a result, for 28 days of managing period, 9, 6, and 3MPa of compensating strength values were suggested when the temperatures were from 4 to 6℃, from 6 to 12℃, from 12 to 17℃, respectively. Additionally, for 42 days of managing period, 6 and 3MPa of compensating strength value was suggested when the temperature was from 4 to 7℃, from 7 to 12℃, and for 56 days of managing period, 3MPa of compensating strength value was suggested when the temperature was from 4 to 9℃. Furthermore, for 28, 42, 56, and 91 days of managing periods, any compensating strength values were needed when the temperature were higher than 17, 12, 9, and 4℃, respectively.

• Computers and Concrete
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• 제27권4호
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.