• Computers and Concrete
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• 제23권6호
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• pp.399-408
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• 2019

Structural health monitoring is important for the safety of lives and asset management. In this study, numerical models were developed for the piezoresistive behavior of smart concrete based on finite element (FE) method. Finite element models were calibrated with experimental data collected from compression test. The compression test was performed on smart concrete cube specimens with 75 mm dimensions. Smart concrete was made of cement CEM II 42.5 R, silica fume, fine and coarse crushed limestone aggregates, brass fibers and plasticizer. During the compression test, electrical resistance change and compressive strain measurements were conducted simultaneously. Smart concrete had a strong linear relationship between strain and electrical resistance change due to its piezoresistive function. The piezoresistivity of the smart concrete was modeled by FE method. Twenty-noded solid brick elements were used to model the smart concrete specimens in the finite element platform of Ansys. The numerical results were determined for strain induced resistivity change. The electrical resistivity of simulated smart concrete decreased with applied strain, as found in experimental investigation. The numerical findings are in good agreement with the experimental results.

• Steel and Composite Structures
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• 제45권6호
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• pp.877-894
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• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.161-162
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• 2022

The purpose of this study is to determine the viscosity of the polymer-cement composites(PCCs) for crack repair of RC structures and to investigate its compaction. According to the study on the viscosity and compaction property of PCCs for crack repair, the viscosity of PCCs varies greatly depending on the polymer type and polymer cement ratio, and by mixing silica fume into PCCs, appropriate viscosity and excellent flow can be controlled without separation of cement and water. As a result of this study, basic data on the viscosity, fluidity, and compaction properties of PCCs for crack repair of RC structure can be obtained.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 봄 학술논문 발표대회
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• pp.179-180
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• 2020

CO2 emissions are caused by cement manufacturing process. To solve this problem construction industry are using industrial by-products to replace cement. In this study, three different industrial by products were used and mixed with hybrid fibers to enhance bond strength. As the result, Regardless of the mixing rate of silica fume, the compressive strength of the ternary non cent mortar was higher than that of OPC and binary. And mixed hybrid fibers cured by room temperature compressive strength were 23% higher than those without hybrid fibers.

• 시멘트 심포지엄
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• 통권49호
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• pp.23-24
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• 2022

Wollastonite는 대표적인 이산화탄소 반응경화 물질로서 이산화탄소와 직접적으로 반응하여 탄산칼슘과 실리카겔을 형성한다. 따라서 이는 의심의 여지 없이 이산화탄소 저장 및 활용에 사용될 수 있는 핵심적인 기술이다. 본 연구에서는 실리카 원료로서 고가의 실리카퓸을 대체하기 위해 모래와 폐유리를 사용하여 실험해 보았다.

• Computers and Concrete
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• 제32권6호
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• Computers and Concrete
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• 제33권4호
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• pp.409-423
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• 2024

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 가을학술발표대회논문집
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• pp.179-180
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• 2023

The adhesion performance of PCCs for crack repair of RC structures was greater in the case of using ultra high-early strength cement than in the case of using ordinary Portland cement, and the effect of mixing silica fume was higher in the case of ordinary Portland cement than that of ultra high-early strength cement. On the other hand, 130% of W/C was more fluid than 80% of W/C in the same P/C 80%, which increased the fillability and improved the strength, but the strength improvement effect was the greatest in adhesion in flexure. Through this study, the basic characteristics of the adhesion performance of PCCs were identified, and based on this, it is necessary to induce an optimal mixing design that can increase adhesion performance through various mixing designs.

• 콘크리트학회논문집
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• 제17권1호
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• pp.129-137
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• 2005

최근 양질의 하천골재가 고갈상태에 직면함에 따라 쇄석 골재의 사용이 보편화되고 있지만 쇄석 골재 사용으로 야기될 수 있는 알칼리-실리카 반응에 대한 문제를 검토하지 않은 채 콘크리트 재료로 사용하고 있는 실정이다. 알칼리-실리카 반응은 콘크리트에 유해한 팽창을 일으키는 작용으로서, 반응결과 알칼리-실리카 겔이 형성되고 이러한 겔이 수분을 계속 흡수함으로써 체적 팽창을 일으켜 콘크리트에 균열이 발생된다. 골재의 알칼리-실리카 반응성을 판정하는 방법은 암석학적 판정법, 화학법 및 모르타르 바 법이 일반적으로 사용되지만, 이 중에서 모르타르 바 법이 비교적 신뢰성이 높다. 본 연구에서는 모르타르바 시험방법 중 ASIM C 227과 ASIM C 1260을 선택하여 암석 유형별로 수집한 12종의 골재들을 대상으로 쇄석 골재의 반응성을 비교, 분석하였다. 또한 본 연구에서는 반응성 골재의 입자크기 및 입도가 모르타르 바의 알칼리-실리카 반응 팽창에 미치는 영향에 관하여 검토하였다 혼화재의 용도는 상당히 많지만 본 연구에서는 쇄석 골재 사용으로 문제되고 있는 알칼리-실리카 반응에 있어서 플라이애쉬, 고로슬래그미분말, 실리카퓸 및 메타카올린을 혼화재료로 사용할 경우 알칼리-실리카 반응에 미치는 영향을 알아보고자 시멘트에 대한 혼화재의 치환율을 달리하여 ASTM C 1260 시험법으로 알칼리-실리카 반응에 대한 팽창 저감효과를 평가하여 보았다. 본 연구에서는 혼화재의 치환율을 0, 5, 10, 15, 25 및 $35\%$로 하였으며, 모르타르 유동성 시험을 병행하여 어느 정도의 유동성을 갖는 혼화재는 45, $55\%$까지 치환율을 증가하여 길이변화 시험을 수행하였다. 시멘트 중량에 대한 혼화재 치환율이 플라이애쉬는 $25\%$, 실리카퓸은 $10\%$, 메타카올린은 $25\%$, 고로슬래그미분말은 $35\%$일 경우 알칼리-실리카 반응에 의한 팽창을 가장 효과적으로 방지할 수 있는 것으로 판단된다.

