Correlation Between V-funnel and Mini-slump Test Results with Viscosity

T. Bouziani,A. Benmounah 대한토목학회 2013 KSCE JOURNAL OF CIVIL ENGINEERING Vol.17 No.1

Self-Compacting Mortars (SCM) can be regarded as high flowing mortars, which must show both a good fluidity (to fill complex formwork shapes) and sufficient viscosity (to avoid segregation). The characterization and control of fresh properties are proving to be critical for the success of SCM design. Usually, this task is performed through technological tests such as v-funnel and minislump. However, the use of viscometers can successfully perform better access of fresh properties. The objective of the present work is to correlate experimental results of v-funnel and mini-slump tests with viscosity of SCM, measured at different rotational speeds,and with constants a and b calculated from the power-law viscosity model. Linear relationships between both v-funnel and minislump tests and viscosity were demonstrated. Statistical models are also established to highlight the influence of constants a and b on the v-funnel and mini-slump variations. Results indicate the usefulness of established models to better understand the trade-off between constants a and b on fresh properties measured by v-funnel and mini-slump tests.

Modelling fresh properties of self-compacting concrete using neural network technique

Mohammed Sonebi,Steffen Grunewald,Abdulkadir Cevik,Joost Walraven 사단법인 한국계산역학회 2016 Computers and Concrete, An International Journal Vol.18 No.4

The purpose of this paper is to investigate the feasibility of using artificial neural network programming for the prediction of the fresh properties of self-compacting concrete. The input parameters of the neural network were the mix composition influencing the fresh properties of self-compacting concrete namely, the cement content, the dosages of limestone powder and water, fine aggregate, coarse aggregate, and superplasticizer, and other parameter of time of testing (5, 30 and 60 minutes after addition of water). The model is based on a multilayer feed forward neural network. The details of the proposed ANN with its architecture, training and validation are presented in this paper. Six outputs of the ANN models related to the fresh properties were the slump flow, T50, T60, V-funnel flow time, Orimet flow time, and blocking ratio (L-box). The effectiveness of the trained ANN is evaluated by comparing its responses with the experimental data that were used in the training process. The dosage of water was varied from 188 to 208 L/m3, the dosage of SP from 3.8 to 5.8 kg/m3, and the volume of coarse aggregates from 220 to 360 L/m3 (587 to 961 kg/m3). In total twenty mixes were used to measure the fresh properties with different mix compositions. ANN performed well and provided very good correlation coefficients (R2) above 0.957 for slump flow, T50, V-funnel flow time, Orimet flow time, and L-box blocking ratio. The predicting results for T60 was slightly lower (R2=0.823). With the calculated models these properties of new mixes within the practical range of the input variables used in the training can be predicted with an absolute error for slump flow, T50, T60, V-funnel flow time, Orimet flow time, and L-box ratio of 3.3%, 13%, 16%, 14%, 15%, and 22%, respectively. The results show that the ANN model can predict accurately the fresh properties of SCC.

지열발전을 위한 지열정 시멘트용 G-class시멘트와 일반 포틀랜드시멘트와의 유동성 비교실험

전종욱(Jongug Jeon),원종묵(Jongmuk Won),최항석(Hangseok Choi) 한국지열·수열에너지학회 2012 한국지열에너지학회논문집 Vol.8 No.2

The G-class cement is usually used for geothermal well grouting to protect a steel casing which is equipped in a geothermal well to transfer geothermal water from deep subsurface to ground surface. In geothermal grouting process, obtaining appropriate fluidity is extremely important in order to fill cement grout flawlessly. In this paper, a series of the V-funnel and Slump Flow test was performed on both of the Portland cement and the G-class cement in order to compare fluidity and filling ability of those kind of cements. In the result of V-funnel test, the fluidity of G-class cement was evaluated much better than the Portland cement at the water/cement ratio of 0.8. In the case of Slump Flow test, the fluidity of G- class cement was estimated slightly better than the Portland cement at both the water/cement ratio of 0.55 and 0.8. Even though the initial fluidity and filling ability of G-class cement were relatively higher than the Portland cement, the results could be considerably changed with time. The results show that the fluidity and filling ability for geothermal well cementation can be properly controlled with water content and additives for adverse geothermal well environment.

부순 모래를 포함한 고유동 모르타르의 유동특성

이근수(Lee Keun-Su),최열(Choi Yeol),이재익(Lee Jae-Ik),정웅(Jung  Woong) 대한건축학회 2008 대한건축학회 학술발표대회 논문집 - 계획계/구조계 Vol.28 No.1(구조계)

The self-compacting concrete(SCC) has been developed for the last two decades. And also the crushed sands as a fine aggregate have been increasingly used for the concrete industries due to the shortage of the natural sands. Therefore, the study of self-compacting concrete(SCC) containing crushed sands is needed and the Rheological properties of SCC is deeply related to the Rheological properties of SCM(self compacting morter). In other words, In order to find out the Rheological properties of SCC, the study about SCM should be needed. The total 20 Mortar mixes were made to compare the effects depending on the various changes in the ratio of viscocity enhancing admixture (VEA) and superplasticizer(SP). The tests for rheological properties of SCM were conducted through mini V-funnel test and mini slump flow test. The data(slump flow, flow time) from the tests is replaced to Relative flow area(Pm) and Relative V-funnel flow time(Ф). The optimum mortar mix condition is approximately satisfied when containing 0.2%(of cement weight) YEA and between 1.0% and 1.2%(of cement weigh) SP in case of w/c 0.45.