• 한국산업보건학회지
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• 제9권1호
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• pp.56-72
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• 1999

Airborne concentrations of welding fumes in which 13 different metals such as Al, Cd, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Si, Sn, Ti, and Zn were analyzed were measured at 18 factories including automobile assembly and manufactures, steel heavy industries and shipyards. Air samples were collected by personal sampler at each worker's worksite(n=339). Blood levels of Cd, Cu, Fe, Mn, Pb and Zn were also measured from samples taken from 447 welders by atomic absorption spectrometry and compared with control values obtained from 127 non-exposed workers. The results were as follows ; 1. Among various welding types, $CO_2$ welding 70.2 % were widely used, shielded metal arc welding(SMAW) 22.1 % came next, and rest of them were metal inert gas(MIG) welding, submerged arc welding(SAW), spot welding(SPOT) and tungsten inert gas(TIG) welding. 2. Welding fume concentration was $0.92mg/m^3$($0.02{\sim}15.33mg/m^3$) at automobile assembly and manufactures, $4.10mg/m^3$($0.02{\sim}70.75mg/m^3$) at steel heavy industries and $5.59mg/m^3$($0.30{\sim}91.16mg/m^3$) at shipyards, respectively, showing significant difference among industry types. Workers exposed to high concentration of welding fumes above Korean Permissible Exposure Limit(KPEL) amounted to 7.9 % and 12.5 %, in $CO_2$ welding and in SMAW at automobile assembly and manufactures and 62.7 % in $CO_2$ welding, and 12.5 % in SMAW at shipyards, and 66.2 % in $CO_2$ welding and 70.6 % in SMAW at steel heavy industries. 3. Geometric mean of airborne concentration of each metal released from welding fumes was below one 10th of KPEL in all welding types. Percentage of workers, however, exposed to airborne concentration of metals above KPEL amounted to 16.8 % in Mn and 7.6 % in Fe in $CO_2$ welding; 37.5 % in Cu in SAW, 30 % in Cu in TIG; and 25 % in Pb in SPOT welding. As a whole, 76 Workers(22.4%) were exposed to high concentration of any of the metals above KPEL. 4. There were differences in airborne concentration of metals such as Al, Cd, Cr, Cu. Fe. Mn, Mo, Ni, Pb, Si, Sn, Ti and Zn by industry types. These concentrations were higher in shipyards and steel heavy industries than in automobile assembly and manufactures. Workers exposed to higher concentration of Pb above KPEI amounted to 7.4 % of workers(7/94) in automobile assembly and manufactures. In shipyards, 19.2 % of workers(19/99) were over-exposed to Mn and 7.1 % (7/99) to Fe above KPEL. In steel heavy industries, 14.4 %(21/146), 7.5 %(11/146) and 13 %(19/146) were over-exposed to Mn, Fe and Cu, respectively. As a whole, 76 out of 339 workers(22.4%) were exposed to any of the metals above KPEL. 5. Blood levels of Cd, Cu, Fe, Mn, Pb, and Zn in welders were $0.11{\mu}g/100m{\ell}$, $0.84{\mu}g/m{\ell}$, $424.4{\mu}g/m{\ell}$, $1.26{\mu}g/100m{\ell}$, $5.01{\mu}g/100m{\ell}$ and $5.68{\mu}g/m{\ell}$, respectively, in contrast to $0.09{\mu}g/100m{\ell}$, $0.70{\mu}g/m{\ell}$, $477.2{\mu}g/m{\ell}$, $0.73{\mu}g/100m{\ell}$, $3.14{\mu}g/100m{\ell}$ and $6.15{\mu}g/m{\ell}$ in non-exposed control groups, showing significantly higher values in welders but Fe and Zn.