Method for estimating workability of self-compacting concrete using mixing process images

Xuehui An,Shuyang Li 사단법인 한국계산역학회 2014 Computers and Concrete, An International Journal Vol.13 No.6

Estimating the workability of self-compacting concrete (SCC) is very important both in laboratories and on construction site. A method using visual information during the mixing process was proposed in this paper to estimate the workability of SCC. First, fourteen specimens of concrete were produced by a single-shaft mixer. A digital camera was used to record all the mixing processes. Second, employing the digital image processing, the visual information from mixing process images was extracted. The concrete pushed by the rotating blades forms two boundaries in the images. The shape of the upper boundary and the vertical distance between the upper and lower boundaries were used as two visual features. Thirdly, slump flow test and V-funnel test were carried out to estimate the workability of each SCC. Finally, the vertical distance between the upper and lower boundaries andthe shape of the upper boundary were used as indicators to estimate the workability of SCC. The vertical distance between the upper and lower boundaries was related to the slump flow, the shape of the upper boundary was related to the V-funnel flow time. Based on these relationships, the workability of SCC could be estimated using the mixing process images. This estimating method was verified by three more experiments. The experimental results indicate that the proposed method could be used to automatically estimate SCC workability.

동제련 슬래그를 혼입한 고강도 콘크리트의 유동특성에 관한 연구

이동운(Dong-Un Lee),윤종진(Jong-Jin Yoon),김대영(Dae-Young Kim) 한국산학기술학회 2016 한국산학기술학회논문지 Vol.17 No.10

본 연구의 목적은 고강도 콘크리트에 광물성 혼화재로서 동제련 슬래그를 혼입하였을 때 유동특성을 파악하기 위한 것이다. 이를 위해 동제련 슬래그를 고강도 콘크리트의 결합재로 사용하여 10 %, 20 %, 30 %, 40 %, 50 %를 시멘트로 대체하여 사용하였다. 그리고 굳지않은 콘크리트에서 슬럼프플로, 500mm도달시간, V-Funnel과 U-Boxt시험을 실시하여 유동성 및 충전성을 알아보았고, 굳은 콘크리트에서는 재령 3, 7, 14, 28일의 콘크리트 압축강도를 측정하였다. 상기의 실험결과, 동제련 슬래그를 광물설 혼화재료로 혼입한 콘크리트의 굳지 않은 콘크리트 특성을 살펴보면 동제련 슬래그의 치환율이 30%까지는 유동성이 증가하였으며, 충전성이 우수한 것을 알 수 있었다. 그리고 굳은 콘크리트의 특성을 살펴보면 동제련 슬래그의 치환율이 30 %일때까지 모든 재령에서 압축강도가 증가하는 것을 확인할 수 있었다. 그러나 동제련 슬래그의 치환율이 30%를 초과하였을 때는 유동성, 충전성 및 압축강도가 감소하는 것을 확인할 수 있어 동제련 슬래그의 최적 치환율은 30 %이내가 적정할 것으로 판단된다. This study examines the properties of high-fluidity concrete after adding copper slag as a mineral admixture. For this purpose, the replacement ratio of cement to copper slag was varied to 0, 10, 20, 30, 40, and 50%. A slump flow test, reach time slump flow of 500 mm, and a U-Box and O-lot test were conducted on the fresh concrete. The compressive strength of the hardened concrete was determined at 3, 7, 14 and 28 days. According to the test results, the workability, compaction, and compressive strength of the high-fluidity concrete increased when replacing 30% of the cement with copper slag. These parameters decreased for all material ages with more than 30% copper slag, which was the optimal mixture ratio.

Estimating the workability of self-compacting concrete in different mixing conditions based on deep learning

Liu Yang,Xuehui An 사단법인 한국계산역학회 2020 Computers and Concrete, An International Journal Vol.25 No.5

A method is proposed in this paper to estimate the workability of self-compacting concrete (SCC) in different mixing conditions with different mixers and mixing volumes by recording the mixing process based on deep learning (DL). The SCC mixing videos were transformed into a series of image sequences to fit the DL model to predict the SF and VF values of SCC, with four groups in total and approximately thirty thousand image sequence samples. The workability of three groups SCC whose mixing conditions were learned by the DL model, was estimated. One additionally collected group of the SCC whose mixing condition was not learned, was also predicted. The results indicate that whether the SCC mixing condition is included in the training set and learned by the model, the trained model can estimate SCC with different workability effectively at the same time. Our goal to estimate SCC workability in different mixing conditions is achieved.